{"id":17742,"date":"2022-08-16T09:23:45","date_gmt":"2022-08-16T07:23:45","guid":{"rendered":"https:\/\/www.digdash.com\/?p=17742"},"modified":"2023-04-26T10:40:22","modified_gmt":"2023-04-26T08:40:22","slug":"why-and-how-to-implement-a-data-governance-policy","status":"publish","type":"post","link":"https:\/\/www.digdash.com\/en\/news-articles\/tips-and-tricks\/why-and-how-to-implement-a-data-governance-policy\/","title":{"rendered":"Why and How to Implement a Data Governance Policy?"},"content":{"rendered":"<p><b>Data governance<\/b><span style=\"font-weight: 400;\"> has become a major issue for companies, which are concerned about fully exploiting their data while also fulfilling various quality and compliance criteria. But what does data governance entail? How do you implement a <\/span><b>data governance<\/b><span style=\"font-weight: 400;\"> strategy? This is what we invite you to discover.<\/span><\/p>\n<h2><b>Data management and data governance: what is the difference?<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">People often confuse <\/span><b>data governance<\/b><span style=\"font-weight: 400;\"> and <\/span><b>data management.<\/b><span style=\"font-weight: 400;\"> But although they are closely related, these two concepts are quite different.<\/span><\/p>\n<h3><b>What is data management?<\/b><\/h3>\n<p><b>Data management<\/b><span style=\"font-weight: 400;\"> refers to the creation and implementation of architecture, policies, and procedures to manage the entire life cycle of an organization&#8217;s data. The implementation of such measures is essential to analyse <\/span><a href=\"https:\/\/www.digdash.com\/en\/news-articles-en\/tips-and-tricks\/data-management-6-tips-for-managing-large-volumes-of-data\/\" target=\"_blank\" rel=\"noopener\"><span style=\"font-weight: 400;\">increasingly complex and large amounts of data<\/span><\/a><span style=\"font-weight: 400;\">. If data is considered a key business asset, it must also be managed as such.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">In concrete terms, data management combines various elements:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Data preparation:<\/b><span style=\"font-weight: 400;\"> the process of cleaning and transforming raw data to ensure accurate data analysis. The importance of this first step is sometimes underestimated, as companies are too quick to produce reports and analyses, and thus, end up making the wrong decisions using the wrong data.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Data pipelines: <\/b><span style=\"font-weight: 400;\">used to automatically transfer data from one system to another.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Data warehouses:<\/b><span style=\"font-weight: 400;\"> facilitate data analysis by consolidating all data sources.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Extract Transform Load (ETL) processes:<\/b><span style=\"font-weight: 400;\"> transform data and upload it to an organization&#8217;s data warehouse. ETL processes are often automated and require pipelines.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Data catalogues:<\/b><span style=\"font-weight: 400;\"> help you manage metadata more effectively, while also making it easier to search and track data.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Data architecture:<\/b><span style=\"font-weight: 400;\"> the data flow management structure.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Data security:<\/b><span style=\"font-weight: 400;\"> refers to the processes in place to protect data from unauthorized access and the risk of corruption.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Data governance:<\/b><span style=\"font-weight: 400;\"> consists in defining policies and procedures to maintain data security and compliance. Data governance is therefore a sub-area of data management.<\/span><\/li>\n<\/ul>\n<h3><b>What is data governance?<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Data governance helps you answer questions, such as:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Who owns the data?<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Who can access what data?<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">What security measures are in place to protect data and privacy?<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Does the data comply with the latest regulations, such as the General Data Protection Regulation (GDPR)?<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">What data sources can be used?<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">It should also be noted that <\/span><b>data governance<\/b><span style=\"font-weight: 400;\"> concerns both the data itself and \u201ccontent\u201d, i.e. dashboards, graphics and other forms of analysis used to make the data speak. Data governance therefore consists in implementing models in different areas, ranging from content management to data source management, including data security.