By Jeroen Rombouts, Professor of Statistics and Econometrics at ESSEC Business School and Chair of the Accenture Strategic Business Analytics, and Gérard Guinamand, CEO of DATAtScale, former Chief Data Officer, ENGIE and Senior Advisor at Data Leaders.
In recent years, companies have become increasingly aware of the potential of technological advances such as connected objects, and analytics platforms, which now function in real-time for decision-making. In addition, companies are open to ecosystems of partners including suppliers, customers, and even competitors.
Given our experience as Chief Data Officers and the handling of many use cases over the last five years, we identify what makes data initiatives successful.
A common element of these advances is the role of data: generated and used at lightning speed, this data can now be sold or shared between companies. Not just that: data analysis is a huge value add for these companies. Given the significant investments involved, companies are now increasingly interested in highlighting the value generated by these data and analytics efforts.
CEOs and company boards are now convinced that this data adds significant value to their organization. In addition to its use for managing KPIs, senior management understands that data can be a source of revenue thanks to new products and services. Data can also be an accelerator of operational excellence by offering new performance levers: predictive maintenance of industrial assets and process automation. In addition, CEOs also see data as an opportunity to reduce commercial, industrial, and regulatory risk.
The Value of Data is at the Heart of the Data-driven Company
A common (mis)conception is that a data-driven company frequently relies on the data it possesses to make its strategic, operational, and financial decisions. This perception is outdated and limits the use of data to the decision-making domain and business intelligence.
In reality, a data-driven company considers data as a real asset of the company, in the same way as their industrial assets or customer roster. An asset is built, managed, maintained, and measured to grow and enhance so that it can generate operational and financial value for the company. To capitalize on their data, companies are expressing a growing desire to become data-driven, which involves converting data into an asset and using it for operations management, product development, financial value creation, and marketing tools. The Chief Data Officers (CDOs) are then entrusted by CEOs and boards to handle diverse strategic, operational, and business-related inquiries.
Finding a way to concretely demonstrate the value produced by the data for the company remains the most important and the most difficult task for CDOs. The CDO must find the right tools and the right KPIs to demonstrate progress. We distinguish the following three actions:
First, identify the data’s value, which forms the basis for creating value through the use of technology and establishing a culture of sharing. The CDO needs to set up KPIs to measure progress: the volume of data stored in the data lake, the volume of shared data, and the volume of data actually used by use cases..
Second, exploit the value of the data: simply measuring the number of use cases is not enough. The CDO must set up KPIs relating to the monetization of the data, the business value produced by the use cases: decrease in churn, reduced plant operating costs, lower equipment maintenance costs.
Third, measure the value of data through the ROI of data investments. We elaborate next on this point.
The ROI of Data Investments
The CDO will have to implement three complementary approaches:
- Show the positive impact of the data-driven company program on the company’s EBITDA (Earnings Before Interest, Taxes, Depreciation and Amortization)In the illustration below, the CDO has identified a set of 50 data use cases that can be developed over three years. These 50 use cases include 30 cases relating to the company’s customer activities and 20 use cases relating to the company’s industrial assets. The impact on EBITDA of these 50 use cases is estimated at €600-700m over the three-year period: benchmarked with public sources from major strategic consulting firms in order to show that it is within the range of these cabinets. The calculation is carried out for each use case by using industry standards for performance drivers and KPIs.
2. Define the overall ROI of the program
In the visual illustration below, the CDO has established a global business case to obtain the funding required for developing seven use cases of the company’s value chain.
There are three steps to undertake:
First, identify the company’s value chain to formulate the value opportunities provided by the data that is already available or can be acquired. Value opportunities can include better allocating investments by focusing on highly productive sites or plants and facilitating improved decision-making supported by complete visibility of activities.
Second, for each value chain stage, identify the use cases with the most business value and the most readily available data (in terms of data access, data quality and skills).
Third, identify the transversal costs related to the technological foundations necessary for the overall portfolio of use cases: costs of maintaining and developing the platform, costs of developing the use cases, and costs of data acquisition, storage, and analysis.
3. Propose a business case calculation method for each developed use case.
To obtain a business case, it is important to keep in mind straightforward rules such as:
- earnings must be compared to a baseline without the expected asset
- benefits must be compared to all costs (project and running costs)
- consider gain progression over years (for example a 3-year vision).
Putting It All Together
Overall, from our experience we conclude that a single standard for measuring data value is not available. Indeed, in most industries it is highly complex to assess the value of data alone in a project. In this article, we summarize a minimal set of easy to implement practices that allows organizations to undertake further steps towards becoming data-driven.
This article was originally published by ESSEC Business School.
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