{"id":25238,"date":"2025-01-13T03:03:06","date_gmt":"2025-01-13T03:03:06","guid":{"rendered":"https:\/\/peraltafinancing.com\/analytics\/10-tips-for-building-an-effective-data-governance-model\/"},"modified":"2025-01-13T03:03:06","modified_gmt":"2025-01-13T03:03:06","slug":"10-tips-for-building-an-effective-data-governance-model","status":"publish","type":"post","link":"https:\/\/fivemor.com\/?p=25238","title":{"rendered":"10 Tips for Building an Effective Data Governance Model"},"content":{"rendered":"<p> <br \/>\n<\/p>\n<div id=\"wtr-content\" data-bg=\"#0cacf8\" data-fg=\"#0cacf8\" data-width=\"5\" data-mute=\"\" data-fgopacity=\"1.00\" data-mutedopacity=\"1.00\" data-placement=\"top\" data-placement-offset=\"48\" data-content-offset=\"0\" data-placement-touch=\"top\" data-placement-offset-touch=\"0\" data-transparent=\"1\" data-shadow=\"1\" data-touch=\"\" data-non-touch=\"1\" data-comments=\"0\" data-commentsbg=\"#0cacf8\" data-location=\"page\" data-mutedfg=\"#0cacf8\" data-endfg=\"#f44813\" data-rtl=\"\">\n<p>Before\u00a0relying\u00a0on analytics for all or part of\u00a0your\u00a0strategic decision-making,\u00a0implement\u00a0suitable processes to ensure that data flows smoothly through all business departments while\u00a0preserving\u00a0its quality, accessibility, usability and security\u00a0using these\u00a0tips.\u00a0<\/p>\n<p>\u00a0<\/p>\n<h2>1. Diagnose the data assets within the organisation\u00a0<\/h2>\n<p>For\u00a0data to be fully profitable for an organisation,\u00a0you must\u00a0know how to select, collect, store and use it effectively, especially as data is both abundant and easily lost.\u00a0You can start to do this by\u00a0taking\u00a0inventory of all the data present in the company,\u00a0identifying\u00a0its various sources (your management systems, websites, social networks, marketing and advertising campaigns, etc.) and then\u00a0defining\u00a0the points of friction where there is a loss of value due to poor data quality.\u00a0Keep in mind the \u201c5 Vs\u201d:\u00a0<\/p>\n<ol>\n<li><strong>Volume<\/strong>: With the growth in the use of connected objects, the development of geolocation and the rise of analytics in digital marketing, the\u00a0volume of data to be stored and processed\u00a0has exploded\u00a0in recent years.\u00a0Determine the quantity of information held in your databases to\u00a0guide\u00a0your\u00a0data\u00a0management method.\u00a0<\/li>\n<\/ol>\n<ol start=\"2\">\n<li><strong>Variety<\/strong>:\u00a0Data can be complex and diverse, as well as structured or unstructured (voice, biometric, transactional, web analytics, textual, images, etc.). It can also come from a wide range of information systems.\u00a0Capture it in different places,\u00a0centralise\u00a0it and cross-check it\u00a0to\u00a0map\u00a0all your data in an exhaustive way.\u00a0<\/li>\n<\/ol>\n<ol start=\"3\">\n<li><strong>Velocity<\/strong>:\u00a0Because\u00a0we live in an age of immediacy, personalisation and predictive marketing,\u00a0we need to move increasingly quickly and\u00a0proactively to meet customer needs.\u00a0Choose high-performance software with powerful computing capabilities\u00a0that\u00a0is\u00a0flexible and\u00a0incorporates\u00a0state-of-the-art\u00a0machine learning.\u00a0Audit your infrastructure to choose the most efficient tools, in line with your needs, and build a\u00a0sound\u00a0technical base.\u00a0\u00a0<\/li>\n<\/ol>\n<ol start=\"4\">\n<li><strong>Veracity<\/strong>:\u00a0This is one of the major objectives in data processing.\u00a0The reliability of data collected and processed\u00a0can be\u00a0threatened in many ways: declarative errors (forms), the diversity of collection points, the actions of bots, malicious acts and other bugs, human errors\u00a0and more.\u00a0There can\u00a0also\u00a0be numerous biases in the analysis.\u00a0This is why it\u2019s\u00a0so\u00a0important to carry out a diagnosis of the quality and accuracy of all your data.\u00a0\u00a0<\/li>\n<\/ol>\n<ol start=\"5\">\n<li><strong>Value<\/strong>:\u00a0The data you use must perfectly align with your organisation\u2019s business and marketing\u00a0goals and create value for both the brand and\u00a0your\u00a0customers. In an environment\u00a0with an\u00a0over-abundance of information, it is about being able to unify all your data, and only the data that useful to you, and act on it swiftly to generate profit or knowledge.