What is AI Demand Forecasting?

AI demand forecasting leverages predictive artificial intelligence to estimate future demand for services or products based on historical data, external market factors, consumer buying patterns, and more.

How Does AI Demand Forecasting Work?

Demand forecasting can be done manually by analyzing various internal and external data sets to make both short- and long-term predictions, but AI models can greatly improve the process. Humans can only take in and make sense of so much data, whereas AI can sift through more information faster and use machine learning (ML) algorithms to more closely examine trends and patterns that may otherwise go ignored. AI then actively learns from historical data, adapts to changing trends, and improves accuracy over time through iterative training processes.

AI can take a few different approaches to demanding forecasting, such as:

• Time Series Analysis: A statistical technique that uses past sales data to uncover patterns, trends, and seasonality to help forecast demand.
• Neural Networks: Analyzes sales data, along with other factors, to predict demand and can utilize deep learning to capture and uncover complex patterns within the data.
• Ensemble Methods: This approach combines multiple models to lessen the weaknesses of individual models and improves overall prediction accuracy.

6 Categories of AI Used in Demand Forecasting

Depending on the type of planning being done, one or more of the six types of AI may be used in demand forecasting.

1. Short-Term Demand Forecasting: Inventory management and production planning can benefit from relatively short-term horizon, which can predict what demand may look like typically within a window of about 12 months or less.
2. Long-Term Demand Forecasting: For longer time frames, long-term AI forecasting can be used, which can generally predict up to around 4 years. This is helpful for strategic planning, significant investments, and supply chain management. 
3. Active AI Forecasting: AI techniques used for predicting the outcome or performance of specific campaigns, growth plans, or business initiatives in real-time. This is particularly useful for startups and businesses seeking funding, because it provides insights into potential growth trajectories.
4. Passive Forecasting: By looking into historical data through AI to forecast future operations, financials, or demand trends without real-time intervention. This can help businesses plan their strategies and resource allocation effectively.
5. Internal Data Utilization: Understanding starts at a business’s front door, gathering organizational data such as sales records, operational metrics, and customer data, and using AI techniques to generate insights about services, products, channels, and locations within the business. 
6. External Data Utilization: If organizations don’t have internal data, or they’re looking for another perspective on a situation, they can use external information from competitors and overall trends in the market and apply AI techniques to gain additional perspectives on market dynamics to make informed decisions about future directions.

Benefits of AI Demand Forecasting

AI demand forecasting can help your business automate workflows, achieve better accuracy, limit your risk, and improve customer satisfaction.

Workflow Automation

If you’re analyzing demand manually, you might not see patterns that allow you to respond in a timely manner. Automating data collection and analysis can improve the forecasting process. You may even be able to form real-time insights that lead to faster and more authoritative decision-making. You’ll also leave less up to chance and reduce errors in data analysis compared to manual calculation.

Keep in mind that automating workflows not only expedites the decision-making process, but also fosters scalability and resource optimization within an organization. By eliminating manual data entry and analysis tasks, AI streamlines workflows and allows teams to focus on more strategic projects vs mundane operational activities. The added scalability also enhances operational efficiency and can decrease overhead costs associated with manual labor.

Plus, as a business grows, AI-driven automation adapts seamlessly and continues to ensure consistent performance and accurate insights.

Better Accuracy and Efficiency

Accuracy isn’t just improved by automating workflows. Artificial intelligence models can take massive amounts of data and surface trends humans might miss, boosting the accuracy of forecasts. With greater accuracy, resources can be placed more effectively based on demand, and businesses can achieve a balance between overstocking and selling out. Additionally, improved accuracy leads to better resource allocation and inventory management, ultimately driving efficiency and cost savings.

Risk Mitigation Through Planning

When you’re able to see changes in demand, you can also foresee potential risks, including supply chain disruptions or emerging market shifts that may impact your business that either drives or runs concurrently with demand. Better business planning can also maximize business continuity.

Enhanced Customer Satisfaction

When customers can get what they need, when they need it, through demand forecasting and business continuity measures, they will experience fewer disappointments due to out-of-stock items. You can meet and even exceed customer demand expectations by suggesting products and promotions based on demand patterns of certain customer profiles. These personalized recommendations and timely inventory management also contribute to a positive customer experience, fostering loyalty and retention.

Companies can gain a competitive edge over other businesses in their industry when AI demand forecasting is used to its fullest potential, particularly with edge computing, which can bring computing power closer to the end user and maximize performance.

Challenges of AI Demand Forecasting

While AI can offer unique benefits to businesses looking to meet demand and surpass customer expectations, implementing new technology also comes with challenges.

Data Dependency and Quality

Your forecasts are only as reliable as the data at your disposal. If the data you have isn’t robust, or is low-quality in another way, your forecasts can also suffer. Sometimes, data may require cleaning and preparation before it’s ready for in-depth insights, or the set you have may be biased or skewed in a way that’s not immediately apparent.

Unforeseen Patterns

While AI models can do a great job of identifying patterns from historical data, they may have a harder time identifying new shifts that are indicative of emerging trends. It’s important not to rely solely on the human eye or an AI algorithm to make decisions about demand, but instead, rely on a combination of the two. It can also be difficult to determine how AI made a particular decision about demand due to the black box nature of some AI models.

Continuous Maintenance

Even though AI can cut down on human intervention and save time long-term, when new AI models are being used, they will require a lot of maintenance and attention to fine-tune and become useful. Your team needs to choose the right algorithm, make updates to the model, and monitor performance to solve any issues that arise and course-correct as needed.

Integration

Artificial intelligence models generally exhibit better performance when operating within cloud computing environments, but integration can still be a concern for businesses that have come far in their digital transformation journeys. It’s even harder to integrate with legacy frameworks. Some AI tools and platforms may not even be compatible with each other. Understanding the capabilities of demand forecasting tools and their ability to integrate with other tools already in your organization’s tech stack will make for a smoother implementation.

Lack of Customization

Unless you have someone on your team who can customize an AI model, you may be left to use off-the-shelf tools that don’t have all the ideal features you’d like to see. Even if you have the capabilities to build from scratch, the process can be time-consuming and expensive. Your business will eventually have to decide between having the flexibility to customize the tool to fit your needs and compromising in the name of time and budget.

Use Cases for AI Demand Forecasting

While AI demand forecasting is primarily used for optimizing supply chain management and assisting with inventory planning, it can also be used in many other contexts for businesses looking to understand the (sometimes) hidden patterns governing their business cycles.

Developing a Product-Mix Plan

Businesses can use AI to analyze market gaps, peak purchase times, and product visibility. These data points can aid in deciding when to roll out new products, what needs to be retired, and what quantities to order without over- or under-estimating supply.

Forecasting Fluctuating Purchase Patterns

Buying patterns are more likely to be cyclical than linear. AI can help plan for seasonal product demand and predict supply chain costs by identifying fluctuating patterns and optimizing sales and operations planning accordingly.

Minimizing Overstock and Stockouts

When you can automatically analyze all available data and constraints through AI solutions, you can also constantly rebalance supply and demand and ensure the right amount of product is ordered at the right times. You can get much closer to “just perfect” inventory levels, while avoiding overstock and stockout situations.

Predicting Website Traffic

Having the right inventory for demand surges doesn’t mean anything if your website can’t handle the influx of traffic. AI can also be used to predict these traffic increases and allocate resources to accommodate bursts in demand from surprise sales or seasonal events.

Workflow Automation and Data Consolidation

When configured properly, AI solutions can integrate with all data sources. This enables the data in an ERP, for example, to be updated automatically through the submission of daily suggested transfer and manufacturing orders to the system. Workflow automation measures can streamline demand planning and eliminate manual processes from routine activities.

Best Practices for Implementing AI Demand Forecasting

Demand forecasting is meant to solve a problem, but first, you need to be clear on your questions and objectives. Here are some best practices for implementing AI demand forecasting at your organization.

1. Defining the Forecasting Problem and Objectives

What are you trying to achieve? Do you want to optimize your inventory levels, refine your supply chain, or personalize marketing campaigns for certain customer profiles? Demand forecasting can help you accomplish these goals, but you must define what you’re looking for first.

2. Use an AI Adoption Framework

When embracing AI, starting from square one isn’t always necessary. Utilizing various Ai adoption frameworks like the Google Cloud AI Adoption Framework, Gartner Hype Cycle, or Microsoft’s AI Adoption Framework can guide you through the process, ensuring the integration and transition to AI technologies is easy and successful.

3. Data Collection and Preparation

Be sure that the data you are collecting is relevant, high-quality, and prepared properly. The better you are sourcing your data from internal and external sources, the easier it will be to make well-informed insights.

4. Selecting the Right AI Tools and Platforms

As we’ve mentioned, selecting the right AI tools and platforms has to do with the functionality you’re looking for, the services you need, and the integrations necessary to get everything working together. It’s important to work with specialized consultants who can ensure the right tools and platforms are selected based on your goals, such as scalability, security, and vendor support.

5. Incorporating Machine Learning

ML algorithms may be part of the AI tools you’ve chosen or may be used in conjunction with data you’re gathering, to help with the demand forecasting process. Linear regression can uncover a linear relationship between two sets of data. Support vector machines can divide data points based on high and low demand. Neural networks can form connections much like the human brain between points that are non-linear and may not seem related at first.

