{"id":1793,"date":"2024-08-16T20:48:54","date_gmt":"2024-08-16T20:48:54","guid":{"rendered":"https:\/\/solutionsreview.com\/thought-leaders\/?p=1793"},"modified":"2024-08-16T20:48:54","modified_gmt":"2024-08-16T20:48:54","slug":"a-data-strategy-theory-vs-practice-part-2-2","status":"publish","type":"post","link":"https:\/\/solutionsreview.com\/thought-leaders\/a-data-strategy-theory-vs-practice-part-2-2\/","title":{"rendered":"A Data Strategy: Theory vs. Practice. Part 2"},"content":{"rendered":"<p id=\"isPasted\">This is Part 2 of the article \u201cA Data Strategy: Theory vs. Practice.\u201d In Part 2, we will continue the analysis of data strategy examples and discuss the following:<\/p>\n<ul>\n<li>A data (management) strategy content: recommended vs. presented in strategies mentioned above (sections 2 and 3)<\/li>\n<li>Recommendations for developing a (meta)data (management) strategy<\/li>\n<\/ul>\n<h2 data-fontsize=\"22\" data-lineheight=\"33px\">A data (management) strategy content: recommended vs. presented in the real strategies (Continuation)<\/h2>\n<h3 id=\"isPasted\" data-fontsize=\"26\" data-lineheight=\"33.8px\">\u201cWhat\u201d section<\/h3>\n<p>This section forces a company to make a serious decision that will impact the success of the data management initiative. This decision is the balance between \u201cwe want\u201d and \u201cwe can.\u201d In other words, it goes about the feasibility of the strategy. When you start writing a data (management) strategy, you should be honest with yourself and your company about the goal of writing the strategy. Do you write it pro forma for demonstration purposes because \u201call others do it,\u201d or do you really need it with the key goal of its implementation? You can stop reading this article if your goal is the first one. If your goal is strategy implementation, I encourage you to dive into the topic of data management principles, framework, and core data management capabilities.<\/p>\n<p>To define the feasible data management strategy, you should make decisions about the following topics:<\/p>\n<h4 data-fontsize=\"20\" data-lineheight=\"28px\">Topic 4: Data management principles<\/h4>\n<h5 data-fontsize=\"26\" data-lineheight=\"39px\">Theory<\/h5>\n<p>A data management principle is a rule that regulates the way data management is implemented.<\/p>\n<p>Different leading industry guidelines have pretty different approaches to defining data management principles. The DAMA-DMBOK approach in the second edition deviates from the approach in the first. In the first edition, the data management principles were more generic. The second edition defines principles per Knowledge Area. I like the approach taken by The Open Group in the\u00a0<a href=\"https:\/\/pubs.opengroup.org\/togaf-standard\/index.html?_ga=2.14610803.577830669.1652819158-1151901334.1652819158\" target=\"_blank\" rel=\"noopener nofollow\" class=\"external\">TOGAF\u00ae Standard<\/a>. In my practice, I apply the method that connects business drivers with data management principles and analyzes the consequences of applying principles.<\/p>\n<p>Figure 5 demonstrates this approach.<\/p>\n<p><a class=\"magnific fr-fic fr-dib lightbox-image-container external\" href=\"https:\/\/media1-production-mightynetworks.imgix.net\/asset\/2aa1379d-751f-4758-bc72-c48f5b030438\/Picture5.png?ixlib=rails-4.2.0&amp;fm=jpg&amp;q=75&amp;auto=format\" data-effect=\"mfp-zoom-in\" rel=\"nofollow\"><img decoding=\"async\" id=\"97313251\" class=\"fr-fic fr-dib attachment\" src=\"https:\/\/media1-production-mightynetworks.imgix.net\/asset\/2aa1379d-751f-4758-bc72-c48f5b030438\/Picture5.png?ixlib=rails-4.2.0&amp;fm=jpg&amp;q=75&amp;auto=format&amp;w=1400&amp;h=1400&amp;fit=max&amp;impolicy=ResizeCrop&amp;constraint=downsize&amp;aspect=fit\" data-asset-id=\"97313251\" data-original-image=\"https:\/\/media1-production-mightynetworks.imgix.net\/asset\/2aa1379d-751f-4758-bc72-c48f5b030438\/Picture5.png?ixlib=rails-4.2.0&amp;fm=jpg&amp;q=75&amp;auto=format\" \/><\/a><\/p>\n<p>Figure 5: The approach to formulating data management principles.<\/p>\n<p id=\"isPasted\">Data management principles must be formulated with a key focus on the feasibility of their implementation. The analysis of consequences must include potential benefits and challenges and required actions.