A Ninety-Day Plan to Build a Data and Digital Strategy – Part Two: Data Assets
By Marc Schuuring, Lucas Quarta, Aziz Sawadogo, and Canberk Koral
Many companies suffer slow, costly data and digital transformations that delay or even prevent their businesses from achieving their goals and capturing value. The reason is that very often, a company’s business and IT sides each pursue their own data and digital transformation in parallel, rather than coordinate the planning and sequencing of their initiatives.
To remedy the disconnect, a company should have both sides take a step back from their transformation initiatives and jointly define a North Star. This exercise entails examining the organization’s broader digital ambitions as well as identifying what we call the data trinity: the data use cases that are important to the business, the data assets that power the use cases, and the data and technology architecture that makes the data assets accessible. The North Star exercise greatly accelerates a data and digital transformation, letting a company realize its business goals sooner.
In a series of three blog posts, we will discuss each of these data trinity elements. In this post, we’ll explore data assets.
Data and digital transformations must have the very best data. Yet many companies still have not identified the data that gives them a sustained competitive advantage. That lack of data maturity is hindering their data strategies and business outcomes.
When defining its North Star, a company must identify the specific data assets that it needs to fuel its use cases. In the case of a clothing e-tailer we worked with, the company began this exercise with a generic list of retail data assets and modified it, eliminating those data assets that were not relevant to clothing e-tailers (such as manufacturing data). The result was a list of data assets that all clothing e-tailers need.
With this list in hand the e-tailer began to map where its data assets reside within the company, organized the data assets into data domains that specified ownership of the data assets to avoid any gaps or overlaps, and assessed the quality of those assets. Then it could determine how to add analytic functions, integrate data sets, and adapt the corporate culture to use data optimally. This approach to data assets differs from the typical IT environment, requiring senior leaders to make several mental flips.
The company’s identified owners were then responsible for these different data assets and improving their quality to implement specific use cases. For example, to fuel its personalization use case, the company needed to know where to find customers’ profile data, as well as the company’s order data, stock data, and product and pricing data—and determine the data’s quality.
Then—and this is critical—the e-tailer sequenced the rollout of use cases so that, to the extent possible, the first wave leveraged and improved data assets that would benefit subsequent use cases, accelerating their deployment and the business impact.
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By using a North Star to align on specific use cases, data projects, and architecture modernization initiatives from day one, the company can ensure it doesn’t move in the wrong direction, ultimately accelerating and improving business outcomes.
Read Part 1 to learn more about data use cases, and stay tuned for Part 3, where we’ll discuss how data and technology architecture fits into the North Star exercise.
About the Authors
Marc Schuuring is a managing director and senior partner in the Amsterdam office of Boston Consulting Group. You may contact him by email at schuuring.marc@bcg.com.
Lucas Quarta is a partner and director in the firm’s Paris office. You may contact him by email at quarta.lucas@bcg.com.
Aziz Sawadogo is a knowledge expert in BCG’s Paris office. You may contact him by email at sawadogo.aziz@bcg.com.
Canberk Koral is data and digital platforms program manager in the firm’s Istanbul office. You may contact him by email at koral.canberk@bcg.com.