\u00a0<\/li>\n<\/ol>\n<p class=\"has-drop-cap\"><em>AT tip:\u00a0With our Data Manager tool, you\u00a0can\u00a0validate each new property sent to your tag before making it available to your company\u2019s employees.<\/em>\u00a0<\/p>\n<p>\u00a0<\/p>\n<h2>2. Unite the whole enterprise around a common data governance strategy\u00a0<\/h2>\n<p>In addition to carrying out the data diagnosis, it is important that all company departments are involved in the use of data, from general management to the operational and field teams, including all team leaders.\u00a0All employees\u00a0should\u00a0fully understand the challenges and benefits of shared, quality, de-siloed data.\u00a0To\u00a0involve the\u00a0whole\u00a0company in this transition, consider these\u00a0phases:\u00a0<\/p>\n<ul>\n<li>Conduct individual or group interviews with different departments to better understand the current\u00a0data\u00a0situation, determine specific organisational requirements and\u00a0assess\u00a0any expectations regarding data governance. Use the opportunity to make teams aware of the potential risks of exploiting poor-quality or unsecured data.\u00a0<\/li>\n<li>Hold\u00a0practical workshops\u00a0to co-construct a holistic methodological framework for deploying data governance.\u00a0<\/li>\n<li>Undertake real use case analysis\u00a0of\u00a0a specific or recurring business problem associated with a specific data scope\u00a0with the support of employees. For example, in the e-commerce sector, this could focus on errors in product packaging dimensions which, in cascade,\u00a0lead to\u00a0logistical\u00a0difficulties and\/or cart abandonment because the customer discovers excessive delivery costs.\u00a0\u00a0\u00a0<\/li>\n<\/ul>\n<p>\u00a0<\/p>\n<p>The key is to generate interest and launch the data governance project with employees who are receptive, in demand and invested.\u00a0You can also\u00a0unite the teams around the drafting of a common data governance charter that outlines the mission, main objectives and roles of all involved.\u00a0<\/p>\n<p>\u00a0<\/p>\n<p>Finally, establish and communicate the strategic objectives\u00a0common to the whole organisation or more specific to each business unit, and then set out all the organisation\u2019s performance indicators\u00a0so\u00a0everyone\u00a0understands their role in supporting the governance model.\u00a0<\/p>\n<p>\u00a0<\/p>\n<p class=\"has-drop-cap\"><em>AT tip:\u00a0We recommend that you start by defining the key\u00a0performance\u00a0indicators you want to measure in our suite. This will allow you to quickly show your collaborators the value of an analytics suite. You can then expand your data model to meet an increasing number of needs.<\/em>\u00a0<\/p>\n<p>\u00a0<\/p>\n<h2>3. Choose a data governance operating model\u00a0both\u00a0appropriate to your structure and agile\u00a0<\/h2>\n<p>When launching a data governance project, you should avoid falling into the trap of trying to tackle all the technical, organisational and regulatory issues at the same time. Overloading everyone\u2019s calendars with a plethora of data governance-related tasks will probably jeopardise your chances of success. Don\u2019t underestimate the time needed to obtain the first tangible results. Establish a precise roadmap, validated by stakeholders, with intermediate\u00a0milestones\u00a0to evaluate efforts and progress.\u00a0<\/p>\n<p>\u00a0<\/p>\n<p>Following the same principle, bear in mind that\u00a0several\u00a0different data governance models\u00a0exist.\u00a0Choose the one that is best suited to your environment, your needs, your human and financial resources and your\u00a0data\u00a0maturity\u00a0stage.\u00a0<\/p>\n<p>\u00a0<\/p>\n<p class=\"has-drop-cap\"><em>AT tip:\u00a0With\u00a0AT Internet, your data model is fully scalable; you can add new properties throughout the life of your analytics project. You don\u2019t need to plan everything in advance to start your integration.