6. Training Users and Managing Data Quality

AI models can get you far, but you need to have human capabilities to take generate action based on your insights. Train users on how these new demand forecasting tools work, and how to refine the models for greater accuracy and quality as time goes on.

Empowering Your Business with AI Demand Forecasting

Leave guesswork in the past. AI demand forecasting can help businesses become more proactive and data-driven in a way that was not previously possible. If you’re interested in learning how AI can transform your business, download the Business Applications of AI/ML whitepaper and contact us to discover how TierPoint’s AI consulting services can help you unlock these capabilities.

FAQs

Businesses looking to speed up their time to market, innovate more effectively, and improve application quality can do so by adopting a DevOps model. This model brings development and operations teams together to achieve a common goal: Efficient collaboration throughout the development and testing of applications.

Here are the 11 best practices organizations should keep in mind when implementing a DevOps transformation.

What is DevOps?

DevOps is a process approach designed to speed up application changes and reduce errors by combining software development processes (Dev) and software operations (Ops). Efficiencies come from automation and monitoring being brought together throughout the application development process. The best DevOps team can deploy faster and instill more faith in end users. Plus, with the operations team involved, end business objectives are considered along with testing.

Some businesses may choose to have a DevOps team and a site reliability engineer (SRE) team. An SRE team is responsible for operational functions – monitoring systems, implementing the CI/CD pipeline, and designing systems for the DevOps team to use. In contrast, DevOps teams are focused on removing barriers between the development and operations teams with the end goal of creating solutions that achieve organizational goals.

Why is DevOps Important?

DevOps plays an important role in fostering collaboration by breaking down barriers between the development and operations teams. When set up properly, a DevOps team can accomplish more in a shorter time frame than two distinct teams implementing a more divided chain of command. By engaging in this cultural shift, businesses can see improvements in agility, teamwork, data quality, and business value.

What Are the Three P’s of DevOps?

You may hear people talk about the three P’s when they refer to DevOps, which are the people, processes, and tools that enable efficiency in the pipeline. When you get your people, processes, and pipeline aligned, the development and improvement process can run like a well-oiled machine.

Our Top 11 DevOps Best Practices for Implementation

Building a strong DevOps practice at your business requires culture change, automation, and a significant investment in technology. Implementing these 11 best practices can move your teams from siloed to in sync.

1. Shift to a Collaboration and Communication-Driven Culture

A DevOps migration strategy is just as much about setting new cultural team expectations as it is about making sure the technology and tools are right for the job. Encouraging regular meetings, setting up knowledge-sharing sessions, and creating shared goals can help shift a separated environment to one that thrives on collaboration and communication. Create shared communication channels, pair members of each team together to learn from one another, and have regular stand-up meetings to start making connections and moving toward work as a unit.

2. Implement a Continuous Integration/Continuous Deployment (CI/CD) Pipeline

A continuous integration/continuous deployment (CI/CD) pipeline involves regularly making updates to a software application with the goal of iterative improvement.

By adopting a CI/CD approach, teams can reduce the risk of introducing bugs or errors into the codebase, as small changes are tested and deployed incrementally. This iterative approach to development promotes faster feedback loops, enabling teams to respond promptly to customer feedback and market demands.

Moreover, CI/CD pipelines enhance collaboration and transparency within development teams, as everyone has visibility into the status of code changes and deployments. This fosters a culture of continuous improvement and innovation, driving the delivery of high-quality software that meets the evolving needs of customers.

3. Conduct Agile Project Management

Up the productivity of your DevOps team by implementing agile project management frameworks or technologies, such as Scrum or Kanban. Use boards to visualize what needs to be done, what’s currently in progress, and what’s getting in the way of finishing parts of projects.

4. Build with the Right Tools

The more you can use tools, like artifical intelligence or machine learning, that integrate easily with your existing systems, the more likely it is that the DevOps team will embrace them. Not only can these tools improve the project management process, but they can also package applications, automate regular tasks, check for errors, and streamline workflows.

5. Set Up Automation Testing

In today’s competitive landscape, companies recognize the need for swift and timely product improvements to maintain customer satisfaction and loyalty. Automation testing plays a crucial role in this process by allowing organizations to identify and fix bugs quickly, ensuring that new applications are developed efficiently and delivered to market faster. By automating repetitive testing tasks, companies can increase testing coverage, improve accuracy, and accelerate the release cycle, ultimately enhancing the overall quality of their products and services.

6. Focus on Observability

Especially in the early days of a new DevOps team, visibility is key. By visualizing system metrics through effective monitoring, logging, and tracing practices, the team gains invaluable insights into the performance and health of their applications and infrastructure. This visibility not only enables swift detection and resolution of issues but also fosters a culture of continuous improvement, as the team collaboratively works towards shared goals, ensuring optimal performance and reliability of their system.

7. Practice Continuous Monitoring

Observability and monitoring work well together. Observability can help illuminate the relationship between data points, while monitoring can alert teams to key data points that may require additional action. Use tools to set up alerts for critical events, and constantly monitor your environment for potential performance and security issues. Through continuous monitoring, DevOps teams can proactively address problems before they become an impediment for users.

8. Use Automation 

Infrastructure provisioning, deployments, and configuration management can all be automated to reduce friction in your team’s processes.

By automating the provisioning, configuration, and management of infrastructure resources, teams can achieve greater efficiency, consistency, and scalability in their operations. Infrastructure as Code (IaC) tools like Terraform, enable teams to define infrastructure components programmatically, treating infrastructure configurations as code that can be version-controlled, tested, and deployed alongside application code.

Automating infrastructure empowers teams to rapidly adapt to changing requirements, provision resources on-demand, and maintain infrastructure consistency across development, testing, and production environments, ultimately accelerating the delivery of reliable and resilient software solutions.

9. Integrate Continuous Security Early

The earlier security vulnerabilities are caught, the less likely they are to impact your end users. By implementing continuous security scanning and vulnerability assessments from the outset of development, teams can proactively identify and address security vulnerabilities before they pose a risk to end users. Waiting until after deployment to address security concerns can lead to costly and potentially damaging security breaches. By incorporating security checks into the CI/CD pipeline, teams can automate security testing and ensure that security measures are applied consistently throughout the development lifecycle. This not only enhances the overall security posture of the application but also fosters a culture of security awareness and responsibility among team members.

10. Adopt Microservices Architecture

Breaking down applications into smaller pieces can make them easier to manage from a development and deployment perspective. A microservices architecture can also scale more efficiently and boost your agility. Containers and API gateways can be used between microservices for communication and management.

11. Gather Ongoing Feedback

Cultural shifts are never going to be seamless, but taking feedback along the way and adjusting when necessary can make DevOps team members feel heard and allow you to handle emerging issues quickly. Use feedback to improve your tools, applications, and processes, and take input from stakeholders at all levels and steps in the development cycle.

Enhance DevOps Best Practices With TierPoint

If you’re looking to innovate using your existing Microsoft Azure or AWS cloud infrastructure and optimize IT, a DevOps approach can help you:

• Speed up time to market
• Reduce risk
• Improve collaboration
• Experience better consistency

Ready to learn more about how this approach can benefit your business? At TierPoint, we’re happy to help. We offer DevOps consulting services that can help you build, plan, implement, and optimize your DevOps transformation. Contact us today to schedule a call with one of our DevOps specialists.

Did you know that artificial intelligence can be incorporated into DevOps processes to improve business initiatives? Download the eBook to discover how to leverage AI.

FAQs

What is Network Modernization?

Network modernization involves updating the current network infrastructure, generally as part of a larger IT modernization strategy. Businesses may choose to add new technologies, upgrade current ones, or perform other optimizations to usher their network infrastructure into a more innovative and interconnected realm.

Types of Popular Modern Network Technologies

Some of the most popular modern network technologies include virtualization, software-defined networking (SDN), network function virtualization (NFV), secure access service edge (SASE), 5G technology, and multicloud networking (MCN) – we’ll go into a little more detail about these later.

Why is Network Modernization Important?

In an evolving technological landscape, network infrastructure can be the key to modernizing your business or feeling trapped by your systems. Network modernization can allow you to meet your business objectives, keep up with the current state of technology, and be prepared to meet future challenges.

Key Benefits of Network Modernization

Modernizing your network comes with several benefits, including adapting your legacy infrastructure, improving your scalability, making your system more secure and reliable, boosting your efficiency, future-proofing your infrastructure, and saving you money.

1. Addresses Legacy Infrastructure Challenges

It can feel easier to maintain legacy infrastructure with little fixes here and there, but eventually, the burden of maintaining infrastructure that isn’t modernized will be something that holds your business back. By focusing on network modernization, you can identify and remedy challenges with your current legacy framework and build better paths.

2. Improves Scalability for Business Growth

James Clear, author of Atomic Habits, said: “You do not rise to the level of your goals. You fall to the level of your systems.” You can easily get penned in when your systems prevent business growth and scalability. By modernizing your network infrastructure, you improve your business’s ability to scale and grow.

3. Strengthens Security and Reliability

Older networks can also be prone to increased security and compliance issues. Network modernization can help your business resolve these issues, mitigate risks by bringing in enhanced security measures, and strengthen your infrastructure using the features inherent in modern networks.

