We find most businesses are eager to dig into their data. The possibility of solving persistent problems, revealing strategic insights, and reaching a better future state has mass appeal. However, when those same businesses insist on pursuing the latest and greatest technology without a game plan, I pump the brakes. Analytics without an enterprise data strategy is a lot like the Griswold’s journey in National Lampoon’s Vacation. They ended up at their destination…but at what cost? More than just gaining a better streamlined process, here is what your business can expect from putting the time into defining your enterprise data strategy.

What You Gain from a Well-Defined Enterprise Data Strategy

Verification of Your Current State

Data is messier than most businesses imagine. Data practices, storage, and quality without a robust governance strategy often morph data beyond recognition. That’s why we recommend that any business interested in enterprise analytics first audit their current state – a key component of an effective enterprise data strategy. 

Take one of our former enterprise clients as an example. Prior to our data strategy and architecture assessment, they believed there were no issues with their reporting environment. The assumption was that all of their end users could access the reporting dashboards and that there were negligible data quality issues. Our assessment uncovered a very different scenario.

Right off the bat, we found that many of their reports were still conducted manually. Plenty of business users were waiting on reporting requests. There were both data quality and performance issues with the reporting environment and their transactional system. With that reality brought to the forefront, we were able to effectively address those issues and bring the client to an improved future state.

Even in less drastic instances, conducting the assessment phase of an enterprise data strategy enables a business to verify that assumptions about their data architecture and ecosystem are based in fact. In the long run, this prevents expensive mistakes and maximizes the future potential of your data.

Greater Need Fulfillment

An ad hoc approach to data restricts your return on investment. Without an underlying strategy, data storage architecture or an analytics solution is at best reactive, addressing an immediate concern without consideration for the big picture. Enterprise-wide needs go unfulfilled and the shelf life of any data solution lasts about as long as a halved avocado left out overnight.

A firm enterprise data strategy can avoid all these issues. For starters, there is an emphasis on holistic needs assessment across the enterprise. Interviews conducted within management-level stakeholders and a wide array of end users help to gain a panoramic view of the pain points and opportunities that data can help solve. This leads to fewer organizational blind spots and a greater understanding of real-world scenarios.

How do you gain an enterprise-wide perspective? Asking the following questions in an assessment is a good start:

  •   What data is being measured? What subject areas are important to analyze?
  •   Which data sources are essential parts of your data architecture?
  •   What goals are you trying to achieve? What goals are important to your end users?
  •   Which end users are using the data? Who needs to run reports?
  •   What manual processes exist? Are there opportunities to automate?
  •   Are there data quality issues? Is data compliant with industry regulations?
  •   What are your security concerns? Are there any industry-specific compliance mandates?
  •   Which technologies are you using in-house? Which technologies do you want to use?

This is only the start of the process. Our own data strategy and architecture assessment goes in-depth with the full range of stakeholders to deliver data solutions that achieve the greatest ROI.

A Clearer Roadmap for the Future

How do your data projects move from point A to point B? The biggest advantage of a data strategy is providing your organization with a roadmap to achieve your goals. This planning phase outlines how your team will get new data architecture or analytics initiatives off the ground.

For example, one of our clients in the logistics space wanted to improve their enterprise-wide analytics. We analyzed their current data ingestion tool, SQL Server data warehouse, and separate data sources. The client knew their new solution would need to pull from a total of nine data sources ranging from relational databases on SQL Server and DB2 to API sources. However, they didn’t know how to bridge the gap between their vision and a real outcome for their business.

We conducted a gap analysis to determine what steps existed between their current and future state. We incorporated findings from our stakeholder assessments. Then, we were able to build out a roadmap for a cloud-based data warehouse that will offer reports for executives and customers alike, in addition to providing access to advanced analytics. Our roadmap provided them with timelines, technologies needed, incremental project milestones, and workforce requirements to facilitate a streamlined process.

With a similar roadmap at your disposal, you will start your organization on the right path to building out an effective data and analytics solution. 

Jason Maas