Companies that haven’t started a data strategy journey may be struggling to play catch-up on the fundamentals of data literacy. Depending on the industry, company, and approach, the journey to this solution can begin at various points along the spectrum of data management models.


There’s no right or wrong way to start a data strategy project, but every company should have an ultimate goal toward becoming data literate—understanding the data it creates, collects, transforms, and discards.Data governance becomes a key point in any data strategy.




Establishing why you need data, not how you’re going to govern your data, should be the basis for your journey. Many senior company leaders are in such a hurry to be compliant that they primarily focus their data governance programs to comply with specific regulations (e.g., the U.K. General Data Protection Regulation, Global Reporting Initiative Standards, Generally Accepted Accounting Principles, or International Financial Reporting Standards). Leadership also may want the organization to be recognized as being digitally savvy to demonstrate that its data governance program is propelling the company forward into the digital world.


The process of building a data strategy, creating a data governance program, and implementing various data solutions takes time. Achieving data literacy in your organization depends on both the people in your organization and stakeholders outside your organization. Determine how to provide these parties with the basics early on as you continue to explore a comprehensive solution. Have your employees join you on the journey, training them in the data they use every day as well as the data strategy itself.




For eight years, a Big 4 accounting firm had been using Excel spreadsheets and PDFs to publish more than 3,000 reports monthly to its internal management team around the world, but it still didn’t have 100% participation from its network of member companies to supply the data on a regular daily and monthly basis.


Our team advocating change needed a quick win to show them what was possible. Instead of going through an in-depth review of the user’s requirements, we decided to move forward with the data we already had. We made the decision to get a new solution up and running as quickly as possible and shut down the existing solution as soon as the new solution went live.

  When we put the new solution in the hands of the end user, we immediately started getting feedback. Much of the feedback was positive because users were able to drill down and slice the data any way they wanted, but there was also a lot of negative feedback such as:  
  • Why is this member company missing?
  • Where did this number come from?
  • Where is this client’s latest billing hours?
  • This number isn’t what we are seeing on other reports.

As we researched the questions every month, we found areas that needed to be fixed—not in the solution, but in the way we captured data and ran our business. We were then able to refine the solution, make recommendations for improvements in business processes, and, most importantly, get the remaining member firms to participate. By year two, we had a robust solution in place that was able to provide management with the visibility of data worldwide—something the company never had before.




A very different example involves a large bank that started with the strategy. It’s common knowledge that the financial services industry is heavily regulated. So many regulations exist—Basel IV, PCI reporting, MiFID II, and so on. In 2019, McKinsey and Company issued its Global Banking Annual Review, noting that investor confidence in banks was weakening. Banks wanted to show they were in control, and data lineage became their approach to demonstrate this. Data lineage solutions popped up to help banks map each piece of data by showing where it goes, how it’s used, and its journey to the final report that may go to a regulatory agency.


The bank under discussion had established its data strategy, moved into data governance, and created processes not only to address how it governed data, but also to document every definition of data it needed to meet reporting requirements. The amount of work required to create these data definitions and data lineage was tremendous. It took the bank 14 months to develop the first report.


The end users were still requesting additional reports, and our team continued defining what they needed by asking the question “why?” and getting answers. But the work to create data definitions and data lineage documents superseded everything else. The actual collection of data and creation of standard reports took a back seat to the bank’s focus on internal policies creation and not on getting data into the hands of the end user.




Both companies became data literate, even though they started at different ends of the data model. Both companies were determined to understand their data and make it available to their end users in a digital format. Based on my experiences in both companies, I recommend always opting for a quick win. Don’t get bogged down in the data strategy. Just start working with the data. Develop digital solutions to answer your questions. Once you begin, you will see that the data strategy will naturally flow out of your work, and data governance will be easier to tackle because you’ll understand the data you have and why you need to govern it.


Also remember that in your role as a management accountant, you play an integral role in setting the data strategy and determining how data governance is handled in your company. You’re the person using the data to provide insights to your stakeholders. Get involved early and help determine which data needs to be managed. Become data literate and help your organization develop a data strategy that delivers successful results and insights that benefit your company and your stakeholders.


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