4. Heightens Operational Efficiency

Modern network management tools offer central control, automation capabilities, and real-time insights. These features can significantly reduce the amount of time spent on administrative tasks, making your organization more operationally efficient. Monitoring and automation tools can also streamline your workflows and free up your IT resources for more pressing projects.

5. Promotes Long-Term Cost Savings

While network modernization can be a significant upfront investment, businesses can enjoy substantial long-term cost savings and see a strong return on investment. A study by NTT found that 70% of CEOs believe that their network is impeding their business growth. Improving business delivery by upgrading network infrastructure can lead to business growth, improve efficiencies, and save money over time.

6. Helps Future-Proof Infrastructure and Sustainability

Future-proofing your business stands in opposition to maintaining legacy systems. Modern network technologies, including MCN, NFV, and synchronous digital hierarchy (SDH), can allow businesses to adapt to future growth and evolving expectations at a faster rate. Many new network solutions are also more sustainable, placing priority on energy-efficient protocols and components, which is another way modernized infrastructure can result in cost savings.

Challenges of Network Modernization

New projects always bring new challenges, and launching a network modernization strategy is no different. Modernization initiatives can be complex, costly, and difficult to pull off based on the level of changes proposed and the in-house skills needed to do the job right.

Tool Integration and Compatibility Issues

While modernizing your network can boost the opportunities for integration and compatibility, businesses can also experience compatibility issues based on their current infrastructure and the tools they are using. Bringing in modern infrastructure can mean more complex workarounds are needed, or even complete upgrades of the legacy frameworks. Integrating tools can also mean increasing access points on the network, adding more vulnerabilities and security concerns. By taking a phased approach, choosing tools with open standards and APIs, and having the right team on board, businesses can reduce the risks associated with integration and compatibility issues.

Complexity and Initial Costs

Modernizing a network can be a complex project from start to finish. The process involves diverse technologies, integration tasks, and potential compatibility issues at every turn. This complexity can lead to large initial costs, but they can be worth it for the business growth and ROI that modernization allows on the other side.

In-House Skills

If your team has never taken on a modernization project, there is a good chance you won’t have the skills you need to properly plan and execute a network modernization plan. Suitable workarounds could include upskilling your current staff or leaning on outsourced talent, something that 59% of businesses are already using to fill their skill gaps.

Change Management and Disruption

Implementing a new network requires careful planning and change management to minimize disruption to ongoing operations. You’ll need to consider the potential downtime that comes with a modernization project. Businesses should also prepare to train employees to meet any changes that come with new network infrastructure.

6 Network Modernization Strategies

New and innovative strategies are available to businesses that decide to modernize their networks. They can harness cloud connectivity, software-defined networking, 5G connectivity, and more to promote scalability, flexibility, and iterative growth.

Cloud-Based Networking

Cloud modernization through cloud-based networking often entails migrating network functions and management tools to the cloud for increased flexibility, scalability, and disaster recovery abilities. Cloud connectivity decreases the need for dedicated hardware, saving costs on on-premises management. Plus, single or multicloud networking can provide centralized management and access from anywhere.

Software-Defined Networking

Software-defined networking can bring about more agility to networks by separating the control plane from the data plane. The control plane is responsible for the “how” behind data forwarding, and the data plane executes the forwarding. SDN can allow the network administrators to program the network and provision new resources quickly. New configurations and responses to traffic demands can be made in a snap. This sets SDN starkly apart from traditional networks, which need to be manually configured on each device.

Network Function Virtualization

Network function virtualization (NFV) virtualizes functions that belong to the network, including firewalls and routers. By virtualizing these functions, much like SDN, networks can have more flexibility and scalability compared to physical components.

SD-WAN

Software-defined wide area network (SD-WAN) serves as a complement to SDN and NVF by managing the data plane across branches and working with network functions such as gateways and routers. The goal of SD-WAN is to create intelligent traffic routing across different connections as a response to real-time performance and application needs.

Secure Access Service Edge (SASE)

The goal of SASE is to provide secure access, no matter the location, to applications and data. The technology employs network security functions, such as secure web gateways and firewalls, with the optimization capabilities of a wide area network (WAN).

5G Technology

5G wireless technology offers markedly lower latency and faster speeds compared to 4G. This level of connection is necessary for businesses looking to power new applications, including connected cities, autonomous vehicles, and AI-enabled tools. Modern networks are well-equipped to handle the massive data demands brought on by 5G connectivity.

Building the Right Network Modernization Plan

Businesses need to see the whole picture before they can build the right network modernization plan that will align with their goals and the current status of their infrastructure. The planning process requires specialized skills, a holistic view of the current architecture, and a clear vision of future plans. If you need some insight on where to go first with your network modernization strategy, TierPoint’s IT Advisory and Consulting services can help.

We’ll cover why AI/ML technologies are important in the world today, including benefits, challenges, and common AI/ML use cases in 12 different industries.

Why Are AI and ML Important in Today’s World?

AI/ML can help businesses meet customer expectations, automate repetitive tasks, provide a competitive edge, and make sense of the massive amounts of data now being generated. In 2025, the amount of data that is being made and consumed is projected to reach over 180 zettabytes. Both artificial intelligence and machine learning technologies can extract valuable information from this data, unearth opportunities for innovation, and transform businesses to provide more personalized and on-demand experiences. Plus, rules for automation can free up human workers so they can work on more creative and strategic projects.

Benefits vs. Challenges of AI/ML

As organizations increasingly look to embrace the use of AI and ML technologies, as with anything, there are certain benefits and challenges to consider before adoption.

Business Benefits of AI/ML

Adopting new technologies can be a significant upfront task, but incorporating artificial intelligence and machine learning into your business processes can come with several benefits. Because AI/ML can be used to analyze large sets of data, unearthing new patterns and trends can lead to more informed and efficient decision-making. The data from AI/ML, as well as tools featuring the technology, can help businesses design new products and services.

By automating tasks, businesses can save time and money, investing their efforts on business-building projects. Providing a more personalized experience through AI/ML can make the end-user experience more positive, increasing retention and customer satisfaction.

These benefits can apply to just about every industry in some way.

Business Challenges of AI/ML

Just like the benefits of AI/ML can apply to almost any industry, so can the challenges. Because much of the applications for AI/ML are on the rise and in development, businesses need to be mindful of data privacy and security implications, both for the safety of the organization and of the end users. The FTC recently announced that businesses cannot quietly update their privacy policies to include disclosures about AI/ML data mining. While AI/ML can be used for product development, the organization has made it clear that consumers need to be informed on what data is being used and how.

That’s not the only ethical consideration businesses have to make. AI/ML algorithms are not perfect or objective – their training can include or grow biases that organizations need to keep in mind when using the tools.

While some AI/ML technologies have been used for years, generative AI using large language models, natural language processing, and robust data sets have been on the rise. In fact, there has been a 20-fold increase in demand for generative AI skills for workers, with 50% of employees believing that having these skills will be important for their roles – and this belief isn’t limited to IT. Despite this perceived demand, only 13% in the past year have been offered AI training. Without more training opportunities, businesses will continue to see a skill shortage.

Depending on the type of functionality businesses want AI/ML solutions to fill, implementation can be expensive. Some of this can come from the level of customization needed, or the effort it will take to integrate AI/ML with existing systems.

12 Industry Examples of AI/ML Use Cases

The benefits of AI/ML can be seen in almost any industry, and common use cases can apply established and emerging technologies in a way that’s well-suited for the nature of the business.

Generative AI can create images, text, and music for organizations, whereas explainable AI (XAI) can provide a transparent view of the decision-making process behind AI algorithms. Edge AI can deploy models at the edge of networks to reduce latency for end-users, and responsible AI can work to address the potential implications of new technology for operators and users.

While some of these conversations are still unfolding, here are some common use cases for AI/ML in 12 different industries.

AI/ML in Healthcare

The real-time imaging enabled by AI/ML in healthcare can help expedite and improve the accuracy of the diagnostic process for patients. Plus, being able to use data sets to diagnose with greater precision can also help clinicians tailor a more personalized treatment plan for patients, which can lead to improved outcomes and patient experience.

AI/ML can also play a significant role in drug development. AI can simulate molecules and predict the effectiveness of drugs during the development process before anything is physically produced.

Patients can also enjoy AI/ML in routine medical settings through AI-powered chatbots that can answer common questions and provide basic guidance.

AI/ML in Finance

Humans can bring their subjectivity into the trading process. AI algorithms can be trained to take a more objective view of the market by identifying patterns and executing trades at times that are calculated to be the most beneficial. These algorithms can also be used on the customer side as robo-advisors, offering automated investment advice and portfolio management for customers who want additional guidance without the higher touch of working with a human advisor.

Detecting fraud quickly in banking is key to keeping costs low and keeping customers protected and happy. AI tools can analyze transactions and quickly detect suspicious activity, preventing and combating fraudulent activities in real time.

AI/ML in Retail

Customers are more likely to leave a retail site if they’re not seeing the products that fit their interest. AI can personalize the customer experience by providing product recommendations based on past buying behaviors and previously indicated customer preferences.

Managing inventory is a delicate balance for all retail businesses. AI/ML solutions, like predictive AI, can help minimize waste and meet demand by predicting which product lines will be strong sellers, optimizing levels to keep both overstock and selling out at a minimum.