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">In general, data governance is based on four main principles:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Data quality:<\/b><span style=\"font-weight: 400;\"> accurate, complete and reliable data is the cornerstone of a data-driven company. Data quality management is therefore a key pillar in any governance policy.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Data security and compliance:<\/b><span style=\"font-weight: 400;\"> data sources must be defined and classified according to their respective risk levels. You must then create secure access points, striking the right balance between security and the user experience.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Data stewardship: <\/b><span style=\"font-weight: 400;\">involves monitoring how teams use data sources. \u201cStewards\u201d must lead by example to ensure data access, security and quality.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Data transparency:<\/b><span style=\"font-weight: 400;\"> all procedures in place must be fully transparent. As a result, analysts and business users must be able to easily determine where their data has come from and whether it has any special characteristics.<\/span><\/li>\n<\/ul>\n<h2><b>What are the benefits of data governance?<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">Now that we have clearly explained the concept of data governance, we must ask ourselves the most important question of all: what&#8217;s its purpose?<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Implementing a <\/span><b>data governance strategy<\/b><span style=\"font-weight: 400;\"> can bring many benefits to a company:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Higher-quality<\/b><span style=\"font-weight: 400;\">, comprehensive and consistent data for thorough analyses and informed decision-making.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">An <\/span><b>overview of the data<\/b><span style=\"font-weight: 400;\">, allowing company players, who may speak a common language, to understand it more easily.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Data mapping<\/b><span style=\"font-weight: 400;\">, so that users can access each piece of data instantly and to help them find exactly what they are looking for.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Data that complies<\/b><span style=\"font-weight: 400;\"> with regulatory requirements regarding confidentiality and privacy at all levels. Having a data governance policy in place is therefore essential to ensure compliance with the GDPR (General Data Protection Regulation).<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Better data management:<\/b><span style=\"font-weight: 400;\"> data governance is an integral part of data management, thereby contributing to the implementation of best practices, in order to manage data more efficiently on a daily basis.<\/span><\/li>\n<\/ul>\n<h2><b>4 steps to ensuring successful data governance<\/b><\/h2>\n<h3><b>1) Set out your data governance objectives<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">A <\/span><b>data governance strategy<\/b><span style=\"font-weight: 400;\"> must be aligned with your company&#8217;s overall strategy and ultimately deliver tangible benefits. Data isn&#8217;t an end in itself, but rather a resource that organizations can exploit to create value and become more competitive.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">To be truly effective and suitable, a data governance policy must therefore be focused on business needs and processes, with clearly defined objectives.<\/span><\/p>\n<h3><b>\u00a02) Carry out data mapping<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">To implement a data governance policy, you must compile an inventory of all the data owned by the company, then map it. This key step will help you answer a number of questions:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">What are the different types of data available?<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Who are their users?<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">How is the data processed and transformed?<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Where is the data stored?<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Are there any data quality, security, or privacy concerns?<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">This allows you to obtain a clear and accurate picture of the data at your disposal, as well as its nature and quantity. You will therefore be able to effectively <\/span><b>structure your data governance strategy<\/b><span style=\"font-weight: 400;\"> and better define its objectives and scope.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">At the end of this mapping work, you will have a large amount of information (metadata) that will help you define and characterize your data. This <\/span><b>metadata<\/b><span style=\"font-weight: 400;\"> must be perfectly managed and structured to guarantee a successful data governance policy.