<\/p>\n<h5 data-fontsize=\"26\" data-lineheight=\"39px\">Practice<\/h5>\n<p>I found the formulated principles in four of the five referenced strategies.<\/p>\n<p>Let us review these principles that I grouped into several categories related to:<\/p>\n<p><em>Data governance:<\/em><\/p>\n<ul>\n<li>\u201cData are an asset\u201d (Strategy 4)<\/li>\n<li>\u201cData must have clearly defined accountabilities\u201d (Strategy 4)<\/li>\n<li>\u201cData must follow rules and regulations\u201d (Strategy 4)<\/li>\n<li>\u201cData should be managed consistently\u201d (Strategy 4)<\/li>\n<\/ul>\n<p><em>Ethics:<\/em><\/p>\n<ul>\n<li>Principles of professional ethics (Strategy 2)<\/li>\n<li>Ethical use (Strategy 4)<\/li>\n<li>Ethical governance (Strategies 3 and 5), including:<\/li>\n<li>\u201cUphold Ethics\u201d (Strategy 5)<\/li>\n<li>\u201cExercise Responsibility\u201d (Strategy 5)<\/li>\n<li>\u201cPromote Transparency\u201d (Strategy 5)<\/li>\n<\/ul>\n<p><em>Decision-making<\/em><\/p>\n<ul>\n<li>Conscious decisions (Strategies 3 and 5), including:<\/li>\n<li>\u201cEnsure Relevance\u201d (Strategy 5)<\/li>\n<li>\u201cHarness Existing Data\u201d (Strategy 5)<\/li>\n<li>\u201cAnticipate Future Uses\u201d (Strategy 5)<\/li>\n<li>\u201cDemonstrate Responsiveness\u201d (Strategy 5)<\/li>\n<\/ul>\n<p><em>Culture<\/em><\/p>\n<ul>\n<li>Data-informed culture (Strategy 4)<\/li>\n<li>Learning culture (Strategies 3 and 5), including<\/li>\n<li>\u201cInvest in Learning\u201d (Strategy 5)<\/li>\n<li>\u201cDevelop Data Leaders\u201d (Strategy 5)<\/li>\n<li>\u201cPractice Accountability\u201d (Strategy 5)<\/li>\n<\/ul>\n<p><em>Data management-related<\/em><\/p>\n<ul>\n<li>Data-centric principles for IT architecture (Strategy 2)<\/li>\n<li>Governance and effective management (Strategy 4)<\/li>\n<\/ul>\n<p>As we can see, these strategies have a lot of similar principles.<\/p>\n<h4 data-fontsize=\"20\" data-lineheight=\"28px\">Topic 5: The data management framework<\/h4>\n<h5 data-fontsize=\"26\" data-lineheight=\"39px\">Theory<\/h5>\n<p id=\"isPasted\">I wrote\u00a0<a href=\"https:\/\/datacrossroads.nl\/articles\/article-series\/dama-dmbok2-vs-dcam\/\" target=\"_blank\" rel=\"noopener nofollow\" class=\"external\">multiple articles<\/a>\u00a0on the differences between viewpoints on data management structures of leading data management guides, DAMA-DMBOK2 and DCAM. Years ago, this unalignment brought me to the idea of developing\u00a0<a href=\"https:\/\/datacrossroads.nl\/product\/the-orange-data-management-framework\/\" target=\"_blank\" rel=\"noopener nofollow\" class=\"external\">the \u201cO.R.A.N.G.E.\u201d data management framework<\/a>\u00a0to solve the issues I found in the leading guidelines. This framework is a set of methods and models to establish an operational data management function.<\/p>\n<p>One of this framework\u2019s foundational models is the data management capability model, presented in Figure 6. The data management capability consists of several core sub-capabilities, each of which plays a different role in delivering business value from data management.<\/p>\n<p><a class=\"magnific fr-fic fr-dib lightbox-image-container external\" href=\"https:\/\/media1-production-mightynetworks.imgix.net\/asset\/1f7dc8ed-42ac-4a14-8fa4-fc367f8f42ab\/Picture6.png?ixlib=rails-4.2.0&amp;fm=jpg&amp;q=75&amp;auto=format\" data-effect=\"mfp-zoom-in\" rel=\"nofollow\"><img decoding=\"async\" id=\"97313375\" class=\"fr-fic fr-dib attachment\" src=\"https:\/\/media1-production-mightynetworks.imgix.net\/asset\/1f7dc8ed-42ac-4a14-8fa4-fc367f8f42ab\/Picture6.png?ixlib=rails-4.2.0&amp;fm=jpg&amp;q=75&amp;auto=format&amp;w=1400&amp;h=1400&amp;fit=max&amp;impolicy=ResizeCrop&amp;constraint=downsize&amp;aspect=fit\" data-asset-id=\"97313375\" data-original-image=\"https:\/\/media1-production-mightynetworks.imgix.net\/asset\/1f7dc8ed-42ac-4a14-8fa4-fc367f8f42ab\/Picture6.png?ixlib=rails-4.2.0&amp;fm=jpg&amp;q=75&amp;auto=format\" \/><\/a><\/p>\n<p id=\"isPasted\">Figure 6: The \u201cO.R.A.N.G.E.\u201d model of a data management capability.