The four stages of data modernization

The four stages of data modernization

Data represent one of the most valuable assets in any given organization’s arsenal. While this is a generally accepted reality of doing business today, the pace of innovation using data can differ significantly from organization to organization. Organizations with sprawling on-premises data environments or leveraging legacy technologies, for example, might be discovering that their pace of innovation is slowing. In contrast, cloud-native organizations can rapidly unlock and take advantage of new use cases through the more straightforward implementation of modern analytics and AI.

Organizations with sizeable on-premises data environments likely have initiatives to modernize their data and leverage AI to solve business challenges but may find the path from on-premises to AI daunting. This blog is intended to help organizations identify the stages of data modernization and clarify the direction forward.

stages for data modernizationStage one: Migration

The first stage of most data transformation projects is data migration. While this may seem like an obvious first step, there can be a tendency to overcomplicate or prematurely transform data before migration rather than perform a lift and shift migration.

While preparing data for use in the cloud before migration may seem like a good way to shorten the timeline for implementing modern analytics and AI, it can result in complications if not properly prepared or formatted. Additionally, lift and shifts typically enable quicker migrations, allowing organizations to retire legacy hardware sooner.

Stage two: Data & application modernization

Once the data is in the cloud, the next stage is to modernize the data and applications. Modernizing data and applications in the cloud can enable wide-ranging capabilities that may have been difficult to achieve on-premises. Examples could include real-time digital collaboration on content creation, more accessible data sharing, simpler and more informative BI dashboarding, and the ability to leverage DevOps to increase time to value for development projects.

Modernizing data and applications also enables organizations to move to the third stage: implementing modern analytics.

Stage three: Implement modern analytics

One of the most impactful benefits of data modernization is gaining the ability to glean more meaningful insights from your data. Modern analytics can help organizations learn more about their customers, identify previously undetected trends or behaviors, and help organizations make more informed business decisions

Connecting multiple disparate data sources to cloud-based analytics is typically more straightforward than connecting on-prem databases to similar solutions. Cloud-based data pipelines generally are simpler to build and do not need to account for issues like data gravity or situations where on-premises databases may not be constantly accessible.

Stage four: Innovate with AI and Machine Learning

The final stage of data modernization is to innovate with AI and Machine Learning (AI/ML). AI/ML solutions can help organizations solve a wide range of business challenges.

Organizations have used AI/ML in several use cases over recent years. An example includes helping retail organizations optimize supply chains and provide more precise demand forecasting by leveraging analytics data from disparate stores. Manufacturing organizations have used AI/ML solutions to help reduce waste by leveraging IoT data to predict wear and out-of-spec production. Organizations seeking to learn more about their customers have also used AI and machine learning to build customer profiles, predict customer behavior, and make marketing recommendations based on those profiles and predictions.

Specific AI/ML implementations can be industry or even organization-specific, but they have immense innovation potential. In fact, many organizations implementing AI/ML solutions find that they never stop innovating due to its potential.

How do I actually get from stage one to stage four?

Data modernization can still appear to be a daunting, time-consuming process even when broken down into four distinct stages. Done alone, it can take a great deal of time and resources, let alone the time required to hire the right skill set to execute the process from end-to-end.

The path to data modernization can be further complicated when leveraging resources that may help with one stage but not another. A lack of continuity in data modernization projects is one of the most considerable risks organizations face when pursuing data modernization. The risk mainly stems from the fact that new resources are unlikely to be familiar with the data or what has already been completed and may want to deviate from the previously established plan. Even with everything well documented, it still takes time to get new resources up to speed.

The best way to tackle this challenge is to partner with a consulting firm that can help navigate all four stages of data modernization from end to end, including planning and executing the migration, modernizing data and apps, implementing modern analytics, and driving AI innovation.

Neal Analytics is uniquely suited to support data modernization projects and ensure a rapid time to value without cutting corners. As an expert in data and AI with an Advanced Specialization in App Modernization from Microsoft, Neal is perfectly suited to help organizations navigate these four stages.

If you are interested in learning more about the path to data modernization or how Neal can help, contact us.

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