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">To achieve this, you must use a <\/span><b>data catalogue<\/b><span style=\"font-weight: 400;\">, allowing you to gather all the metadata and consult it easily. This gives the company quick access to any of its data, as well as all the corresponding information: origin, transformations, uses, etc.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Through <\/span><b>data mapping<\/b><span style=\"font-weight: 400;\">, all users speak a common language, greatly facilitating data access.<\/span><\/p>\n<h3><b>\u00a03) Define a specific framework to ensure more effective data governance<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Data governance is a global strategy that concerns all company departments: you must therefore define a solid framework to support its deployment.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The rules in place to ensure <\/span><b>data compliance and security<\/b><span style=\"font-weight: 400;\"> must be clearly stated. New ways of working must also be established to ensure quality and effective data management. It is also essential to determine the rights and responsibilities of different users with respect to the company&#8217;s data.<\/span><\/p>\n<p><b>The roles of employees concerned by the data governance policy<\/b><span style=\"font-weight: 400;\"> must also be clearly defined, even if that means updating their job descriptions. This will not only allow you to clearly explain the duties of each person, but also to transmit your data culture throughout the organization.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">All teams are therefore encouraged to use data in some way or another and to adopt the best data practices. You may wish to hold meetings or workshops dedicated to data governance to raise awareness of this topic among employees.<\/span><\/p>\n<h3><b>4) Integrate efficient data governance tools<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">To carry out a data governance program on a large scale, companies must be able to count on efficient tools adapted to their needs. A <\/span><a href=\"https:\/\/www.digdash.com\/en\/news-articles-en\/business-intelligence-bi\/what-is-business-intelligence-all-about\/\" target=\"_blank\" rel=\"noopener\"><b>Business Intelligence<\/b><\/a><b> solution<\/b><span style=\"font-weight: 400;\">, in particular, is not only essential to guarantee data quality, security and transparency, but also to fully exploit data.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">If companies use a <\/span><b>BI tool<\/b><span style=\"font-weight: 400;\">, they are guaranteed access to reliable, comprehensive and exploitable data, which they can then use to perform highly accurate analyses. The result: better day-to-day decision-making and a clearer future vision.<\/span><\/p>\n<p>&nbsp;<\/p>\n<p><b><i>The main aim is to have access to qualitative, secure and compliant data, but implementing a data governance policy is a complex process, especially since it is often supported by a <\/i><\/b><a href=\"https:\/\/www.digdash.com\/en\/news-articles-en\/business-prospects\/succeed-in-deploying-your-business-intelligence-project-the-complete-guide\/\" target=\"_blank\" rel=\"noopener\"><b><i>Business Intelligence project<\/i><\/b><\/a><b><i>, hence the importance of following these four key steps.<\/i><\/b><\/p>\n<div align=\"center\"><a href=\"https:\/\/content.digdash.com\/en-demo-request-lp\"><img fetchpriority=\"high\" decoding=\"async\" class=\"alignnone wp-image-19314\" src=\"https:\/\/www.digdash.com\/wp-content\/uploads\/2023\/04\/cta-telecharger-10-300x100.png\" alt=\"\" width=\"597\" height=\"199\" srcset=\"https:\/\/www.digdash.com\/wp-content\/uploads\/2023\/04\/cta-telecharger-10-300x100.png 300w, https:\/\/www.digdash.com\/wp-content\/uploads\/2023\/04\/cta-telecharger-10.png 600w\" sizes=\"(max-width: 597px) 100vw, 597px\" \/><\/a><\/div>\n","protected":false},"excerpt":{"rendered":"<p>Data governance has become a major issue for companies, which are concerned about fully exploiting their data while also fulfilling various quality and compliance criteria. But what does data governance entail? How do you implement a data governance strategy? This is what we invite you to discover. Data management and data governance: what is the &#8230; <a title=\"Why and How to Implement a Data Governance Policy?\" class=\"read-more\" href=\"https:\/\/www.digdash.com\/en\/news-articles\/tips-and-tricks\/why-and-how-to-implement-a-data-governance-policy\/\" aria-label=\"Read more about Why and How to Implement a Data Governance Policy?\">Lire plus<\/a><\/p>\n","protected":false},"author":21,"featured_media":17583,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[27],"tags":[],"class_list":["post-17742","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-tips-and-tricks"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Data management and data governance: what is the difference? - DigDash<\/title>\n<meta name=\"description\" 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