<\/em>\u00a0<\/p>\n<p>\u00a0<\/p>\n<h2>4. Select stakeholders and identify all data actors\u00a0<\/h2>\n<p>In the past, there has been a tendency to place data governance solely under the responsibility of IT teams. In today\u2019s data-driven organisation, governance should be implemented and supported\u00a0across\u00a0all business units, as each\u00a0has\u00a0a role to play\u00a0when it comes to\u00a0data\u00a0oversight.\u00a0<\/p>\n<p>\u00a0<\/p>\n<p>First,\u00a0hire or\u00a0appoint a\u00a0Chief\u00a0Data\u00a0Officer\u00a0responsible for data governance throughout the company. Their role is to get projects approved and\u00a0prioritised,\u00a0manage\u00a0budgets, procure staff for the\u00a0program\u00a0and ensure full documentation. Ideally, the CDO should report directly to the CEO. In\u00a0leaner\u00a0organisations, assign the\u00a0role to an executive\u00a0counterpart,\u00a0such as a BI &amp;\u00a0Data\u00a0Manager.\u00a0Then, expand the project team by putting together a multidisciplinary group with the following profiles:\u00a0<\/p>\n<ul>\n<li><strong>Data Owner(s)<\/strong>:\u00a0They\u00a0oversee\u00a0the data in a specific area or business department. Data\u00a0owners are responsible for ensuring processes are followed to guarantee the collection, security and quality of data.\u00a0They\u00a0must\u00a0map the data,\u00a0control access to it,\u00a0ensure it is protected and define a repository to\u00a0contextualise\u00a0the data. In other words, surrounded by an abundance of data, they\u00a0must\u00a0determine how particular data is\u00a0used\u00a0in responding to a specific problem.\u00a0So, the marketing director can be the data owner of customer data,\u00a0the HR director can be the data owner of internal company data\u00a0and the CFO can be the data owner of financial data.\u00a0<br \/>\u00a0<\/li>\n<li><strong>Data Steward(s)<\/strong>:\u00a0They are the data coordinators and administrators of your\u00a0<a href=\"https:\/\/marketing.piano.io\/cookieless-toolkit\" target=\"_blank\" rel=\"noreferrer noopener\">data lake<\/a>, the\u00a0centralised repository\u00a0that\u00a0allows\u00a0the storage and analysis of all structured and unstructured data at any scale. They are responsible for\u00a0organising\u00a0and managing all data or a particular data entity, with the aim of\u00a0standardisation\u00a0and compliance with policies and regulations. They capture data elements; they can correct them by ensuring that there are no duplicates in the lake; they can give certain information the status of \u201creference data;\u201d\u00a0and\u00a0they verify the level of confidence and quality of the databases The\u00a0data\u00a0steward also has a role in reporting standards and works in tandem with the\u00a0data\u00a0engineer (who designs the data pipelines), the\u00a0data\u00a0scientist (who designs and applies algorithms) and the\u00a0data\u00a0analyst (who, as the name suggests, analyses the data). In tighter\u00a0organisations, a\u00a0data\u00a0manager may fill this\u00a0data\u00a0steward role.\u00a0<\/li>\n<\/ul>\n<ul>\n<li><strong>A Data Custodian<\/strong>:\u00a0This is more of an IT role, ensuring the control, preservation, transport and storage of data in the company. They are not in charge of data quality issues, which fall under the responsibility of the\u00a0data\u00a0steward. As the database administrator, the\u00a0data\u00a0custodian ensures the proper life cycle of the data by authorising and controlling access to the data, defining technical processes to ensure the integrity of the data and carrying out technical controls to secure, back up and archive the data and the changes made to it. In some companies, the\u00a0data\u00a0architect\u00a0may hold the role of\u00a0data\u00a0custodian.\u00a0<\/li>\n<\/ul>\n<p>\u00a0<\/p>\n<p>Of course, alongside these primary roles, there are also secondary functions such as data analysts, marketing managers, product owners\/managers, content or community managers, traffic managers and UX designers who use, consume and analyse data on a daily basis. Not forgetting the support functions, the\u00a0Data\u00a0Protection\u00a0Officer (DPO) is responsible for information, advice and internal control of personal data governance. They ensure the strict application of data protection regulations (GDPR, CCPA, CNIL guidelines).