In many organizations, your analysts, data scientists, and data engineers are struggling to give business leaders answers to the burning questions they have around data. The reason? They’re having to figure out how to navigate a situation known as “data sprawl.” 

Overcoming Data Sprawl

“Data sprawl” occurs when data is siloed across multiple locations, and the integration of technologies that connect data sources increase in complexity as they are spread across systems that are built over decades.

A real example of data sprawl that we’ve encountered was for a 100+ year-old large enterprise organization: the source system data was from a 1980s mainframe technology (incredibly stable), loading into 1990s data warehouse technology, and the goal was to move to the cloud. Imagine having to go backwards to find data sources and connect everything, not even knowing if or where documentation exists (it usually doesn’t). 

You want your users to have as close to a one-stop-shop as possible for data needs. Getting a one-stop-shop for self-service analytics is something the entire industry has been dangling in front of organizations for years and yet has been incredibly hard to achieve. 

When you hear vendors promise “out-of-the-box” and “limited config,” we find that implementation and success of these systems fail to live up to expectations. Each individual client has a unique data landscape that needs to be accounted. The challenge is made far worse by the rapidly increasing levels of complexity in technology systems and a limited talent pool, where often inexperienced data engineers are trying their best to integrate systems cross-functionally. 

Investing in Data Engineering Tools 

At Clarkston Consulting, we take a tailored approach to find how to best leverage the right tools given your company’s and team’s individual levels of expertise and problems. We have found that collaborative data engineering tools, such as Databricks or Snowflake (two big players in the space with growing talent resource pools), are excellent choices to bring your analysts, data scientists, and data engineers together on a singular platform that is growing rapidly. What that means is that it’s easier for you to find the talent you need, and that the investment in an easy-to-use unifying platform for all your data can continue to deliver ROI as you continually evolve and grow.

“Delta Tables” are a key feature of a tool like Databricks. Delta tables enable you to decouple your storage from your compute costs, which is a more efficient way to manage resources. Additionally, we’ve found clients who connect dashboards to underlying delta tables now have a centralized place for their data sources, cleaning up the data sprawl.

Data Sprawl: Clarkston Can Help 

Transitioning your team from old paradigms to a new one is a shift for your organization, and typically data teams have full plates and fires to put out. Overcoming data sprawl and moving to a new system in a strategic way requires training, learning new languages, and new processes. At Clarkston Consulting, we take pride in helping your team transition to emerging platforms and fully unlocking the power of cloud-provider tools through mentorship, training, and best practices – components often overlooked and underutilized, yet critical in setting your team up for success.

Contributions from Matt McMichael