{"id":5331,"date":"2021-03-02T11:16:57","date_gmt":"2021-03-02T11:16:57","guid":{"rendered":"https:\/\/www.theoaklandgroup.co.uk\/?p=5331"},"modified":"2024-02-21T15:46:30","modified_gmt":"2024-02-21T15:46:30","slug":"how-to-build-a-data-governance-program-by-stealth-introducing-the-lighthouse-projects-concept","status":"publish","type":"post","link":"https:\/\/weareoakland.com\/blog\/how-to-build-a-data-governance-program-by-stealth-introducing-the-lighthouse-projects-concept\/","title":{"rendered":"How to build a Data Governance Program by Stealth: Introducing the Lighthouse Projects Concept"},"content":{"rendered":"<p><span style=\"font-weight: 400;\">Chances are, you&#8217;re reading this article because you&#8217;ve been asked to launch, or support, some form of data governance initiative.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Firstly, congratulations. You&#8217;re embarking on a worthy endeavour.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Here at Oakland, our <\/span><a href=\"https:\/\/www.theoaklandgroup.co.uk\/data-strategy-governance\/data-strategy\/\"><span><span style=\"font-weight: 400;\">Data Governance Team<\/span><\/span><\/a><span style=\"font-weight: 400;\"> have trodden a similar path as we guided our clients across multiple data governance implementations. The results have been profoundly beneficial; often in ways the organisation had never considered at the outset.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Secondly, having delivered a number of these programs, we&#8217;re in the fortunate position to look in the rear-view mirror and identify those critical-path activities that were the keys to success. We&#8217;ve curated several of these learnings in this article; the first in a series of best-practices we&#8217;re keen to share with our clients and community.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Finally, we wanted to provide a &#8216;warts and all&#8217; account of what you&#8217;re about to experience as you embark on a data governance journey. As you&#8217;ve no doubt experienced, every project is unique, so if you would like to discuss how our approach and experience could help within the context of your data initiative, please book a <\/span><a href=\"https:\/\/www.theoaklandgroup.co.uk\/get-in-touch\/\"><span><span style=\"font-weight: 400;\">discovery call<\/span><\/span><\/a><span style=\"font-weight: 400;\"> to discuss further.<\/span><\/p>\n<h2><span style=\"font-weight: 400;\">Building the foundation for a successful data governance program<\/span><\/h2>\n<h4><span style=\"font-weight: 400;\"><br \/>\nWhat motivates the next generation of Data Governance stakeholders?<\/span><\/h4>\n<p><span style=\"font-weight: 400;\">We typically see the demand for an enterprise-wide data governance process initiated as part of a broader data foundation or enterprise-wide digital transformation. Many clients we work with are keen to embark on their data journey in a quest for monetisation and the creation of new opportunities through innovation.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">There is a growing realisation that better enterprise data leads to increased business performance, smarter decision-making and significantly reduced risk. As a result of these strategic triggers, we increasingly find executive stakeholders reaching out for guidance.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Making enterprise data management changes centrally in these organisations can be a challenge because they are often organised around different segments or geographies. There is also a lot of autonomy in large organisations, so to make changes centrally you&#8217;ve got to be business-led.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">People have to see the value in the changes you want to make.<\/span><\/p>\n<h4><span style=\"font-weight: 400;\">The Data Governance Process: &#8216;What vs How&#8217;<\/span><\/h4>\n<p><span style=\"font-weight: 400;\">If you&#8217;ve spent time sifting through the vast amount of books, articles, webinars and frameworks on the topic of data governance, you&#8217;ll soon realise that the &#8216;whats&#8217; of data governance are reasonably well understood and readily available. Most organisations we speak to know <\/span><b><i>what<\/i><\/b><span style=\"font-weight: 400;\"> is required to get the basics of data governance in place.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">However, particularly with larger, federated businesses, the translation from the &#8216;what&#8217; into the &#8216;how&#8217; is where many data governance programs begin to struggle.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">This question of <\/span><b><i>how<\/i><\/b><span style=\"font-weight: 400;\"> to deliver data governance in a way that engages and motivates the business, whilst simultaneously laying a future data governance foundation, is a challenge we&#8217;ve obsessed over for many years at Oakland.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">It\u2019s through this obsession that led us to the realisation that many business cases are not positioned correctly to launch a successful platform for data governance.<\/span><\/p>\n<h4><span style=\"font-weight: 400;\">Re-thinking the data governance business case<\/span><\/h4>\n<p><span style=\"font-weight: 400;\">In the early beginnings of our data governance practice, we would collaborate with clients to build defensible business cases for data governance, often as as a request to help construct a large data transformation program.