\u00a0You can build an\u00a0RACI matrix (Responsible, Accountable, Consulted, Informed)\u00a0to model and formalise the roles and missions of each stakeholder.\u00a0\u00a0<\/p>\n<p>\u00a0<\/p>\n<p class=\"has-drop-cap\"><em>AT tip:\u00a0Our suite has reporting interfaces tailored to each user profile:\u00a0Data\u00a0Query for advanced analysts and data scientists;\u00a0Explorer for analysts and IT;\u00a0and Dashboard for marketing and HR.<\/em>\u00a0<\/p>\n<p>\u00a0<\/p>\n<h2>5. Create steering bodies and remove data silos within the company\u00a0<\/h2>\n<p>Once you have assembled your data governance project team, you can bring them together in a dedicated\u00a0data governance\u00a0office,\u00a0a committee\u00a0that\u00a0makes\u00a0strategic decisions about the implementation within the various business units of the company. They approve data policies and standards and deal with any data management, security and quality issues that arise.\u00a0Set up one-off or regular sessions with written or spoken feedback\u00a0if you want to pass on the information and decisions taken to the more operational teams that use the data.\u00a0<\/p>\n<p>\u00a0<\/p>\n<p>Ideally, you should choose a horizontal mode of governance by putting data at the\u00a0centre\u00a0of your activity and your business issues. Based on this principle, you can, for example, accelerate the removal of silos between direct marketing, advertising and customer service, and bring together CRM and media expertise and technologies within organisations, brands and their agencies. To achieve this, start by making your employees aware of the benefits of cooperating and sharing data\u00a0daily. To facilitate the process, you can also set up cross-functional projects that bring together teams that are not used to working together and give them common goals.\u00a0<\/p>\n<p>\u00a0<\/p>\n<p>Next, ensure that all the data useful for carrying out projects is consolidated on a data management platform or a hub that guarantees the reliability and interconnection of data. It is\u00a0critical\u00a0to make all teams aware of the existence of a centralised data asset and to share a common vision\u00a0of\u00a0how to manage data between the various parts of an organisation\u00a0to\u00a0harmonise practices.\u00a0<\/p>\n<p>\u00a0<\/p>\n<p class=\"has-drop-cap\"><em>AT tip:\u00a0AT Internet\u00a0allows you to centralise and share your data sets\u00a0with\u00a0the entire company through a central search zone. You can then integrate them into business-oriented workspaces or share them with a range of business\u00a0areas\u00a0across through Explorer and Dashboards.<\/em>\u00a0<\/p>\n<p>\u00a0<\/p>\n<h2>6. Documenting the project and writing shared resources\u00a0<\/h2>\n<p>To successfully implement a data governance project, establish standard processes and adopt a common language within the organisation.\u00a0<\/p>\n<p>An effective way to carry this out\u00a0is to\u00a0provide\u00a0your\u00a0teams with a\u00a0<strong>data map<\/strong>,\u00a0a comprehensive topography of all the data collected and used by the company in the various information systems. It allows the identification of data assets, their flows, their storage and their processing methods.\u00a0The aim of this process is to make the data fully accessible and understandable to all employees so that\u00a0everyone\u00a0can\u00a0identify the origin of a piece of data, know how it is calculated\u00a0and\u00a0spot any duplication.\u00a0Data mapping consists of several tools:\u00a0<\/p>\n<ul>\n<li><strong>A business glossary<\/strong>:\u00a0A\u00a0unique knowledge base common to all employees,\u00a0it allows for the precise definition of all the terminologies linked to the data in circulation,\u00a0with the aim of facilitating the exchange of information between the various participants.\u00a0<br \/>\u00a0<\/li>\n<li><strong>The data model<\/strong>:\u00a0This huge table shapes the structure of the company\u2019s data and gives information about its storage.\u00a0<br \/>\u00a0<\/li>\n<li><strong>A data flow diagram<\/strong>:\u00a0This provides guidance on the methods of transforming, standardising and processing data within the different information systems of the company.