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Data governance (DG) is the process of managing the availability, usability, integrity and security of data in business systems, based on people, processes, internal standards and policies that help control the use of data. Effective data governance ensures that data is consistent, reliable and not misused. This becomes increasingly important as organisations face more data, new privacy regulations and increasing reliance on data analytics to optimise business processes, drive decision-making, train artificially intelligent algorithms, sell data and gain competitive advantage from data.

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Data governance is a basic building block of a data strategy and it is therefore important to focus on the results and expected business benefits. A successful data governance strategy focuses not only on good processes and clear roles, but most importantly on the business value it creates. Without effective data governance, data inconsistencies in various organisational systems remain unresolved. Customer names, for example, may be defined differently in sales, logistics and customer service systems. This complicates data integrations and integrity, affecting the reliability of Business Intelligence (BI) analyses, reporting, AI development and other applications that use this data. In addition, data errors may not be identified and fixed in a master database, permanently affecting the accuracy of insights and analytics.

The data for profit analysis and other critical analysis are usually scattered across different applications and departments. Even when an ERP system is in place, it does not always present a single source of truth. There are often different classifications of customers, products and sales data scattered throughout the organisation. This indicates a need for a consistent hierarchical set of classifications that are managed and used throughout the organisation via a modern data platform.

The reality is that many companies have data silos that contain inconsistent data and are not accessible to the right users across the organisation. Or that external data sources are not integrated in ascalable way. It takes effort to align the collection, creation, classification, formatting and use of data across departments. And often, responsibility over data is unclearly delineated and scattered in the organisation across different roles that do not unify tasks. As a result, data governance has become one of the core elements of a data strategy, resulting in an increase of data quality, data valorisation and creates the following benefits:

• to avoid inconsistent, scattered data silos in different departments
• to agree on common definitions for a clear understanding of data
• to improve data quality by identifying and resolving errors in datasets
• to increase the accuracy of analyses and provide management with reliable information
• to build operational artificial intelligent algorithms, post proof of concepts
• to implement policies that help prevent errors and misuse
• to contribute to compliance with privacy laws, ESG and other legislations

A key goal of data governance is to break down data silos in the organisation and integrate external data sources in a scalable way. Governance is necessary because separate systems are set up without a data architecture. Often the IT landscape grows organically so data governance can help harmonise data across those diverse systems. This is done using processes, complemented by roles spread throughout the organisation and supported by tools. Another goal of data governance is to ensure that data is used correctly, to avoid errors creeping into systems and to avoid sensitive (business critical or privacy sensitive) data being used incorrectly. This can be achieved by defining uniform policies around data usage, along with procedures to monitor and facilitate usage. In addition, data governance can help strike a balance between data collection practices and privacy-sensitive information. Besides more accurate analytics and stronger regulatory compliance, other benefits of data governance include improved data quality, lower data management costs and improved access to needed data for data scientists to develop Artificial Intelligence. Ultimately, data governance can help improve decision-making by giving executives valuable information. This leads to competitive advantages, increased revenue and profits.

A data governance approach consists of the rules, processes, policies, organisational structures and technologies deployed to support the programme. It also defines the mission, goals and KPIs, as well as the roles, accountability and responsibilities for making decisions within the programme. Via collaboration and a change process, this approach is clarified and operationalised with the stakeholders.

On the technology side, data governance software should be used to contribute to the automation and the quality of the programme. Based on the data governance approach, a number of choices are made and capabilitie scan be defined, which contribute to a software selection process. In this way, software can be found that supports collaboration and further development of governance policies.

D-Cide has developed a methodology that addresses these domains, making it possible to set up an executable strategy with a well-defined Return on Investment.

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