AI/ML in Manufacturing

One of the most important AI/ML use cases comes from the manufacturing industry. Equipment failures and downtime can lead to devastating revenue losses. Predictive maintenance powered by AI/ML can analyze sensor data and predict when equipment may be on the verge of failure, allowing for much shorter periods of downtime, if any.

While humans can see obvious quality issues, there may be pieces that come down the factory line with minute issues that can’t be seen by the human eye. AI image recognition can be trained to identify small defects in manufacturing that may cause big problems for end users. When AI is used to automate repetitive tasks in quality control or data entry, workers can be used for more creative tasks, including developing new products or working on strategic improvements.

AI/ML in Automotive

Some self-driving cars are already on the market. These vehicles process a massive amount of data and require strong 5G connections to navigate the roads, make quick decisions, and understand their surroundings.

However, more common AI/ML features can be present in non-self-driving cars as well. Advanced driver-assistance systems can offer adaptive cruise control, automatic emergency braking, and lane departure warnings. And, much like on the factory line, AI can predict failures in consumer cars and make suggestions for preventative maintenance measures.

AI/ML in Education

Every student learns differently, and AI/ML solutions can make the learning experience truly individualized, providing the right educational materials at the right pace for different learning styles and levels of proficiency.

This can also be applied to tutoring settings, where AI can be used to automatically provide feedback and guidance to students learning new subjects.

While teachers play a vital role in grading and feedback to help students grow and learn in the classroom, they can also be supported through automated grading. Educators can create rules based on a rubric and allow for automated grading of essay-based assignments, giving them more time to focus on other in-class tasks.

AI/ML in Telecommunications

Optimized network performance can be greatly improved through the use of AI/ML solutions that can route resources in an instant.

Cyberattacks can throttle the lines of communication in key moments, but AI can be used to identify and mitigate network and infrastructure cyberattacks before they’re noticed by humans.

Issues common to telecommunications can be answered by AI chatbots, leaving human interaction for more critical issues and outages that don’t have easy fixes.

AI/ML in Marketing and Sales

Personalization can be used in marketing and sales similar to its application in retail settings. AI can target ads based on user behaviors and preferences, increasing engagement rates and conversions while improving the consumer experience.

High-potential leads for sales teams can be identified using lead scoring and qualification tools powered by AI, while chatbots can assist with both lead generation and customer satisfaction by answering questions and routing prospects through part of the sales process.

AI/ML in Human Resources

Recruiters looking for specific skills and experience in new employees can use AI tools to analyze resumes and cover letters, cutting down on the number of reviews they have to perform.

Once an employee is hired, AI/ML solutions can also aid in the onboarding process with personalized training experiences.

AI/ML in Cybersecurity

The longer a cyber threat goes undetected, the worse it can be for an organization. AI/ML solutions can often find cyber threats in real time, allowing cybersecurity teams to mount faster responses. This can be done through anomaly detection – finding unusual activity that might be indicative of an incoming cyberattack.

AI can also protect consumers through fraud prevention measures, identifying and preventing fraudulent activities and transactions.

AI/ML in Real Estate

Virtual tours for homes have become more common, and AI-powered virtual tours can create a more dynamic experience for potential remote buyers. Markets can change quickly, but AI algorithms can be used to estimate property values based on up-to-the-minute market data, helping sellers and buyers get a more accurate read on pricing.

Real estate agents can improve their productivity through AI-enabled lead generation and qualification, which can be used to identify potential buyers and sellers based on their demographics and recent online behavior.

Finally, predictive maintenance can also be used in real estate for rental properties or single-family homes, giving maintenance workers and homeowners a heads up on when certain updates are needed or issues are likely to arise.

AI/ML in Energy

Optimizing energy distribution according to predicted consumption across a power grid through the use of smart grid technology can improve efficiency and reduce costs for a local power utility company.

Grids that leverage renewable energy can also use AI/ML to predict the output they should expect to see from sources such as solar and wind farms, making the integration with conventional energy sources in the grid more seamless.

While some energy demand can be predictable, many factors can change that demand quickly, including weather, historical data, and certain events. AI can perform demand forecasting to more accurately meet these fluctuations.

Improving Business Outcomes with AI/ML Adoption

Adoption of artificial intelligence and machine learning solutions can be a blessing to your business and end users, but knowing how to apply the solutions and best leverage the data you already have can feel like a big weight on your shoulders. At TierPoint, our consulting services can help you determine where and how AI/ML tools should be implemented to improve business processes across a broad spectrum of industries.

In the meantime, read through our whitepaper to dive into exactly how AI/ML is benefitting organizations throughout the world as well as additional applications of each.

FAQs

In this article, we’ll explore principles of a DevOps migration strategy, key considerations, and best practices when developing an effective DevOps migration strategy.

An Overview of the Cloud DevOps Model

In a cloud DevOps model, development and operations teams collaborate closely throughout the software development lifecycle, including the design, development, testing, deployment, and support phases. This collaborative approach improves the efficiency of delivering applications and services. How? It breaks down silos and promotes shared responsibilities.

To further align operations with development practices, operations teams may adopt techniques commonly used by developers. For example, they may embrace the Agile development process, which emphasizes iterative and incremental development, close collaboration, and frequent feedback. By adopting Agile principles, operations teams can adapt to changing requirements and deliver value more rapidly.

What is a DevOps Migration Strategy?

A DevOps migration strategy is a detailed plan and approach used by an organization when transitioning its applications and infrastructure to a DevOps environment. Although a DevOps environment mainly involves a change in organization structure (rather than physical infrastructure), it can be completed alongside a cloud migration, which enables organizations to take advantage of the benefits offered by cloud platforms.

When a business decides to shift to a DevOps configuration, a well-defined migration strategy becomes essential and helps determine necessary considerations for successful organization restructuring. It involves identifying and reviewing the roles and responsibilities of team members involved, defining the new workflows and processes, assessing the tools and technologies that may need to be incorporated or updated, and establishing metrics to measure the success of the migration.

A cloud DevOps migration strategy needs to use best practices and cover several key aspects:

• Outline the desired state of the DevOps environment
• Acknowledge the specific goals the organization wants to achieve
• Provide a roadmap for the phased implementation of DevOps practices, considering factors such as project priorities, resource availability, and potential risks
• Address the integration of cloud services and infrastructure into the DevOps environment, which may include cloud-native tool adoption, configuring cloud resources to support automation and scalability, etc.

Additionally, effective communication and collaboration between teams are crucial during the migration process. To ensure the successful adoption of DevOps practices, the strategy should also include plans for fostering:

• Cross-functional collaboration
• Knowledge sharing
• Training initiatives

Another important aspect of DevOps migration is the increased focus on testing. Operations teams may start testing more frequently and early in the development process to identify and address issues proactively. This shift towards continuous testing helps ensure the quality, reliability, and performance of applications and reduces the likelihood of encountering problems in production. 

Why is a DevOps Migration Strategy Important?

As IT systems become more complex, migrating to a DevOps configuration can help organizations find simplicity and efficiency . The cloud DevOps model offers a solution by streamlining processes and fostering collboration between teams within the organization. The more you’re able to collaborate and integrate teams using DevOps principles, the more improvements you can see.

For example, by encouraging cross-functional collaboration and knowledge sharing, companies can unlock synergies and leverage diverse expertise. This integration leads to smoother handoffs, faster issue resolution, and a shared sense of ownership, ultimately resulting in high-quality deliverables.

Principles of a DevOps Migration Strategy 

Evaluate IT Infrastructure and Tools

To lay the proper groundwork, an organization should begin its DevOps migration strategy by identifying goals that align with where they want its development projects to go. The DevOps team can use these goals to evaluate their infrastructure and determine if they have the tools and framework they need to get to where they want to be. The strategy should include a plan for how to phase out less-than-helpful tools, as well as a list of priorities and desired features that can help choose new tools.

If some tools have been working fine, but could stand to be changed in the name of efficiency, integration, or cost-saving, include those notes in the evaluation as well. This can also help determine what gets migrated now versus later, or what gets migrated at all. Businesses should also consider the time it takes to migrate workloads over and the potential disruption a move will have on business.

Traditional  IT vs. DevOps

Traditional IT is generally more specialized and siloed. For example, it’s uncommon to have the operations team interact with the development team. IT teams may also have divisions in labor between compute, networking, and storage tasks. Because of this segmentation, it can be difficult to coordinate larger tasks, leading to delays in provisioning.

In contrast, DevOps merges the development and operations teams to form a cohesive unit. The team then works together to achieve a unified outcome through shared processes and automation.

Automation Tools

By leveraging automation tools and practices, businesses can achieve faster and more reliable deployments, saving time and effort while ensuring consistency across environments. Which tools make sense will depend on how your business is currently structured and which processes can be replicated using automated rules.

Tools commonly used in cloud DevOps include:

• Continuous Integration or Continuous Deliver: These automate the build, testing, and deployment processes. They also allow developers to frequently integrate code changes and automatically build, test, and launch applications in different environments.
• Configuration Management: Such tools assist in automating the provisioning and management of infrastructure and system configurations.
• Infrastructure as Code: IaC tools enable the provisioning and management of cloud resources and infrastructure using code; and they enable infrastructure to be defined, versioned, and deployed programmatically.
• Testing and Test Automation: A critical element in the DevOps model is testing to detect issues early on in the development process and ensure quality. Testing tools can be used to automate various types of testing, including unit, integration, and end-to-end.
• Containerization: Containers provide a consistent, isolated environment for apps to operate across different systems. These tools allow for the creation, deployment, and management of containerized applications.
• Monitoring and Logging: Monitoring and logging tools can be used to track the performance, availability, and health of applications and infrastructure while also providing real-time visibility into elements such as system metrics, logs, and events.
• Collaboration and Communication: These tools enable effective communication and coordination between different teams within an organization, and allow code, documentation, and insights to be securely shared.

