Data governance, the importance of structured data management

Data governance

Data Governance is a set of processes, roles, policies, standards and metrics designed to ensure the effective and efficient use of information to enable an organisation to achieve its objectives. It establishes processes and responsibilities that ensure the quality and security of the data used within an organisation. Data governance defines who can take what action, on what data, in what situations and using what methods. A well-designed data governance strategy is critical for any organisation working with big data, as it helps define how the business can benefit from consistent and common processes and responsibilities. Key business dynamics highlight what data needs to be properly controlled in a data governance strategy and the expected benefits of implementing it

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, due to specific regulations or to the centrality of data in the core business. In these sectors, Data Governance often translates into the activation of new organizational structures which define policies for good management of the data and verify that these procedures are respected.

Data governance, the importance of a strategy

Data Governance means using organisational leverage to ensure that data is managed properly, by being able to clarify

  • the meaning of each piece of data;
  • the responsibilities of the business rather than IT;
  • the criteria for defining quality data;
  • ensuring that the whole organisation is aligned on these aspects.

Even before defining new ad hoc processes, it is, therefore, a matter of verifying how data should be managed in existing processes, having a common language and ensuring that staff dealing with data have the right skills and creating a real data culture throughout the company.

Data governance, how to introduce it in the business

Introducing Data Governance into the business risks being seen as an expensive process whose benefits are visible in the medium to long term. For this reason, today more agile approaches are preferred, which allow this path to be approached gradually, focusing on the main needs of the organisation and structuring the path in successive sprints. But what are the essential steps in this process?

  • Identify the main needs of the organisation and frame how Data Governance can support them, directly or indirectly. The endpoint must be continuously communicated and supported throughout the programme.
  • Form a multidisciplinary working group, involving the different souls of the company. One of the most common mistakes is to think that IT alone should lead and implement the initiative.
  • Start with the basics: define a business glossary, i.e. 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 at an organisational level, defining roles and responsibilities around data definition and management. IT plays a key role in this phase, both in implementing the necessary support tools and in gaining functional knowledge.
  • Measure. Define KPIs at the outset that allows progress and benefits to be measured from the outset and throughout the life of the programme.

Data governance, the cost to the company

Like any process, the introduction of Data Governance involves a series of costly activities in terms of human and financial resources, which is why it is essential to identify the areas in which this process is a priority and can generate real value for the business:

  • Data governance should only be applied to certain areas: those that enable the organisation to grow its business and those where security and compliance are of significant importance.
  • Data governance is not about new activities or new processes: on the contrary, in most cases, it is about ‘tidying up’ activities that are already being done in the organisation in an unstructured, inefficient way and without clear attribution to a figure. It is not a question of adding new processes, but of modifying existing processes to ensure that good data management practices are respected. These practices are generally described in data governance policies.

Data governance, the benefits for the company

There are several benefits that the introduction of a structured Data Governance process in the company can bring, among the main ones are:

  • Revenue growth. Data governance strengthens business solutions that aim to increase market sizes, such as pricing algorithms or personalisation tools.
  • Confidence in the data being used to make decisions, thereby increasing business responsiveness.
  • Decreased risk. Even today, most data governance programmes are driven by security, privacy and regulatory compliance needs. Managing data correctly means being able to identify, monitor and anticipate risks.
  • Efficiency. Avoid wasteful, low-value activities such as checking data and correcting errors.

In the last two years, many companies that had not yet introduced Data Governance practices have started to structure them, considering the ever-increasing importance of data for business. Governing data, rather than reacting to problems in a timely manner, is an essential step in improving effectiveness and deriving economic value from data.

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