\u00a0<\/li>\n<\/ul>\n<p>\u00a0<\/p>\n<p>Also, the data mapping includes a section on the format in which the different types of data are made available, as well as their conditions of access and use.\u00a0<\/p>\n<p>\u00a0<\/p>\n<p class=\"has-drop-cap\"><em>AT tip:\u00a0Put time and\u00a0effort\u00a0into\u00a0the naming of all your analytics elements so that your employees feel comfortable with the data they are handling.<\/em>\u00a0<\/p>\n<p>\u00a0<\/p>\n<h2>7. Ensure the quality of your data to improve your decisions\u00a0<\/h2>\n<p>In a data-driven organisation, data steers most of your decisions,\u00a0like\u00a0the nature and timing of promotional operations or communication campaigns,\u00a0the segmentation of audiences and the reliability of targeting\u00a0and\u00a0the correction or addition of functionalities on a website or mobile application.\u00a0To carry out all these actions, you must have complete confidence in the quality of the data.\u00a0And using poor-quality data can have serious consequences for your business, such as:\u00a0<\/p>\n<ul>\n<li>Loss of revenue and business opportunities\u00a0<\/li>\n<li>Decrease in the ROI of your actions\u00a0\u00a0<\/li>\n<li>Reduction\u00a0in\u00a0quality of decisions\u00a0\u00a0<\/li>\n<li>Contamination of other data projects (CRM,\u00a0data\u00a0lake, CDP, etc.)\u00a0\u00a0<\/li>\n<li>Loss of internal confidence and credibility with your customers\u00a0<\/li>\n<\/ul>\n<p>Data can also be altered by a variety of risk factors\u00a0during\u00a0its journey:\u00a0<\/p>\n<ul>\n<li>Unmeasured traffic due to missing or incorrect tags\u00a0<\/li>\n<li>Unmeasured traffic due to partial measurement methods (sampling)\u00a0<\/li>\n<li>Overestimated traffic due to bots\u00a0<\/li>\n<li>Traffic blocked by adblockers\u00a0<\/li>\n<li>Overestimated conversions due to poor source attribution\u00a0<\/li>\n<li>Traffic not excluded despite lack of user consent\u00a0<\/li>\n<\/ul>\n<p>\u00a0<\/p>\n<p>To avoid\u00a0these risks, be vigilant at all stages of the data life cycle,\u00a0starting with the critical moment of data collection because this phase is permanent. And each modification or update of the\u00a0website\u00a0or tracking inevitably poses a risk to the quality of the collection. Put in place effective methodologies and tools to orchestrate and document this process.\u00a0First, make sure that the tags are correctly implemented in your tagging plans. Check them regularly and completely, ideally with automated acceptance tests, as manual operation increases the risk of error\u00a0and is\u00a0tedious and time-consuming.\u00a0<\/p>\n<p>\u00a0<\/p>\n<p>We believe that this data control process should be extremely fast and easy to monitor, verify and correct. That\u2019s why\u00a0AT Internet\u00a0offers a dedicated data quality toolkit. It allows analysts and marketers to check, test and modify tags themselves, without the help of technical teams.\u00a0<\/p>\n<p>\u00a0<\/p>\n<p class=\"has-drop-cap\"><em>AT tip:\u00a0Create sites for your internal environments,\u00a0such as\u00a0a \u201cdevelopment\u201d site,\u00a0that\u00a0let\u00a0you test the addition of parameters in your tags before deploying them on your production sites.<\/em>\u00a0<\/p>\n<p>\u00a0<\/p>\n<h2>8. Ensure the regulatory compliance of the data\u00a0<\/h2>\n<p>With the implementation of the GDPR\u00a0in 2018, the\u00a0CCPA\u00a0in 2020\u00a0and the French CNIL\u2019s guidelines in 2021, companies are becoming increasingly aware of the importance of respecting the protection of users\u2019 personal data on their various digital platforms.\u00a0<\/p>\n<p>In the event of non-compliance, you may face sanctions ranging from a simple reminder to a heavy administrative fine, as well as severe restrictions on your data capital.\u00a0Non-compliance can also damage your brand image and\u00a0reduce\u00a0consumer trust.\u00a0It\u00a0is important to take steps on your websites and mobile applications to ensure\u00a0you collect\u00a0your visitors\u2019 consent in a free and informed manner. To do this, choose a supplier\u00a0with\u00a0rigorous data management and full respect for legal regulations.