High-Performing IT

The continuous delivery approach strengthens the feedback loop between development and operations teams. This leads to faster identification and resolution of issues, thus allowing teams to identify and resolve defects earlier in the development process which improves the overall quality and stability of the software.

The frequent deployment characteristic of cloud DevOps models contribute to decreased failure rates and improved recovery times. “High-performing” IT companies who adopt a mindset that encourages risk-taking and experimentations, are more adept at discovering and addressing issues rapidly. If a failure or an incident occurs, the streamlined deployment processes and automated infrastructure provisioning enable swift recovery, as well as rollback, minimizing the impact on users and the business.

All in all, the cloud DevOps model not only results in lower failure rates and shorter recovery times, but also enhances an organization’s ability to:

• Quickly deliver value to users
• Stay ahead of market demands
• Drive innovation

DevOps Requires Teamwork

Of course, anything that calls for the formation of a team also requires collaboration to be successful. This can mean making large cultural shifts and setting new organizational expectations. Consider the following questions:

• What are common goals everyone can get around? Common goals should align with overall business objectives such as enhancing customer satisfaction, improving time-to-market, or increasing operational efficiency. By establishing shared objectives, the team can work together towards a unified vision.
• How will responsibilities be assigned and what does the management structure look like? Determining the management structure is another critical aspect of team collaboration. Embracing agile and lean management principles can empower teams to make decisions, take ownership, and self-organize, leading to increased collaboration and efficiency.
• How will success be evaluated? Key performance indicators (KPIs) may include deployment frequency, lead time for changes, mean time to recovery, customer satisfaction, and business impact. Regularly reviewing and assessing these metrics enables the team to monitor progress, identify areas for improvement, and celebrate achievements.

The important thing to remember is that you can start small. Building the foundation and determining what will be automated, how testing will be carried out, and what goals the team shares, will pave the way to a more effective DevOps team.

What to Consider in Your DevOps Migration Strategy?

DevOps migration is an ongoing process that requires commitment, continuous improvement, and a willingness to embrace change. It requires teams to adopt new mindsets, embrace automation, and leverage shared tools and processes to streamline the software development and delivery lifecycle.

When you’re migrating your teams to a DevOps model, you’ll want to consider the following:

Selecting the Right Tools & Technology

Just because a DevOps tool is popular doesn’t mean it’s appropriate for your team and your specific requirements . You’ll want to consider the learning curve necessary to master the tool, how it integrates with the rest of your tech stack, how well it can scale, the availability of documentation, its security features, and more. Some popular tools for configuration management are Ansible, Chef, CFEngine, and Puppet, but to make the right choice for your team, you’ll have to conduct a careful evaluation.

Planning Infrastructure Changes

As organizations embrace DevOps practices, they may find that their existing infrastructure needs to be reconfigured or optimized to support the new workflows and requirements. This could involve scaling up or down resources, modifying network architectures, implementing automated provisioning and scaling mechanisms, adopting cloud-native technologies, or moving legacy application to a new cloud environment altogether.

Infrastructure changes may require collaboration with a data center provider or help from other managed service providers to ensure there is no dip in uptime and service.

DevOps Security (DevSecOps)

Microservices and containers used in DevOps, as well as other services and apps, can be vulnerable to security breaches if not properly managed and secured. However, when implemented with the appropriate security measures, they actually enahnce the security posture of DevOps environments.

A DevSecOps model places security at the center of DevOps processes, ensuring that it doesn’t become forgotten. The security team should work alongside developers, testers, and operations teams in a way that security becomes less of an event and more of an iterative, ongoing activity. This includes ensuring secure configurations for cloud resources, integrating security practices into development and deployment pipelines, implementing identity and access management controls, and leveraging encryption and monitoring solutions to protect data and applications.

An increase in security breach incidents, coinciding with stricter regulatory standards, has transformed DevSecOps from optional to practically mandatory.

Security is Shared Responsibility

As part of this integrative model, all parties are responsible for security, not just in a DevOps team, but across the entire organization. IT teams need to train employees to spot common security threats, security teams need to implement protocols, and development teams need to keep security in mind as they write code.

Automation

DevOps and automation often go hand-in-hand. An organization that’s looking to make its processes more efficient via DevOps should also understand the agility and improved accuracy that comes with automation. Implementing automation also reduces the amount of boring, repetitive tasks team members have to take on, allowing them to focus on more meaningful work that drives the business forward.

Continuous Integration and Continuous Delivery

In continuous integration and continuous delivery (CI/CD) environment, applications are always ready to deploy changes as the need arises. The only way this can be accomplished effectively is by automating certain processes that smooth out the development pipeline.

Small Code, Big Impact

Sometimes it’s the smallest pieces of code that have the biggest impacts on your business. This is often the case with automation. Creating small solutions that speed up a repetitive problem by even seconds can accumulate quickly into hours saved per week or more. The DevOps team should be considering what small changes can make these big impacts, and the Ops side can lend a lot of insight in this area.

Network Monitoring

By monitoring a network, businesses can find and troubleshoot problems regarding network traffic, applications, and devices. If there are performance bottlenecks or security threats, they can be found through network monitoring. A DevOps environment should have application, configuration, and cloud monitoring as part of what they’re observing in a network.

Application Monitoring

If users encounter a problem with an application, it may cause a loss of trust, decrease in engagement, or it may even get them to switch to another business. Mission-critical applications need reliable uptime, and they need to be free of bugs. Application monitoring can identify problems before they become detrimental to your business.

Configuration Monitoring

The way a network is configured can impact compliance, performance, and security. These settings mainly impact internal users, which means problems can hamper productivity or connectivity with customers and other end users. Configuration management should also be set up so that when changes are made, companies can revert back to previous configurations if something goes wrong.

Cloud Monitoring

Without proper monitoring, the use of cloud resources can get out of control. Organizations should be in the practice of tracking resource usage, managing budgets, and setting up automated rules to keep usage, and therefore costs, under control.

How TierPoint Can Help Accelerate Your DevOps Practice

A strong DevOps practice requires collaboration, communication, and a clear path forward. TierPoint offers consulting to help businesses form a strong DevOps practice, as well as define a DevOps implementation roadmap.

Need help with your DevOps practice? Learn more about our DevOps consulting services.

What is Data Center Consolidation?

Much like it sounds, data center consolidation is a strategy designed to reduce your data center footprint. It can streamline your workload in a way that results in big advantages. When you work to consolidate data, you can enjoy cost savings, improved security, energy efficiencies, and increased productivity. Additionally, with the right strategy, you can still have reliable, fast access to your centralized resources no matter their location.

Importance of Data Center Consolidation

Is your business growing? Or have you been running workloads out of multiple data centers or a combination of on- and off-premises facilities? You may benefit from a data center consolidation project.

When done correctly, data center consolidation efforts can…

• Boost operations
• Make the use of IT assets more efficient
• Reduce redundancies
• Make the experience better for the end-user

Benefits of Data Center Consolidation

There are numerous benefits that accompany data center consolidation efforts.

Physical Location

If one of your current data centers is in an area prone to extreme weather and disaster, consolidating to a data center in a less vulnerable location or one that is designed to handle such events can improve security and reliability for your infrastructure and end users. The data center may also include physical elements that make it less vulnerable to threats, such as cooling and fire suppression systems.

Shifting from CapEx to OpEx

In a CapEx model, organizations pay for infrastructure costs upfront, such as hardware, and all of the costs needed to build an on-premises data center. With OpEx, on the other hand, businesses only pay for the services and equipment they use, typically on a subscription-basis, which is provided by a third-party. Using a third-party data center is considered OpEx since you pay for the space/resources you use and the time in which you use it.

Many data centers are under utilizing their capacity, but still paying for it, under the CapEx model. In an OpEx model, organizations are able to scale what is needed rather than invest up-front in infrastructure/hardware that is not currently needed.

For organizations with variable needs, or ones that may grow in the future, shifting the approach from CapEx to OpEx can save money and reduce the likelihood of having to make major investments that may take years to pay off.

Energy Efficiency

Consolidating your workflows and shutting down unnecessary servers or redundant systems leads to efficiencies in data processing and reduced energy consumption. According to a recent study by the University at Buffalo, “Data center managers can reduce energy consumption 10-30% by consolidating jobs to as few servers as possible when workloads are high, and evenly distributing the workload across all servers when loads are low.”

Additionally, an Energy Star study found that decomissioning a single server can help businesses save around $500 in energy costs and $1,500 in hardware maintenance costs on an annual basis.

Physical Security

If your consolidation plan includes a move to a third-party data center provider, you can enjoy the benefits of their physical security systems without the burden of implementing your own, including security cameras, an access control system, environmental controls, and a secure perimeter.

It is also important to ensure your data center provider meets your compliance requirements, such as HIPAA, SOC 2, HITRUST, etc.

