The notion of governance associated with information systems first appeared in the 90’s. It refers to the IT management resources implemented in an enterprise to achieve its strategic objectives. With the explosion of new technology for the purpose of collecting and sharing information, new security rules to guarantee data protection have been imposed.

In the context of fundamental digital changes, enterprises bear witness to the massive volume of data generated for their race to innovation. This influx of information is leading enterprises to initiate a data-driven governance in a global and transversal way like a strategy-focused priority.

We like to define data governance as an exercise of authority over decision-making power (planning, surveillance, and enforcement of rules) and the controls on data management.

In other words, it allows the clear documentation of the different roles and responsibilities around data as well as determining the procedures and the tools supporting data management within an organization.

An enterprise’s data is a « shared asset » and must be treated as such. It is for this reason that data governance is essential. This set of practices, policies, standards, and guides will supply a solid foundation to ensure that data is properly managed, creating value within an organization.

 

DATA GOVERNANCE: BETWEEN CONTROL AND FACILITATION?

In the past, implementations of data governance within organizations have rarely been successful. Data managers are often too focused on technical management or the strict control of data.

For data users who strive to experiment and innovate around data, governance can evoke a set of restrictions, limitations, and useless bureaucracy. They have frightening visions of data locked in dark catacombs, only accessible after months of fighting against administrative hassles. For others, they remember the painful amount of energy wasted at meetings, updating spreadsheets and maintaining wikis, only to find that no one was even benefiting from the fruits of their labor.

It’s no wonder that data governance has a bad reputation. Despite the fact that it brings real value, organizations avoid carrying out a governance due to past experiences, ill-advised.

It is clear that enterprises are conditioned by regulatory compliance: to guarantee data privacy, its security, and to ensure risk management. Nevertheless, to undertake an offensive strategy, which tends to improve an enterprise’s data usage – guaranteeing useful, useable, and used data – becomes the next crucial step in enhancing this asset.

Data governance therefore is based on two approaches: offensive or defense. It is a matter of orienting the enterprise’s strategy towards IT requirements in terms of data security or a capitalization strategy and analysis in order to generate business value.

Here are, among others, the objectives of a data governance strategy:

DEFENSIVE APPROACH

  • Undertake compliance with the authorities of other countries to avoid sanctions, such as the General Data Protection Regulation (RGPD) enforced in May 2018.

  • Comply with the internal obligations and rules to which the organization’s data is subject.

  • Ensure data security, its integrity and its quality for proper use.

OFFENSIVE APPROACH

  • Increase a company’s profitability and competitive position with the help of data.

  • Optimize data analysis, modeling, visualization, transformations and data enrichment.

  • Increase the company’s flexibility in the use of its data.

Whether your sector of activity is highly regulated or immensely competitive, you will have to invest in having perfect control over your data in order to create innovative products and to keep your head above water.

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