\u00a0<\/p>\n<p>\u00a0<\/p>\n<p class=\"has-drop-cap\"><em>AT tip:\u00a0The data model provided by\u00a0AT Internet\u00a0allows you to specifically measure and track the proportion of your users who have not consented to being tracked online.<\/em>\u00a0<\/p>\n<p>\u00a0<\/p>\n<h2>9. Democratising the use of data internally\u00a0<\/h2>\n<p>The democratisation of data within the company is\u00a0critical\u00a0to\u00a0data governance. It is a process of making data accessible to as many people as possible by adopting a data culture.\u00a0To set up a supportive framework for this data evangelisation work,\u00a0you can\u00a0start by:\u00a0\u00a0<\/p>\n<ul>\n<li>Accurately informing all business teams about all\u00a0data managed within the company, its meaning and its context\u00a0<\/li>\n<li>Specifying\u00a0the use cases for this data\u00a0<\/li>\n<li>Indicating\u00a0where it is located and how to access it\u00a0<\/li>\n<li>Giving\u00a0details about the quality and reliability of the data\u00a0<\/li>\n<li>Designating\u00a0data referents who\u00a0can\u00a0support users on a daily basis\u00a0<\/li>\n<\/ul>\n<p>Next, set up a specific support\u00a0program, such as\u00a0training sessions and internal workshops,\u00a0to guide users in the operational use of tools and data\u00a0for\u00a0specific issues.\u00a0To\u00a0encourage all employees to use the data, the data team can design dashboards dedicated to\u00a0the management of\u00a0each activity.\u00a0<\/p>\n<p>\u00a0<\/p>\n<p class=\"has-drop-cap\"><em>AT tip:\u00a0We believe that the most effective way to democratise data is to convey it through reports sent by email as well as through your company\u2019s internal messaging system.<\/em>\u00a0<\/p>\n<p>\u00a0<\/p>\n<h2>10. Evaluate the performance of your data governance system on an ongoing basis\u00a0<\/h2>\n<p>Once you have laid\u00a0the foundation of\u00a0your data governance strategy, measure the satisfaction of internal staff, evaluate the performance of the measures taken, progressively iterate and improve the processes according to your maturity curve. Nothing is set in stone in the deployment of data governance.\u00a0Be as agile as possible to ensure the methods applied align\u00a0with the objectives pursued.\u00a0<\/p>\n<p>\u00a0<\/p>\n<p class=\"has-drop-cap\"><em>AT tip:\u00a0AT Internet\u00a0allows you to monitor the evolution of key metrics over time, so you can visually analyse the benefits of your sales and marketing actions.<\/em>\u00a0<\/p>\n<\/div>\n\n","protected":false},"excerpt":{"rendered":"<p>Before\u00a0relying\u00a0on analytics for all or part of\u00a0your\u00a0strategic decision-making,\u00a0implement\u00a0suitable processes to ensure that data flows smoothly through all business departments while\u00a0preserving\u00a0its quality, accessibility, usability and security\u00a0using these\u00a0tips.\u00a0 \u00a0 1. Diagnose the data assets within the organisation\u00a0 For\u00a0data to be fully profitable for an organisation,\u00a0you must\u00a0know how to select, collect, store and use it effectively, especially as [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":25239,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[12033],"tags":[2539,11603,5227,7727,1168,3535],"dealstore":[],"offerexpiration":[],"class_list":["post-25238","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-analytics","tag-building","tag-data","tag-effective","tag-governance","tag-model","tag-tips"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>10 Tips for Building an Effective Data Governance Model - Som2ny Network<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/fivemor.com\/?p=25238\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"10 Tips for Building an Effective Data Governance Model - Som2ny Network\" \/>\n<meta property=\"og:description\" content=\"Before\u00a0relying\u00a0on analytics for all or part of\u00a0your\u00a0strategic decision-making,\u00a0implement\u00a0suitable processes to ensure that data flows smoothly through all business departments while\u00a0preserving\u00a0its quality, accessibility, usability and security\u00a0using these\u00a0tips.\u00a0 \u00a0 1. 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