Data Governance

What is Data Governance

Data governance designates a set of procedures enabling a business to obtain high-quality data. Such process includes people, methodologies and IT technologies united by the common goal of achieving a correct and effective data management.

Data governance focuses on the following key elements: consistency, integrity, security and availability of the data ensuring a reliable basis for the entire organisation, as well as a stable support to develop efficient decision-making processes.

Data governance means also to determine precise and standardised rules which allow, through automation, the reduction of corporate risks linked to incorrect data and subsequent erroneous interpretations.

 

Data Governance framework in Banking

The urge towards legislation and the regulatory compliance theme have become two extremely relevant topics in the banking sector. MiFID II, PSD2 and GDPR are just some examples of the increasing strictness of the Authorities in relation to transparency and security.

This process involves Italian and foreign banking institutions alike, with the aim of building a common definition scope for the inputs to the system. The generation of new rules required the sector to seek dedicated IT solutions to manage increasingly complex processes, financial resources to invest in digital transformation and human capital to assign to project responsibilities.

Several credit institutions have established actual data governance functions designed for the management, monitoring and checking of data, from collection to delivery.

The banking system has recognised the deficiencies of traditional system, such as the origination of parallel data management and duplication processes or the dispersion due to the collection of data by different business units within the same organisation. Mergers and incorporations between institutes have often led to some management confusion, causing system overlaps and stratifications.

 

Why is Data Governance so important for the banking sector: the advantages

The establishment of a correct functional process in data management is essential to comply with regulations, handle risks and generate value for the bank.

An accurate approach to data is fundamental to achieve business objectives:

• Data integrity and regulatory compliance

The necessity to meet compliance needs has led the banking sector to conform and take better account of the data governance thematic.

 

All industry regulations such as MiFID II, which aims to ensure higher transparency of financial intermediaries’ activities and actions; PSD2, causing credit institutions to share financial records with the outside in a protected and secure manner; and GDPR, supplementing the rules on personal and sensitive data protection, are centred on the integrity and reliability of the data base: two essential qualities to avoid breaches and penalties.

 

• Single customer view allowing a proper marketing strategy

An appropriate data management leads to the creation of value for the bank. Reliable, disposable and readily available data enable the responsible functions to identify the most suitable actions for customer care and management, as well as the best marketing strategies.

To harness the information assets and generate competitive advantages towards competitors, a high-quality structured framework should be established.

 

• Building a strong relationship with customers and enhancing the ‘brand reputation’

The use of correct data increases banks’ credibility and capacity to be active in the relationship with customers, with particular regard to consulting services using the data base to meet the financial needs of that relationship.

 

• Optimising operating time

Clear and transparent data significantly improve the time needed for each individual operation. On the contrary, incongruent or inconsistent data entail a wasting of time and resources. Erroneous data management thus becomes a major operating cost, especially for large entities.

Accordingly, credit institutions need to adapt to achieve the advantages offered by data governance and avoid potentially unsustainable business risks.

Support comes from the above-described decision-making process, with human capital and particularly technology as fundamental basis to trigger change.

For efficiency purposes, data governance is ever more often supported by cloud and new platforms which are able to meet the needs of a banking system increasingly oriented towards digital transformation.

Save Consulting cloud-based solution

MidaBI: this quick, powerful and intuitive business intelligence module allows the management of regulatory reporting data base, as well as an in-depth cross-check of data quality (i.e. an actual data governance).

The advantages for users:

• Ease of use and emersion of prospective discrepancies among the generated surveys;
• Positive procyclicality in the identification of differences, as well as their dispositions and the achievement of management efficiency goals;
• Wide reporting possibilities, both structured (predefined reports) and free through the production of indicators, value classes and related time mismatch analysis (data mining);
• Powerful what-if engine allowing users to easily simulate value classes, aggregations and/or indicators.

 

Pillar 3: this data governance module enables the production of reporting required by the third Pillar or Basel Pillar, starting from the filing of regulatory reporting.

The Pillar 3 module features undeniable operational advantages (generally aimed at risk management):

• To avoid a destructured recovery of information from different business sources;
• To manage the information set based on predefined and certified logics, using EBA’s Guidelines and European Regulations;
• To generate the quantitative information set in a few simple steps and almost immediate development timeframes;
• To define the control system, also for regulatory compliance and information auditability purposes;
• To possibly have a reporting (information management) where information is navigable backwards (from final data to the starting components).

 

Non-harmonised Reporting: the historical regulatory reporting assets. This module allows the management of non-harmonised supervisory reporting (so called ‘matrices’) through the following steps:

• Base uploading
• Check of formal correctness and guided management (using dedicated drop-down menu) of any potential adjustments
• Regeneration of the base considering the adjustments
• Through the MidaBI module, production of both specific reports (predefined set of reports) and reports referring to more general regulatory data governance, with the possibility to compare non-harmonised data with the harmonised ones.

Like every other TigreArm module, this module is web-based and can be used also for remote working