Since the start of the COVID-19 pandemic, companies have had to accelerate their digital transformation. This implies increased investments, so substantial that they require C-level support. The stakes are high for organizations. From accelerating sales to optimizing operational processes, digital impacts the value chain in every aspect. If the digital revolution generates an inevitable modernization of companies and a hope of value generation, it also provokes a major challenge for organizations: Data.
Data from transactions, customers, products, etc. invades the daily operations of organizations, constituting a potentially valuable asset, but above all an important challenge in terms of governance and management. Organizations must increase the understanding of these data as part of their transformation.
In the very short term and in an uncertain time, data becomes more crucial than ever to identify the levers of performance of companies. Optimizing costs, increasing business revenues, and driving process efficiency are all initiatives based on the availability of relevant data. As the decision cycles accelerate, many decision-makers will no longer be able to drive their businesses with approximate and often inaccurate data. Having good data - and just in time - has become a pressing necessity. But this prospect seems attainable only if the data heritage is better mastered. This is precisely the purpose of the "Data Footprint" method designed by Kearney and Essec. Evaluating the data footprint now constitutes an essential approach to secure investments and increase control over data assets.
The Data Footprint approach introduces a virtuous practice that aims to understand the data heritage, risks, challenges and limits linked to data within organizations. The Data Footprint is an evaluation process based on a 360° analysis of the data required as part of a company initiative steered by the entity in charge of Data Governance.
The aim of the Data Footprint is to assess the data assets to establish a risk assessment score. Based on multiple dimensions of analysis such as data quality or security, our method allows a quantified assessment of the data heritage in an organization. Today, the data heritage is still poorly controlled and exploited in many companies. What is the quality level of critical data sets in the organization (e.g customers/suppliers’ data)? What is the level of risk associated? What is the degree of control and ownership of data in the organization? These questions are often asked by decision makers without concrete answers based on a structured assessment. The complexity of information systems combined with the lack of governance make the data equation often complex and costly.
The Data Footprint allows companies to get a tangible data assessment across multiple dimensions in order to establish a risk score. The purpose of such a measure is to be able to accurately assess areas of weakness and to monitor data heritage improvements. The approach also allows internal and external benchmarks based on a standardized analysis grid.
The strategy for implementing a Data Footprint should be progressive while focusing on the critical data sets in the context of companies’ major programs, projects or business transformation initiatives.
The approach should involve several collaborators, at least representatives of business lines and IT, who jointly use a score sheet based on the following five dimensions:accessibility and availability,quality, ownership,risks, and identification of the future users. The overall score calculated on these five dimensions can range between 0 and 15, the lower the score the higher the risk related to the enterprise initiative.
Consider as an example a company specializing in the distribution of electronic equipment to the general public through its distribution network of more than 2,000 stores. As part of its data strategy, the company decides to launch a priority project that deploys a “Customer-centric” approach in order to increase customer value. The objective is to capture a better understanding of customer preferences in order to meet their expectations. The company anticipates a significant potential risk linked to data (availability, quality, etc.) and decides to launch a Data footprint approach.
The total Data risk score for this company was less than 5 in the evaluation exercise. On the recommendation of the Chief Data Officer in agreement with the rest of the team, the decision to launch the project is postponed pending the implementation of a specific data related action plan. This approach allowed the company to apprehend a major risk related to data on this project. Indeed, a rapid launch of this project without prior assessment would have potentially led to failure with economic consequences (losses estimated at a few hundred thousand euros). The approach also made it possible to initiate collaborative work around the data over the entire duration of this assessment (one month), and thus avoiding internal misunderstandings about the responsibilities of the various stakeholders (Business lines, IT teams, etc.). Finally, a clear action plan could be drawn justifying the investment of technical and human resources to upgrade the information system.
For a more technical version of this article or further details on the Data Footprint, please contact:
Reda Gomery, Vice President, A.T. Kearney, Reda.Gomery@kearney.com
Jeroen Rombouts, Professor, Essec Business School, firstname.lastname@example.org