More than 50 data leaders – from more than 20 different industries – participated in the webinar in which the Data To Value community, promoted by Irion, presented the results of the first phase of field testing. During these months of intense discussion, professionals in different fields tested in their own organizations, spreadsheets in hand, the analysis model dedicated to Project Valuation, which helps quantify the economic benefits of a single Data Governance project.
The goals of the DTV community
The other model proposed in the Data To Value working groups, on the other hand, focuses on Program Evaluation, i.e., the overall impact of a broad and continuous Data Governance presidium. The practical application of these models is crucial to be able to calculate the impact on corporate budgets of data governance activities, as proposed at the start of the work: see the white paper Value Based Data Governance written by Franco Francia, Egle Romagnolli, Elena Testoni, Stefano Zoni and Mauro Tuvo. The long-term goal of the community is to evolve these two models until they become a market standard, across economic sectors, for any company that needs to accompany data management projects with a timely assessment of the economic effects of its activities and delivery with regard to the management of its information assets.
How Data To Value came about
“This model was tested in reality: it did not remain just on paper, but set up from an already completed project and then applied and verified on a real use case. The estimation of the value of the benefits was done by configuring the expected drivers and the result was then confirmed by the CFO, who in that case was also leading the project,” recounted Sara Giannetti, Information Technology Specialist in Credem Group.
“Facing the digital transition and the shift to a data-driven paradigm, many realities today have ongoing initiatives that address information assets,” explains Mauro Tuvo, Principal Advisor Irion and author of ‘Data Governance’ (FrancoAngeli) the book edited by Egle Romagnolli that collects 20 testimonials on data governance activities.
“We felt the time was right to share a model that would help in assessing what returns to expect from these interventions. So we started a sort of ‘roadshow’ between Bologna, Milan, Rome and Turin to illustrate the features of the model. Even Douglas Laney, renowned author of Infonomics, at DIAC, the annual DAMA Italy convention where he was guest speaker, expressed appreciation for the work done.”
Presentations also involved the Big Data Business Analytics Observatory of the Politecnico di Milano, with growing interest from those who deal with data and need practical tools to leverage it in their companies. Until the big event, with more than 150 participants in the room, which took place last September in Reggio Emilia and kicked off the discussion tables.
Data use destinations and risk mitigation
“We tied the ‘use cases’ of the data to their contribution to the company’s income statement,” Tuvo explains. “A certain use case can then contribute value, in terms of increased revenue or cost savings.” In other cases, however, the data assets involved (e.g., tables, data product, business term) will participate in the use case for a portion, along with other business assets: with the model, this partial contribution can also be calculated.
“Data quality issues, lack of ownership, lack of knowledge or disregard for semantics: all of these critical issues can bring down the value of data,” the expert continues. “Each project therefore aims to reduce the likelihood and impacts of these risks, making sure that the reduction in value is mitigated precisely by the data governance interventions we have planned.”
This is why, according to the Data To Value models, it is so important to establish a “mitigation goal”: each Data Management initiative must aim to reduce risks (related to data management) by a certain percentage. In the proposed models there is a formula that links the overall value of the Data management intervention to the sum, for all data uses, of the risk mitigation effect induced by the project itself.
The calculation templates included in the model allow the ROI (in terms of profitability) of the individual data governance project to be estimated with sufficient accuracy. “The value of data, as a productive factor, must become a dimension of management control with which the CFO can monitor the effects of data management on the company’s income statement,” stresses Mario Vellella, principal domain advisor Irion.
First tests on insurance and regulatory reporting
“The possibility of knowing what our data is worth has created amazement and interest. This model will allow us to give more strength in presenting data projects: the biggest difficulty is to get involved, to change mentality. It is a comparison between colleagues, useful to overcome initial fears,” emphasized Luca Fioretti, risk management data quality team leader, recounting the scenario and calculation process employed by Reale Mutua Assicurazioni to put the model to the test on two uses (DDUs) related to regulatory compliance.
The first focused on the Solvency Capital Requirement (SCR), a typology of DDUs framed in the Risk Management Typology (macro area); the second focused on IFRS17, the standards for international financial reporting, framed in Regulatory Reporting. The contribution of the DDU to a Typology is estimated as a percentage (0-100%). The contribution of individual data to the DDU follows a five-level scale, in ascending order: nil, low, shared, critical, exclusive. For example, if we think of portfolio data, we can state both that they are not only used to calculate policies (so their contribution will not be “exclusive”) and that their specific contribution will not be zero; therefore, we will state it on an intermediate level.
From fiscal services to public finance
“This model has been a great communication tool in the company. Having made an estimate of the profitability of the project, linked to the percentage of data ownership, convinced everyone: it was not just an economic evaluation, but a real redefinition of objectives,” recounted Mauro Artico, team leader business intelligence at Servizi CGN. Italy’s leading provider in B2B tax and labor consulting analyzed with the DTV model the macro-processes in which data are involved: the DDUs identified are business support (CRM), management and operational reporting, compliance and business operations.
“The ability to read the data and speak the same language is crucial,” recounted Sabrina Adamo, data governance specialist at Cassa Depositi e Prestiti.The model fairly mirrored the structure of our budget, although at this stage we will thin out the smaller items. To use it fully we will have to revise the rules of engagement for data governance and involve all stakeholders.” CDP’s areas: supervisory reporting, PNRR monitoring (business) and credit monitoring (risk management). Once the model was calibrated at the beginning, simulations began. “We are mature enough to dialogue already with business owners. On management control, on the other hand, it is more complex to interact, partly because of the difficulty in allocating costs.Data To Value is a great opportunity to ask the right questions,” the expert concludes.
