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What is Data Modernization? 

Data allows organizations to effectively operate, plan for the future, and mitigate problems. Following the shift to hybrid work and an increasingly globalized world, updating and investing into data systems should be an ongoing priority for every firm. 

What is Data Modernization?

Data modernization is the process of updating outdated databases to modern, cloud-based systems. Below, we outline some of the key considerations that firms should have before undertaking a data modernization effort.  

Data Modernization in Depth

A legacy database is one that has been in use for several years. It may not be fully efficient or compatible for modern tasks, and it may store data in siloed or unsecure locations. While some legacy databases are adequate for a business’s current operations, many need to be replaced. 

A database can be indicative of needing an upgrade for many reasons. Primarily, if the system is outdated to the point where its data is at risk of being lost or corrupted, then the company should make its replacement a priority. A firm may also want to modernize the system if the data quality is noticeably poor, the old infrastructure is no longer serviced by any third-party vendors, or the architecture is simply inefficient for a firm’s evolving tasks. Ultimately, it is at the firm’s discretion whether they should invest time and resources into a new database. 

As the business world increasingly embraces hybrid and remote work environments, access to cloud-based data is crucial. Aside from better reliability, security, speed, and efficiency, this globalized access to cloud data can be pivotal for task and team alignment. For example, if a firm has their manufacturing plants in Germany but their corporate headquarters in the U.S., the teams can easily become siloed and ultimately inefficient. Upgrading legacy databases can allow disjointed teams to have real-time access to higher quality and larger volumes of data.  

Data-First Strategy

A data-first strategy is when a firm places data as the fundamental base of their business strategy. It includes determining the best channels for collecting data, storing, and gaining access at a moment’s notice. 

Firms that employ data-first strategies understand that data is a crucial element to maximizing revenue, efficiency, and growth. Thus, these firms must make continuous efforts to upgrade physical legacy systems and their strategy to flex and expand with evolving business processes.  

Here are some of the most important considerations for creating a data-first business strategy: 

Define clear goals: It is important to begin a data-first strategy with the end in mind. This means developing a set of general goals for both the overarching business and for any data teams. For example, a business may aim to retain a portion of their budget every five years to invest in a new database. Another might be that hiring managers are able to fill all needed data analytics roles proactively. Ultimately, these overarching goals need not be quantitative, but rather a general plan to guide data strategy. 

Set achievable KPIs: Defining the right key performance indicators is crucial to understanding where a business stands in their strategy. These should be quantitative and include specific timeframes. For example, a business may target 10% revenue growth in the next year. Another may be a specified increase in forecast reports that are able to be generated from raw data. Regardless of what the KPIs are, they should be given to a specific team or leader to manage and keep on track. 

Flexibility: Building flexibility into a strategy will help ensure its success and longevity. This means implementing safety measures for any possible problems, creating wiggle room in KPIs and budgets, and having back up strategies for different scenarios. Unanticipated challenges will certainly arise, and firms need to be ready to deal with them. 


With any change in strategy or business resource, there are bound to be some difficulties. When a legacy database has been in place for an extended period, one of the most common issues that arise is discontent post-modernization. Employees that have grown used to outdated technology may not be content with a new system that they need to relearn. It may be necessary to partner with a leading technology implementation firm to make the transition process as smooth as possible. 

Up-front costs from large capital investments can also be a major deterrent. Firms should instead regularly set aside funds and create long-term budgets for anticipated data modernization. This is important to avoid draining emergency funds if legacy systems are left in place until they die without warning.  

Finally, another issue that may arise is a lack of talent. IT talent shortages are an ongoing issue that leaves many firms short staffed. It may be important to hire candidates before an implementation is conducted so that needed roles are already filled.  

Moving Forward with Data Modernization

Ultimately, proactively upgrading a legacy database, rather than being forced into it, is the best plan. Staying ahead of the curve with databases can bring competitive advantages that allow for business goals to be met more easily. It’s up to each firm to take the initiative and decide when and how they would want to invest in their data strategies. If your business needs assistance with this, our team of data management experts can help.  

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Contributions from Jake Park-Walters

Tags: Data & Analytics, Data Strategy, Data Management