Complexity Reduction

When you spread out your data, your risk increases. Here’s an example. Say you’re a business that’s spent the last decade on acquisitions. You now find yourself with data in 10 locations with different security standards. In the event of a security breach, you may find yourself in a tangled mess.

Data center consolidation means you can also monitor your data from a central point. This is true even if your workloads are hosted in multiple centers run by the same provider or if you’re computing on the edge. Third-party data centers have in-house data security experts who offer services that will best protect your data.

Increased Productivity

Imagine the effort it takes to pull one lever in three different locations. Now imagine the effort it takes to pull it in one place, even if you have to do it three times. The more you streamline your processes, the more you save time and money while stregnthening uptime and availability.

With a third-party data center, you also have options for managed services. These services will help you augment your team with onsite data center experts. That benefit can free up significant time so your team can focus on strategic revenue-generating projects.

Cost Reduction

With proper planning, data center consolidation projects can reduce your costs considerably. To attract more customers and effectively compete, data center providers have reduced their rates for leasing. Moving to a provider from on-premises shifts costs away from onsite management.

When you consolidate your workflows into a single colocation facility, you can also reduce redundancies in processing and staff resources for more savings.

Access to Managed Services

Most third-party data center providers offer additional managed services for customers. These can include remote hands, routine maintenance, a workspace for business continuity purposes, network services, and more. 

Data Center Consolidation Risks

Data center consolidation can also come with downsides. Understanding what they are, and preparing to counter them, is the best way to keep the move a beneficial one.

Downtime

Especially when consolidation is in process, business operations can get interrupted, leading to downtime. The best way to keep this from happening is by having a team of data center facilitiy experts provide a comprehensive consolidation and migration strategy with a roadmap in place incorporating client maintenance windows.

Loss of Data

Data loss in the consolidation process can come from human error, security breaches, and technical failures from hardware or software. Moving to a smaller data footprint can reduce complexity, but it can also mean that mistakes or vulnerabilities have a greater impact on your infrastructure.

Compromised Data Security

Likewise, having a single point of failure post-consolidation can make security risks more impactful. Maintaining a failover point at a second data center, even when looking to simplify, is an important safeguard for businesses looking to stay operational when experiencing a disaster or breach.

Data Center Migration Strategy

Consolidation will include at least one data center migration, where you will be moving data, applications, and infrastructure from one data center to another. The process is often more complicated if you are moving from an onsite data center to a third-party provider. Developing a strategy that includes goals for productivity, redundancy, network connectivity, scalability, efficiency, security, compliance, and performance can help you get the most out of the migration.

Data Center Consolidation Best Practices

Evaluate

• Hardware Assets: Take stock of the hardware being used in your current environment. If you own servers, networking equipment, and storage devices, that’s something you’ll need to think about before consolidating and moving to a data center provider. Set a time in advance of the move to re-boot all equipment to ensure they will turn on properly.  Will you take the equipment with you to a colocation data center, or will you decommission and sell it? This may depend on the age of your equipment, the future plans of your business, or your current budget.
• Facilities and Energy Usage: To determine that you’ll actually be saving money by consolidating, you’ll want to evaluate the current energy usage and the cost of maintaining facilities and compare it to what you expect your expenses to be in a new data center. If you’ll continue to manage your own data center, you need to factor in the costs of cooling, heating, security, and everything else needed to run the facility. If you migrate to a data center provider, your main cost will be based on the space and power you’re using in the building.
• Geographic Location and Real Estate: Different geographic locations are susceptible to different environmental conditions. There also may be differences in price due to market demand in certain cities. The buildings themselves may also have desirable features, such as environmental controls and security measures. Also consider network latency, which can also vary by location.
• Software Assets: If you’re consolidating due to a merger, you also want to think about the state of your software. What needs to move into the new data center? Is there anything that can be pared down prior to migration? Do you need to make changes to licensing agreements or find integrations for additional critical software?

Plan

• Data Center Consolidation Disaster Recovery Planning: Because you have fewer points of failure, you’ll want to keep that in mind when doing disaster recovery planning. You should understand the impact a disaster may have on your systems in a consolidated environment long before anything happens. One of the services your data center provider may offer to help with this is disaster recovery as a service (DRaaS).

Design

Once you have an idea of the current environment, where you’ll be consolidating your systems, backup plans, costs, and assets, you should work on a design of what the new infrastructure will look like. Data center infrastructure management (DCIM) software can be used to create a virtual representation of your consolidated environment.

Consolidate

The consolidation plan gets enacted on the day you start moving everything. It should outline any equipment migration, potential phases, team access needs and responsibilities, and clear instructions to go with the design. The more you plan ahead of time, the less downtime you’ll experience.

Working with a Managed Service Provider During Data Center Consolidation

Data center consolidation is a major change to your IT infrastructure. And it’s a project that requires expertise and planning to mitigate risks and downtime. An IT consulting service should help you benefit from new technologies and adapt your IT infrastructure to meet new business requirements. Professional help with consolidation strategies means you’ll get support for:

1. Physical and cloud migrations
2. Security consulting
3. Disaster recovery
4. Business continuity planning
5. Cloud readiness assessments

TierPoint’s team of experts can guide you toward the right data center approach as part of your overall IT modernization strategy. Together, we can review your entire IT infrastructure to find the right data center optimizations that will help you achieve your modernization goals. Read our guide on IT Modernization and Optimization to find resources to help you learn more and achieve those goals.

FAQs

As businesses look to hybrid cloud adoption solutions and migrate to hybrid environments, they want to do it quickly, but effectively. Adopting the cloud comes with benefits like cost savings and more opportunities for digital innovation, but making the change doesn’t happen at the press of a button. Even if you have the talent to manage an IT environment, it’s another thing entirely to manage hybrid cloud environments, conduct a cloud migration, and execute a cohesive hybrid cloud strategy. How can you speed up hybrid cloud adoption without running into snags?

What hinders hybrid cloud adoption?

Several factors can stand in the way of hybrid cloud adoption and that’s why having a solid cloud adoption strategy can help reduce the following obstacles including, the cost of implementation, challenges with existing architecture, security concerns, and conflicts with other business challenges and requirements.

Another way to overcome these obstacles is by using cloud services such as Managed Azure Stack. This service enables businesses to extend their Azure cloud capabilities across both public and private environments. By taking advantage of the familiar Azure ecosystem and offering a true hybrid solution, companies can ensure a smooth transition to accelerate their hybrid cloud adoption.

Costs across multiple cloud environments

There are many costs associated with the cloud, especially when you have multiple cloud platforms within one environment. Depending on the platform, you may have the option of fixed or consumption-based pricing models. Businesses often have trouble identifying the right pricing model, but it may depend on the workload. Certain public cloud “pay as you go” pricing changes depending on time (or data), CPU, and execution usage.

Understanding these pricing models will provide better visibility for what you’re spending where. And help you see where optimizations can be made along the way.

Migration

Migrating to a cloud platform is already a complex task. Migrating to multiple cloud platforms in a hybrid environment can add even more complexity. Rushing to move your infrastructure to the cloud could mean putting things together in a less-than-ideal way – skipping key integrations, cutting corners, and creating broken pieces along the way that lead to increased costs and decreased productivity. Taking the time to do things right and create a cohesive cloud migration strategy will help circumvent any major issues.

Security & compliance

Managing security requirements across multiple cloud environments can introduce more risks. Those multiple environments also need to meet your regulatory and compliance requirements. Security teams need to account for this added complexity and supplement internal security with outside expertise when implementing best practices and frameworks.

Governance

What happens after a successful cloud migration? Many businesses fall short of having a strategy once they have deployed a hybrid cloud solution. Governing that environment requires cloud visibility and resources to maintain that environment and reduce cloud sprawl. This also means having the right staff to maintain this environment. Some organizations do not have those internal resources and look outside of their organization to fill gaps.

The secret to hybrid cloud adoption, deployment, and management

We’ll level with you. The secret to successful adoption, deployment, and cloud management is not as complicated as you think. It all comes down to bringing in the right qualified people. Most businesses can be more successful with cloud infrastructure by relying on expertise from a managed services cloud provider. The right partner can help you accomplish the following:

Identify the right roadmap

After looking at the current state of your IT environment, an experienced cloud provider can help you figure out the best pathway to success. The process would look something like this: They would start with understanding your business goals and outcomes. Then, they will survey your entire IT environment to understand your current infrastructure. What does your infrastructure look like, including storage, hardware, software, and networking? This cloud partner would examine your workload needs and identify the right cloud platforms for each one. Depending on what they find, the options can vary. A cloud provider might suggest the use of a private cloud, multitenant, on-premises infrastructure, or public cloud deployments.

Private cloud

Private cloud infrastructure can either be located on-premises or at a separate data center facility that you lease or own. When it comes to the private cloud, you own your infrastructure and resources. If you use hosted private cloud, however, a third party will manage your resources. Hosted private clouds come with additional peace of mind.

Also read: The Big Benefits of Private Cloud

Public cloud

Hyperscale, also known as public cloud, is generally a multitenant cloud model where you pay for cloud services but skip the large infrastructure expenses. The public cloud enables businesses to scale their infrastructure. All while saving money on power, space, cooling, and facilities.

