How to put data governance into practice

Data governance is a major lever for improving company performance and accelerating business transformation. Data has become a major business asset with very specific characteristics: easy to exchange, often low and/or ephemeral unit value, easy to hack, little known by users and beneficiaries, intangible, sometimes difficult to locate, etc.

At iQo, we support companies’ Data Strategy through an enhanced data use framework in order to open up new development perspectives both internally and externally with customers and partners.

Data governance: managing a strategic business asset

Data governance can become a lever for business performance and transformation, particularly by:

better commercial targeting;
designing innovative products and services and challenging the business model;
the power and agility of the production system, thanks to continuous measurement and data exchange with the ecosystem (OpenData and Data Supply Chain);
a Data for Good approach to reinforce CSR issues;
improving customer and partner relations/information;
improving the strategic management of companies and decision making through data analysis;
improving employee experience (see our article: Using data to improve employee experience).

What should you expect from good data governance?

To achieve these objectives, data must be managed as a dynamic, volatile, and sometimes shared asset. It therefore needs a specific governance framework.

Data governance is first and foremost an organizational framework for organizing the creation, storage, and management of data to guarantee its availability, consistency, usability, integrity, and security. The objective is to manage data as a “product” that meets the needs of the business, its customers, and its partners.

At the same time, data governance must be sufficiently agile to be able to manage current developments, such as Big Data, Open Data, etc., but also future revolutions, particularly those linked to the convergence of Big Data and Artificial Intelligence.

Six principles for organizing data governance

In this context, implementing efficient data governance within the company should be organized based on six key principles.

Define a data governance framework

From the moment data becomes a source of revenue and/or performance, its use must be monitored and controlled. The data governance framework responsible for this monitoring must guarantee the achievement of the data’s business objectives as well as impeccable data quality and security.

This involves taking into account several key aspects of the data:

its reliability in terms of quality
its integrity in terms of preservation
its availability at any time and in any place
its protection against unauthorized or fraudulent use

Take an adaptive approach to your data governance

Depending on the complexity of the activities and the level of control over the data or analysis practices, data governance must adopt a style that can range from “command” to delegation/empowerment of the users and beneficiaries of the data.

The company cannot adopt a blanket approach. It must instead define its approach for each scope of use (functions or processes), re-evaluated over time, and adapted, if necessary, by adopting an agile governance style.

Promote data and data science to pursue a business performance objective

The company’s areas of differentiation and pain points offer favorable ground for developing and supporting the use of data science, particularly through:

detailed market and customer analyses
product design/improvement
process performance
supplier performance
the supply chain
project management, etc.

Integrate data governance with existing governance processes

As we have seen, data is increasingly becoming a key issue for companies. It joins other structuring subjects as the umpteenth subject that generates the umpteenth layer of data governance.

A major challenge is to integrate this governance into existing governance systems wherever possible and to encourage acculturation to avoid the feeling of “adding another layer” to a management system that needs to remain agile.

Emphasize a collaborative approach

Data is the result of a process, of a transversality, which can involve the company’s different internal functions as well as its partners or customers.

Data governance thus carries a challenge on its shoulders: defining data objectives, standards, roles, and responsibilities. This challenge relates to:

performance management
quality management
knowledge management

But to meet the issues facing the organization, its main challenge is to promote cross-functionality and to support the “de-siloed” and collaborative use and enrichment of data.

Anticipate a data-driven Copernican revolution

Several revolutions will strongly challenge data governance. They raise questions for which we currently have only the beginnings of an answer.

The massive opening up of data: how can we design data governance when companies will massively subscribe to a sort of “Data LinkedIn”, a place where “data recruiters” will connect economic actors to share, commercialize, and increase their data? What impact will this opening up have on the business value of data? It will probably involve strengthening Data Lifecycle Management practices and, more generally, Information Lifecycle Management practices.
The blurring of company boundaries in an increasingly interactive, co-decisional ecosystem: company & partners, company & customers, etc.
Product/Data convergence: already present in certain sectors, it will spread, implying a mutation of “data governance” into true “product management”.
Data/AI convergence: this convergence marks out a potentially vertiginous upheaval of the “manpower – methods – machines” triptych with the automation, self-adaptation, and autonomization of processes and of certain decisions that can lead to a fusion between process and data.

In any case, these revolutions call for a convergence of technological (IT, data, AI) and business governance. This will require the construction of new, more outward-facing standards and a revolution in the company’s sovereign and operational skills.

Data governance through strategic integration

At this stage, we understand that because of its considerable potential, but also because of the challenges it represents for the company, data use must imperatively feed companies’ strategic thinking.

This is especially true since its massive use can influence the company’s business model and therefore its strategic positioning. And the technological developments underway or to come, which will provide phenomenal amounts of data, will only reinforce this trend!