<\/p>\n<p>Data lifecycle management is the core capability that delivers business value for an organization\u2019s stakeholders. Business architecture and data governance are strategic capabilities that define the direction of data management development. Data governance is a special capability. The title \u201cdata governance\u201d incorrectly reflects this capability\u2019s real role. This capability governs data management, not data. Its core task is to establish data management as a business function applying a data management framework and then control data management function operational efficiency and effectiveness. Data governance does it by controlling the establishment of the data management organizational structure, processes, policies, and tools and ensuring resources for all data management sub-capabilities.<\/p>\n<h5 data-fontsize=\"26\" data-lineheight=\"39px\">Practice<\/h5>\n<p>Several strategies (1, 3, 4) refer to themselves as a framework. It looks like the organizations did not use any industry framework and tended to develop their frameworks to meet their goals.<\/p>\n<h4 data-fontsize=\"20\" data-lineheight=\"28px\">Topic 6: The scope of the data management capability<\/h4>\n<h5 id=\"isPasted\" data-fontsize=\"26\" data-lineheight=\"39px\">Theory<\/h5>\n<p>The \u201cO.R.A.N.G.E.\u201d data management model demonstrates the core data management sub-capabilities. The most important thing is that all data management sub-capabilities are interrelated. Organizations that started implementing data quality as their first initiative have a high probability of failing. In order to properly manage data quality, an organization must have data governance and data and application architectures, including data modeling and metadata management, including data lineage, etc. This is something that many data management professionals do not realize. This situation is partly due to the DAMA-DMBOK2 approach. This guideline creates the impression that all Knowledge Areas can be implemented independently. However, I have to give it my due that on page 38, the authors stated the following: \u201c<a href=\"https:\/\/www.dama.org\/cpages\/body-of-knowledge\" target=\"_blank\" rel=\"noopener nofollow\" class=\"external\">None of the pieces of existing DAMA data management framework describe the relationship between the Different Knowledge Areas<\/a>.\u201d<\/p>\n<p>The rule of scoping the data management capabilities is simple: the business drivers identified in the strategy section \u201cWHY?\u201d will define the set of required sub-capabilities. The level of development of these sub-capabilities will depend on an organization\u2019s resources.<\/p>\n<h5 data-fontsize=\"26\" data-lineheight=\"39px\">Practice<\/h5>\n<p>Read further:\u00a0<a id=\"isPasted\" href=\"https:\/\/datacrossroads.nl\/2024\/03\/18\/a-data-strategy-theory-vs-practice-part-2\/\" target=\"_blank\" rel=\"noopener noreferrer nofollow\" data-fr-linked=\"true\" class=\"external\">https:\/\/datacrossroads.nl\/2024\/03\/18\/a-data-strategy-theory-vs-practice-part-2\/<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>This is Part 2 of the article \u201cA Data Strategy: Theory vs. Practice.\u201d In Part 2, we will continue the analysis of data strategy examples and discuss the following: A data (management) strategy content: recommended vs. presented in strategies mentioned above (sections 2 and 3) Recommendations for developing a (meta)data (management) strategy A data (management) [&hellip;]<\/p>\n","protected":false},"author":481,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[11],"tags":[],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v23.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>A Data Strategy: Theory vs. Practice. 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The key areas of her professional expertise are the data management maturity assessment, implementation of data management frameworks, and data lineage. Irina has practical experience in software implementation such as ERP and DWH\/BI, management consultation, financial and business controls, and data science.\",\"url\":\"https:\/\/solutionsreview.com\/thought-leaders\/author\/irina-steenbeek\/\"}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"A Data Strategy: Theory vs. Practice. Part 2 - Solutions Review Thought Leaders","robots":{"index":"noindex","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"og_locale":"en_US","og_type":"article","og_title":"A Data Strategy: Theory vs. Practice. 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