A public cloud is a type of multitenant architecture, where your resources are stored in a shared environment. While the private cloud involves a dedicated environment, multi-tenancy architecture has multiple customers operating on the same servers. There are many benefits of public cloud and it can be a great, budget-friendly option for organizations, but cost control and expertise are also up for question and need to be evaluated carefully.

On-premises

Many businesses have critical legacy systems that still need to be supported. Often, they are not workloads they need to sunset or rebuild. You don’t have to remove on-premises data centers and workloads from the mix. Eventually, you’ll probably want to migrate everything to make updates and continued innovation more seamless, but in the meantime, some of your workloads could be hosted in on-premises environments.

Hybrid cloud or multicloud

And lastly, all of these efforts can be combined in different iterations to form hybrid cloud and multicloud models. A multicloud is any infrastructure that includes a combination of cloud options (e.g., one private and two public cloud environments). Hybrid cloud can include a mix of cloud and non-cloud, on-premises environments. Using either of these approaches can mean getting the best of a few worlds all rolled into one.

Understanding these options, building a roadmap and strategy, and helping you deploy can mean your business adopts the cloud at a quicker rate. Working with an expert to sift through the flavors of the cloud and picking the combination that’s just right for your organization also optimizes your cloud spend – you can rest assured that you aren’t spending money or resources on things you don’t need.

Provide expertise that would otherwise not be available

When you work with an experienced cloud partner, there’s no need to search high and low for top IT talent. Your cloud partner has hired staff with specific expertise tailored to cloud deployments. They can strategize and migrate to a hybrid cloud for your business with ease because they’ve done it before. Experts are at your fingertips through your partner relationships. They have the techniques, knowledge, and advanced tools needed to successfully lead a hybrid cloud migration.

Spearhead cloud automation projects

Are there certain tasks your team engages in repeatedly? Ones that involve pressing the same sequences of buttons over and over? Repetitive tasks that change with few variables could benefit greatly from cloud automation. Automating tasks can cut down on redundancies and improve productivity; however, creating automation takes time and expertise.

A skilled cloud partner can not only help you identify opportunities for automation, but they can also create, test, and implement automation projects. Your employees will have some time freed up for other, more strategic tasks, and you may see fewer errors on the simpler tasks that are now performed automatically.

Help companies overcome IT skill and resource gaps post-migration

As more businesses compete for the small pool of cloud experts and engineers, the global IT worker shortage continues to grow. This shortage runs in direct opposition to the digital transformation goals of most businesses, with 58% of IT leaders increasing, or hoping to increase, investments in emerging technology. For 64% of the new tech these firms would like to take on, they have found that talent shortages have been the biggest thing standing in their way.

A cloud partner can bring in outside help. Day-to-day cloud operations can be handled by an external team, and the configuration of skills you have at your disposal can change alongside the changing needs of your digital transformation projects, all without having to hire, fire, or train additional internal employees. With access to varied talent, your staff has more time for business-critical projects, such as product development.

Cloud modernization can consist of a lot of heavy lifting, but it doesn’t have to. If you’re looking to sell hybrid cloud adoption or a modernization project to leadership, and you’re looking for more resources, check out our eBook, How to Sell Cloud Modernization to Your Leadership.

The growth of data, the surge of security breaches targeting remote workers, and tighter regulatory requirements on consumer data protection, are challenging data-driven companies to rethink their approach.

“Organizations need mature modern data protection, data backup, and recovery,” said Andy Fernandez, disaster recovery product marketing manager for Zerto. Fernandez spoke at the ActualTech Media’s spring summit.

Top 5 challenges for protecting data

Companies face several challenges when it comes to improving and modernizing data protection, however. Those include:

Hybrid IT environments

Multicloud and hybrid cloud environments, with various platforms, operating systems, and geographic locations, are challenging to secure. If possible, IT should find cross-platform security tools that work across all of the environments, both on-premises and in the cloud. There are also best practices that can help ensure data is protected in a hybrid environment.

One of the most important protections is identity and access management (IAM). IAM controls user access based on an employee’s job needs. With an IAM solution, an IT manager can enforce “need to know only” access to systems.

This minimizes the potential damage when a user’s login credentials are stolen during a data breach. IAM also helps prevent an unhappy employee from stealing or destroying data.

Encryption is also a key element of data security in distributed and hybrid environments. Encrypting data traveling between clouds and between cloud and non-cloud systems makes data unusable should it be intercepted.

Many cloud providers offer consulting expertise on securing hybrid and multicloud environments. In addition, a cloud provider that specializes in managed security services can help configure and implement cross-platform cybersecurity solutions. MSPs are cloud services providers who handle many of the planning and management tasks for their customers.

Physical insecurity

With more employees connecting remotely, there’s a risk that other device users could inadvertently download malware, steal passwords (or personal data), or steal the device. As many office buildings are still half empty, it’s also easier for outsiders to steal devices or data without detection.

Social engineering and human error

A majority of cyber-attacks depend on human gullibility to succeed. Phishing is when a cybercriminal attempts to trick an unwary user into giving up confidential information or personal information. Phishing is a common social engineering method.

According to industry reports, 65% to 90% of breaches are caused by phishing or other social engineering campaigns. Ransomware, a top security threat that can cripple a business, is typically spread through social engineering.

Many security solutions filter out phishing and malware attachments, but some inevitably slip through. Training employees to recognize social engineering in an email or phone call is a critical aspect of data protection. Training is especially critical for remote and home-based workers.

Data storage and disaster recovery costs

The average in-house cost of storing a petabyte of data is nearly $2 million over five years. That includes hardware, maintenance, staffing, etc. As companies accumulate more and more data, the cost of keeping it on-premises is becoming unaffordable. Yet no business can afford data loss.

Many organizations solve this problem by turning to cloud-based disaster recovery (DR) services. Cloud DR provides a reliable storage solution at an affordable monthly cost.

The challenge is to select the disaster recovery service that best fits an organization’s budget and recovery time requirements. The recovery point objective (RPO) and recovery time objective (RPO) are the two most important metrics in selecting a cloud provider.

Also read: 3 Recovery Objective Considerations for Your Disaster Recovery Plan

The RTO measures how quickly data is restored. An RTO might be an hour, a day, or just a few minutes, depending on the company’s business requirements.

RPO determines how fresh the recovered data will be. Companies in industries like e-commerce, financial services, or healthcare need to recover all of their data and restore services immediately.

Other industries might be able to lose more of their data or wait longer for full recovery in exchange for a lower cost.

Lack of expertise

Busy IT departments often lack the time or expertise to implement and manage data storage and recovery programs. Fernandez quoted from a survey on DR and data protection that found a lack of IT skills was the #1 data protection and DR challenge for organizations. Small businesses particularly lack knowledge of data storage and recovery techniques.

Learn how to overcome data protection challenges

Learn more about modernizing your data protection. Watch TierPoint and Zerto speak on data security and recovery at ActualTech’s spring summit below.

Disaster recovery services providers can assist with finding the right solution for businesses of all sizes. Providers offer:

• cloud assessments with a custom roadmap for the customer
• disaster recovery and business continuity planning
• deployment of disaster recovery solutions
• monitoring and management
• troubleshooting and tech support
• the ability for the client to subscribe to the level of service they specifically need

TierPoint’s recovery experts can provide you with expert guidance in planning, addressing RTP/RPO, implementation, monitoring, and management. Contact us today to learn more.

In this blog post, we’ll examine the obstacles preventing many organizations from launching an SAP S/4HANA migration, especially when migrating from an existing system on-premises to the cloud. 

SAP S/4HANA in the Cloud: What’s Holding You Back?

Migrating workloads to the cloud lies at the heart of the digital transformation strategy for many enterprises. The latest Cloud Computing Study from IDG found that 81% of organizations house at least some applications in the cloud. 

By late 2021, 59% of enterprises plan to house the majority or all of their workloads in the cloud. Only 5% plan not to leverage any cloud-based resources. Given the importance of digital transformation to business processes and today’s digital business models, we’ll see how long this tiny minority can hold out.

The study didn’t report on which workloads were still on-premises for these organizations, but it’s a safe bet many of them involve heavily integrated ERP (enterprise resource planning) systems. In a corporate fact sheet dated August 2020, SAP claimed that 77% of all business transactions worldwide touch an SAP system. 

With so many touchpoints throughout the enterprise and into the supply chain, migrating to a cloud-based ERP software system is one of the most challenging digital transformation initiatives an enterprise can undertake. It’s understandable if SAP users haven’t yet jumped at the opportunity to migrate their core ERP to SAP S/4HANA in the cloud.

Also read: How SAP S/4HANA Fuels ERP Digital Transformation 

Here are three obstacles that often cause business and IT leaders to think twice about moving SAP S/4HANA workloads to the cloud.  

1. Platform launch-related delays

SAP S/4HANA was first released in 2015. Early on, confusion regarding the migration path created some hesitancy. SAP also said they would be discontinuing support for SAP ECC, the predecessor to S/4HANA, in 2025.  

Later, the date was changed to the end of 2027, with the option to pay higher maintenance premiums to receive support through 2030.

This gave users a little breathing room but also a reason to delay data migration planning. Still, experienced SAP administrators know the clock is ticking. Migrating to a new version of SAP can be a multi-year proposition. To go live before support ends, they will need to kick off a migration planning project soon. 

