Osservatorio Big Data & Business Analytics: il mercato dei dati accelera (+20%). Irion e CREDEM presentano Value Based Data Governance

The data market beats the crisis and accelerates. The expenditure of Italian companies in infrastructure resources and data management and analytics software and services in 2022 records a boom of 20% compared to the previous year and reaches 2.4 billion euros. This is the highest growth rate since 2019. Moreover, it confirms the positive trend already underway in 2021, after a year with the market almost at a standstill due to the pandemic. Such is the picture that emerges from the Big Data & Analytics Observatory study presented at the Data-driven culture convention at the Politecnico di Milano. In particular, the most substantial acquisition is that of Data Management and Analytics software, with 54% of total expenditure and +25% over 2021. The growth is most pronounced in the areas of GDO and Retail, followed by Public Administration and Health. However, only 15% of large Italian companies are at an advanced level of maturity, according to the new Data Strategy Index developed by the Observatory. 30% are labelled as “proactive”, 22% as “watchful”, 18% “immature”, and 15% “at the first steps”. The index assesses three areas: Data Management & Architecture (technological management and governance of data assets), Business Intelligence and Descriptive Analytics (tools and skills for pervasive business intelligence), and Data Science (data analysis).

Value-Based Data Governance paper presented at Politecnico di Milano

At the event, Irion Principal Advisor Mauro Tuvo together with CREDEM Information Governor Elena Testoni presented the paper Value-Based Data Governance, co-authored by CREDEM Chief Data & Analytics Officer Stefano Zoni, DAMA Italy Vice-President Franco Francia, and Irion Head of Marketing Egle Romagnolli). It suggests an original and field-tested system to calculate, with the facts and figures, the economic value of Data Governance activities. Carlo Vercellis, ordinary professor of Machine Learning and head of Observatory, highlighted that “whoever works with data knows that a substantial chunk of time is devoted to data quality.” In his turn, Mauro Tuvo noted that “in banking and insurance, Data Governance emerged from data quality initiatives; it was a Trojan horse.” Why this emphasis on the economic value of data? “It contributes to developing data culture, especially among those who allocate corporate budgets. It’s not important to calculate the value of the data itself but to consider it at two different levels, the entire Data Governance program and individual action,” – Tuvo sums up. To correctly set up the model, it is necessary to understand the data’s intended use. For example, is this data needed for regulatory reporting? To consider productive processes? To assess risks? In the end, the leverage is to determine how the various types of data use affect the company’s income statement. The model considers the possible risks that could lower the final value of the data (with respect to the initial theoretical value) as well as the the project capacity and the Data Governance program to reduce these risks.