Data Governance for strategy

Data Governance is a set of processes, roles, policies, standards and metrics aimed at ensuring an effective and efficient use of the information available to the company, which allows an organization to achieve its objectives. It establishes processes and responsibilities that ensure the quality and security of the data used within an organization. Data governance defines which figure can take certain actions, on what data, in what situations, and using what methods and a well-designed Data Governance strategy is critical for any organization working with big data.
In the current market, Data Governance is a practice adopted almost exclusively by banks and insurance companies or in any case by companies that are obliged to activate control mechanisms, for specific regulations or for the centrality of data in the core business, but more and more companies will have to adapt to this standard to really value, also in a commercial perspective, the information assets.

Doing Data Governance means using organizational leverage to ensure that data is managed properly, and being able to shed light on:
● the meaning of each data item;
● responsibilities for Business rather than IT;
● the criteria for defining a given quality;
● ensuring that the whole organization is aligned on these aspects.
Even before defining new ad hoc processes, it is therefore a matter of verifying how data must be managed in existing processes, to equip themselves with a common language and to ensure that data handlers have the right skills and create a real data culture throughout the company.

Introducing Data Governance in the company risks being seen as an expensive process whose benefits are visible in the medium/long term. For this reason, today, more agile approaches are preferred that allow us to approach this path in a gradual way, focusing on the main needs of the organization and structuring the sprint path. But what are the essential steps in this process?
● Identify the main needs of the organization and frame how Data Governance can support them, directly or indirectly. The arrival point must be continuously communicated and supported during the program.
● Form a multidisciplinary working group, involving the different souls of the company. One of the most common mistakes is to think that it is IT independently to lead and implement the initiative
● Starting from the basics: define a business glossary, or a vocabulary with definitions of some of the most relevant data for the business, identifying some key processes that use this data. It will then be easier to move to the organizational level, defining roles and responsibilities about the definition and management of data. IT in this phase plays a fundamental role, both for the implementation of the necessary support tools and for its knowledge at a functional level.
● Measure. Initially define the KPIs that allow to measure from the beginning and for the duration of the program the progress and the obtaining of benefits.

Like any process, the introduction of Data Governance involves a series of activities that are costly in terms of the use of human and economic resources, here is why it is essential to identify areas where this process is a priority and can generate real value for the business:
● Data Governance should be applied only to certain areas: those that allow the organization to grow its business and those in which security and compliance assume a relevant importance.
● Data Governance does not translate into new activities or processes: on the contrary, in most cases it is a matter of "clean up" activities that are already done in the organization in an unstructured, inefficient and without a clear attribution to a figure. It is not a question of adding new processes, but of modifying existing processes to ensure compliance with good data management practices. These practices are generally described in data governance policies.

There are several benefits that the introduction of a structured process of Data Governance in the company can bring, including:
● Revenue growth. Data Governance strengthens business support solutions that aim to increase market size
● Trust the data that is used to make decisions, thereby increasing the responsiveness of the business.
● Decreased risk. Even today, much of Data Governance programs are driven by security, privacy and regulatory compliance needs. Managing data correctly means being able to identify, monitor and anticipate risks.
● Efficiency. Avoid waste and low value-added activities such as verifying data goodness and correcting errors.
Over the past two years, many of the companies that had not yet introduced Data Governance practices have begun to structure them, considering the increasing importance of data for the business. Managing data, rather than reacting to problems on time, is an essential step to improving your effectiveness and gaining economic value from data.