2. SAP Cloud concerns

SAP solutions include a SaaS version of S/4HANA. This can be deployed in an SAP single or multitenant cloud. While SAP is the undisputed leader in ERP solutions, not every business or IT leader is ready to trust them with their cloud environment. For example, those organizations that leverage multiple clouds might have concerns about connectivity and latency between an SAP-managed cloud and dependent solutions running on AWS or Azure.  

SAP’s cloud offerings have also created some concerns over control of the environment and applications. In an SAP cloud, the vendor maintains control over when new releases and upgrades are implemented. This can create concerns for enterprises when they have non-SAP business systems tightly integrated to their ERP backbone. Without the time to test the impact of a new release, they could find themselves scrambling to restore these connections. 

3. Gap in talent and resources

SAP S/4HANA can also be deployed in a hyperscale cloud environment, like AWS, or in a proprietary cloud. However, these options require the enterprise to have both application, user experience, and environment expertise. It’s rare to find an in-house IT team with those skill sets, especially for an ERP system that’s been completely rearchitected.

Businesses often ask themselves: Has my team migrated an SAP instance before? Can my team manage a Brownfield or Greenfield deployment? Would my deployment require a third-party provider’s assistance?

In addition, unless using a third-party data center, a proprietary cloud deployment carries with it the same overhead burden as any other on-premises deployment. Most organizations migrate to the cloud to reduce or eliminate costs such as facility maintenance, investments in hardware, and staff expenses. 

Brownfield vs. Greenfield: What’s the difference?

SAP S/4HANA migration projects typically fit into one of three types: 

• Greenfield approach – This is one in which the organization is migrating from a non-SAP ERP application.  
• Brownfield approach – A brownfield migration refers to a migration from SAP ECC to S/4HANA in which existing datasets are migrated. 
• There is a third option that goes even deeper called Selective Data Transition. This usually refers to a migration in which the company is an existing SAP customer (like SAP HANA or ECC), but the SAP implementation is fresh. In this migration path, only select data sets are migrated.  

Also read: Top 5 SAP ERP Challenges for IT Organizations 

Plan for and overcome SAP migration risks

Migrating to SAP S/4HANA requires planning, and application, and environmental expertise. Unfortunately, many organizations that provide SAP migration services focus on one or the other. Working with a managed service provider helps organizations overcome many of the risks of an SAP migration and ongoing management of their production environment. Providers work closely with your internal team to help overcome bandwidth issues and skill set gaps.  

List of questions to ask for your SAP S/4HANA migration

During your migration planning phase, you need to answer these vital migration questions:

• Is this the right time to upgrade?
• Do we have the right system architecture for a smooth implementation and adoption?
• Will our data migrate as is? Or do we need to scrub/recreate some data sets?
• Should the migration be completed in phases?
• How long will it take to complete, and what are the milestones?
• How much downtime should we expect? How can we minimize the impact on business operations? 
• How will we keep our data secure during the migration? How will we keep it secure once we go live?

By nature, ERP migrations are a project that involves many different individuals with unique skill sets from across the organization. Ensuring vital responsibilities are covered and assigning people to the right roles is essential to an effective, surprise-free transition. 

TierPoint’s SAP Managed Services for S/4HANA offering is an innovative and comprehensive solution for the modern enterprise that combines a best-in-class, high-availability private cloud platform with SAP Managed Cloud expertise. Managed SAP S/4HANA provides the benefits of the cloud (converting CapEx to OpEx, lowering overhead, reducing IT burden, real-time monitoring, etc.) combined with greater flexibility and control over your environment as well as expert SAP and cloud services.  

To learn more about Managed SAP S/4HANA and our other managed services offerings, visit us on the web. You can also download our Managed SAP S/4HANA fact sheet and schedule a consultation with one of our SAP migration specialists. 

What is causing this acceleration?

Social distancing and hygiene requirements forced hospitals and other providers to speed up their adoption of transformative technologies, including cloud services, electronic records, virtual offices, telemedicine, and artificial intelligence (AI). Are You Keeping Up with Healthcare Transformation Tech Trends?

Healthcare leaders ramped up their deployment of e-health or telehealth applications. This allows patients could see their doctors remotely and added or upgraded self-service portals to give patients easier access to health services and to their personal records. Team collaboration tools and software-as-a-service applications helped physicians, researchers, pharmacists, and other healthcare professionals share information and insights.

Most if not all modern healthcare technologies will depend on the cloud, from virtual urgent care applications and cloud data storage to secure file sharing.

The healthcare cloud computing market will likely surpass $66.3 billion by 2030, from $26.5 billion in 2020 according to Fatpos Global consulting firm, while McKinsey estimates that up to $250 billion of U.S. healthcare spending may be on virtual healthcare products and services.

Cloud computing is popular in most industries to help reduce IT costs and increase the efficient use of IT resources. Cloud enabled digital transformation allows companies to easily scale up their cloud resources, as needed, and then release those resources when no longer required.

Top drivers for cloud adoption in healthcare

In addition to those benefits, health systems have additional motivations for adopting cloud computing. Those are:

Demand for remote patient care delivery

When Covid-19 struck, consumer demand for digital healthcare services rose sharply. Virtual patient visits rose from 15% in 2019 to 28% in April 2020, and 80% of patients say they’ll continue using virtual visits even after the pandemic is over, according to Deloitte.

In addition to virtual doctor’s visits, consumer use of healthcare portals rose substantially and will continue to grow. According to IDC, two in three patients will access healthcare through a portal or other “digital front door” by 2023.

The overall spending on telehealth technologies. IDC has projected that digitally-enabled remote care and clinical trials will drive 70% growth in spending on connected health technologies by 2023. It is up to the healthcare providers to continue to offer quality care, despite the shift from in-person visits.

Securing medical records and vital patient data

A report by Accenture noted that security in the public cloud requires end users and applications to be explicitly granted permission for access. The default is to refuse access. Public cloud providers today are also secured by multiple, advanced technologies including encryption, identity, and access control, intrusion prevention and detection, real-time security intelligence feeds, and threat management. A good cloud security services provider will have experience in both the healthcare industry and cloud security, and certification in government regulations and industry best practices.

Blockchain is gaining adoption in healthcare. Blockchain is a digital ledger that records, encrypts, and time-stamps records into an inter-locked chain that makes it impossible to modify any one record without unraveling the chain.

The global healthcare blockchain market is forecast to top US $3 billion by the end of 2026—an annual growth rate of over 62%. Blockchain has the potential in verifying smart contracts, securing electronic health records, and ensuring the integrity of products in the supply chain, including drugs and medical supplies. Blockchain might help hospitals avoid counterfeit products, such as occurred with some shipments of N95 masks.

Growth in big data and analytics

Hospitals manage tons of data, from supply inventories and insurance data to patient medical charts, research, doctor’s notes, and diagnostic images. Hospitals produce 50 petabytes of data per year, according to the World Economic Forum.

This volume of data includes operational and patient data. The amount of healthcare-related data is constantly increasing. This mass of healthcare data is both a blessing and a curse for healthcare organizations.

On the positive side, data generated data from diagnostic equipment, patient monitors, medical images, electronic health records (EHR), and Internet-of-things (IoT) devices help hospitals to improve patient diagnosis and treatment outcomes. For example, leading makers of wrist-worn devices have developed algorithms to detect early signs of infectious diseases, including COVID-19 and the flu. The global Internet of Things (IoT) healthcare market is projected to reach $188.2 billion in 2025.

Analytics and AI-based diagnostic tools enable doctors and scientists to uncover valuable insights from data. For instance, researchers developed an AI algorithm that can mine EHR data for optimal treatment strategies for patients. At the University of Washington, researchers use machine learning  to check a patient’s pulse and heart rate via real-time video feed over a mobile phone. IDC projects that, by 2026, 65% of medical imaging workflows will use AI to detect underlying diseases.

The less positive side to data growth is the cost and time involved in managing huge volumes of data. Cloud-based storage is a cost-effective solution for storing and backing up petabytes of data, and many cloud providers offer data management services. Likewise, cloud-based content management and electronic health records (EHR) applications enable hospitals to store volumes of patient data without the expense of new hardware.

The flow of data between platforms and APIs

The cloud provides a cost-effective platform for sharing data between healthcare providers. Hospitals, clinics, pharmacies, and insurers can share data and files over a common cloud platform using healthcare interoperability standards and APIs – a capability that isn’t possible with legacy EHR systems. Cloud-based electronic records applications and healthcare exchanges also enable data sharing between disparate cloud healthcare applications.

In addition, many leading cloud platforms–including Microsoft’s Azure for Health and the Google Cloud—have APIs based on healthcare open standards for data exchange. These include the Digital Imaging and Communications in Medicine (DICOM) for diagnostic images and the Fast Healthcare Interoperability Resources (FHIR) for describing data formats and an API for exchanging electronic health records.

Cloud is key to successful digital strategies in healthcare

Facilitating data flow and cost-effective storage and security, the cloud has become the standard platform for modernizing healthcare IT systems.

Are you looking to use cloud services to better deliver health outcomes for your patients? Cloud services providers offer a range of services for data interoperability, data security, cloud migration, and cloud connectivity options to help healthcare companies be more innovative and competitive. Learn more about our healthcare IT solutions for businesses.

Originally published in September 2019, this post was updated on June 15, 2021, to reflect changes in stats and to add more context around trends impacting the healthcare industry.