{"id":17357,"date":"2023-04-25T15:46:03","date_gmt":"2023-04-25T13:46:03","guid":{"rendered":"https:\/\/www.blueway.fr\/?p=17357"},"modified":"2024-07-12T11:34:04","modified_gmt":"2024-07-12T09:34:04","slug":"data-governance-toolkit","status":"publish","type":"post","link":"https:\/\/www.blueway.fr\/en\/blog\/data-governance-toolkit","title":{"rendered":"Which toolkit best supports implementation of your organisation\u2019s data governance?"},"content":{"rendered":"\n

Decision-makers often wish to put a data governance strategy in place. However, the next step is to determine how to implement such a strategy and which data governance toolkit to use. <\/strong>The functional rules, control over data traffic and the distribution of roles played by actual people are just some of the issues to be clarified through the adoption of an appropriate methodology and toolkit, culminating in effective operational data governance for your organisation.<\/p>\n\n\n\n

What need is met by a data governance strategy?<\/h2>\n\n\n\n

Data governance is a cross-cutting approach, which consists of better understanding and standardising the use made of data by all stakeholders in an organisation<\/strong>. This improved understanding and standardisation are essential, to meet the needs of the functional business areas consuming the data, and achieve their objectives. But this is not all.<\/p>\n\n\n\n

With the huge increase in volumes and the crucial requirement to protect data (security and integrity<\/a>) , plus compliance with GDPR and any industry-specific regulations), setting up data governance is often no longer really an optional matter for businesses. Governance provides a data management framework, adding value and ultimately supporting better-informed decision making.<\/strong><\/p>\n\n\n\n

\"mdm<\/figure>
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Master Data Management : data quality and traceability at the heart of your information system<\/p>\n\n\n\n

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Download white paper<\/a><\/div>\n<\/div>\n<\/div><\/div>\n\n\n\n

The key areas and roles on which data governance is based<\/h2>\n\n\n\n

Governance must be developed along four main avenues to meet a company’s strategic and operational requirements:<\/p>\n\n\n\n

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Developing a data culture and key roles in the organisation<\/strong><\/strong><\/h3>\n\n\n\n
\"Culture<\/figure>\n\n\n\n

To enable all involved to better understand and play a part in the data governance process, it is essential to develop a data culture internally. To this end, members of staff will be trained, and project sponsors and key roles defined and assigned, adapting the organisation to best address your data issues.<\/p>\n<\/div>\n\n\n\n

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Data standardisation and quality<\/strong><\/strong><\/h3>\n\n\n\n
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\"Qualit\u00e9<\/figure>\n\n\n\n
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It is essential to have quality rules, a data glossary and a single version of master data to ensure the whole organisation is working on the same basis.<\/p>\n<\/div>\n<\/div>\n\n\n\n

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Data movement<\/strong><\/strong><\/h3>\n\n\n\n
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\"Transport<\/figure>\n\n\n\n

Once data has been evaluated, it is important to ensure it can be effectively conveyed to, and understood by, the business functions. Data governance entails proper control over data traffic<\/a>, taking into due account the applications in place, the IS architecture, and business requirements.<\/p>\n<\/div>\n\n\n\n

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Management throughout the lifecycle<\/strong><\/strong><\/h3>\n\n\n\n
\"Gestion<\/figure>\n\n\n\n

Mapping and displaying the data lifecycle, from collection to consumption, maximises that data\u2019s utility. Properly delimited processes and effective supervision ensure alignment and better management of the data lifecycle (data lineage, traceability, etc.).<\/p>\n<\/div>\n<\/div>\n\n\n\n

In terms of human resources, data specialists can get involved at various stages to ensure that processing meets objectives and requirements, and fits with the organisation\u2019s business activities. Such specialists include:<\/p>\n\n\n\n