"I don't have any data quality problems. "We manage our data via an ERP system, so it's good quality. ". Who hasn't heard that? But things have changed in recent years, with the arrival of cloud computing and, above all, big data. Today, data quality is a key issue, particularly in the banking and insurance sector, where Solvency II regulations require data quality management. Éric Gacia, Consulting Director for Banque Assurance at Micropole, looks at recent changes in this area.
According to a 2011 PricewaterhouseCoopers (PwC) survey, "90% of companies believe it is essential to have a data quality strategy, but only 15% actually address it." Why? Companies often do not have internal sponsors to manage this critical aspect. The goal, however, is crucial: to know the data, define an internal data dictionary and a common vocabulary for the company to share a consistent vision of the data.
A regulatory requirement
Solvency II places the emphasis on data quality and data governance through very concrete regulatory requirements. This has become a real business issue in the banking and insurance sector, whereas just two or three years ago, the profitability and relevance of these projects had not been proven. "At one of my customers, a major group in the banking and insurance sector, everyone is now on board with this initiative, because no one has any indicators of data quality, which makes it difficult to use the data and leads to discrepancies, sometimes significant, between the various reports produced. Another, smaller organization told me that it carries out its marketing campaigns "blindly", due to its lack of confidence in the data used." Management has become very sensitive to this issue. There's a real maturity about it, especially as ROI is rapidly being achieved. The key is to gain confidence in the data used, to know it so as to exploit it more effectively, to save time and to devote oneself to higher value-added tasks linked to one's core business.
What governance for what data?
Governance covers all the rules and procedures to be followed. It is therefore necessary to set up a data quality steering committee involving the many players concerned: general management, the IT department, the risk management and management control departments, etc. The aim is then to set up an operational working group which will analyze quality indicators, propose and monitor action plans aimed at improving quality, and proactively analyze the impact of data-related developments (new products, new management applications, etc.). Data quality management has led to the "creation" of a new position within companies: that of Data Manager. A true conductor of the orchestra, the Data Manager will monitor the indicators in place or to be created, the integrity of repositories, as well as steering improvement actions to measure their effectiveness.
If all the company's data is concerned, external data is more difficult to control. In order to integrate this data, it will be necessary to implement a data quality firewall that constitutes a data quality lock (compliance with quality standards, management of rejections and implementation of manual correction actions, etc.) before it is integrated into internal operational applications. The data stewardship portal can also be made available to data provider partners in order to make them accountable and provide them with all the tools they need to guarantee the quality of their data.
The need to involve business managers
A number of internal sponsors are essential to the success of a data quality governance project. All departments are involved: general management, finance, marketing, risk management, internal control, accounting, etc. Very often, each department manages its own data, which is neither shared nor centralized, resulting in significant discrepancies in the figures. A cross-functional understanding of the approach enables the creation of a reliable data repository shared by all.
