Overcoming Data Modernization Challenges

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In our previous blog, we discussed some of the biggest challenges that you face in modernizing your data. Data complexity, the constantly changing landscape in terms of cloud-based data solutions, and advanced/proprietary code in legacy systems as well as cost can all lead to lost resources, time, and money.

Fortunately, these challenges can be addressed. This article discusses solutions and best practices for meeting data modernization obstacles.

Be Adaptive

Understand that data modernization is a moving target. Design your modern data solutions to adapt to the real-time needs of your organization. Understand the reality of an accelerating infrastructure, and design your data with movement in mind from the beginning. This means your ETL (Extract, Transform, Load) should be designed with reuse and transparency in mind. Documentation should be made available across your IT infrastructure so that knowledge transfer is not a burden when your team moves on.

Understand Your Data and Existing Platforms

You already have powerful existing business logic and tools at your disposal. Use these to your advantage when designing your data modernization project in order to minimize data movement and transformation. Centrally manage your data so that you are able to make changes efficiently and reliably.

Designing your data modernization project gives you the opportunity to more richly understand your data as a whole. As you stage your transformations, you will uncover whatever metadata you don’t currently understand. Documenting and adapting to this knowledge will give you complete control over your data and further prepare you for changes that will need to be made in the future.

Reduce Complexity

Avoid utilizing server complexity with point-to-point data movement and centralized repositories. It is tempting to utilize complex solutions (especially in the cloud) that lead to costly and poorly documented processes. The reality for most projects is that it often makes more sense to process and transform data natively rather than in staging servers. This approach gives you more complete oversight over your infrastructure as your environment becomes more complex over time. It will also allow for hybrid environments to be implemented more easily as your data sources become more diverse.

Manage Your Data Centrally

A danger of many data architectures is allowing for complex and multiple data ecosystems to inform integrations. Doing this creates more noise than can be handled and often leads to loss of data and/or the inability to replicate the work you will do to modernize your data. Design your data integration separate from legacy processing systems and with existing business logic in mind.

Approach Modernization Systematically

It is easy to approach data modernization as a onesize-fits-all solution. While every data modernization project is different, the reality for many organizations is that it can often be done in pieces. Assess your current methodologies, systems and long-term goals with key stakeholders before diving in to your project. Use that data to produce a systematic roadmap that clearly lays out your modernization objectives and timelines, with the most critical data at the top. Attack those project goals one at a time to arrive at a solution that is transparent, reusable, and well-documented for your changing landscape.

Data modernization will open the door for your enterprise to leverage more modern technologies, access data in real time, and potentially save you time, money and resources. Keeping these items in mind will keep your modernization project from becoming counter-intuitive and a drag on your organization.

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Christopher Shoe is a Fixate IO Contributor and has built a career on aggressive growth and innovation. Over the past 6 years he has grown new business initiatives within international corporations and startups, always with a focus on data-driven decision-making and technological innovation.


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