<\/span><\/p>\n<h6><strong>The Data Management fundamentals<\/strong><\/h6>\n<p><span style=\"font-weight: 400;\">Large-scale data and digital transformation programs typically incorporate a broad swathe of data fundamentals such as:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Data Strategy<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Master Data Management\u00a0<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Data Analytics (Business Intelligence, Data Warehouse, Data Lakes)<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Data Quality Management<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Metadata Management and Data Lineage<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Data Stewardship<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Data Protection and Regulatory Compliance<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Big Data<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">If you&#8217;re embarking on a similar data transformation, there are comprehensive frameworks such as <\/span><a href=\"http:\/\/dama-phoenix.org\/wp-content\/uploads\/2015\/09\/DAMA-Phoenix-DMBOK2.pdf\"><span><span style=\"font-weight: 400;\">DAMA DMBOKv2<\/span><\/span><\/a><span style=\"font-weight: 400;\"> that provide a comprehensive &#8216;what&#8217; list of data elements required for the &#8216;mega-transformation&#8217; projects.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Whilst it seems logical to combine data governance with all the other elements of a large-scale data transformation journey, you will often bump into challenges when constructing the data governance business case and getting it approved.<\/span><\/p>\n<h6><strong>Challenges with the Data Governance business case<\/strong><\/h6>\n<p><span style=\"font-weight: 400;\">Some of the issues you&#8217;ll face when constructing a data governance business case may include:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Cost Quantification<\/b><span style=\"font-weight: 400;\">: The costs associated with poor data quality are difficult to quantify (they are often not even recognised, let alone measured)<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Limited Interest<\/b><span style=\"font-weight: 400;\">: Things that could easily be measured (e.g. cost of excessive manual cleansing, customer complaints leading to administrative overhead) may not generate a sufficiently compelling business case<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Lack of Attribution<\/b><span style=\"font-weight: 400;\">: It can be challenging to attribute any savings realised to a particular business unit (e.g where data quality improvements are made spanning multiple central and local departments)<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Excessive Complexity<\/b><span style=\"font-weight: 400;\">: Identifying broader use cases for new revenue opportunities from data exploitation requires detailed technical and business understanding (e.g. building a single customer view or Master Data Management (MDM) capability will provide benefits, but they&#8217;re a non-trivial exercise)\u00a0<\/span><\/li>\n<\/ul>\n<h6><\/h6>\n<h6><strong>The 3 pillars of a successful Data Governance business case<\/strong><\/h6>\n<p><span style=\"font-weight: 400;\">To address the previous challenges, we discovered that a more pragmatic approach is to build a data governance business case with a simpler, three pillar approach:<\/span><\/p>\n<ol>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Limit the early investment<\/b><span style=\"font-weight: 400;\"> \u2013 plan a modest level of investment, resisting the urge to build an &#8216;all-encompassing&#8217; data governance program<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Focus on value creation<\/b><span style=\"font-weight: 400;\"> \u2013 deliver a series of tactical &#8216;Lighthouse Projects&#8217; that create value for the business<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Fly under the radar<\/b><span style=\"font-weight: 400;\"> \u2013 gradually establish your data governance program and enabling activity in &#8216;stealth mode&#8217;, locking in the gains of each Lighthouse Project as you progress<\/span><\/li>\n<\/ol>\n<h4><\/h4>\n<h4><span style=\"font-weight: 400;\"><br \/>\nLaunching your data governance framework with stealth: Applying the \u2018Lighthouse Project\u2019 approach<\/span><\/h4>\n<p><span style=\"font-weight: 400;\">When we switched to helping clients implement data governance through <\/span><b>&#8216;Lighthouse Projects<\/b><span style=\"font-weight: 400;\">&#8216;, we found that executives were more likely to get on board when they were investing in quick wins that delivered faster returns, as opposed to much larger data governance programs that often grapple for attention amongst so many other transformation programs.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Larger data governance programs often run into additional problems once they get past the business case phase, such as:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Interest stagnation:<\/b><span style=\"font-weight: 400;\"> Too much emphasis is placed on building the data governance foundation, at the expense of solving problems that are attractive to the business.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Lack of ownership:<\/b><span style=\"font-weight: 400;\"> Data cuts across organisational boundaries, so this requires new data ownership and accountability structures that are often lacking from data governance initiatives.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Hidden data risks:<\/b><span style=\"font-weight: 400;\"> It can be challenging figuring out where the risks are within your present data landscape, and which ones require attention.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Lack of adaptation<\/b><span style=\"font-weight: 400;\">: You can&#8217;t copy and paste a data governance framework in the hope that &#8216;one-size-fits-all&#8217;. Instead, you need to adapt your implementation with a mix of different tactics over the long-term.<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Through the delivery of high-value Lighthouse Projects, the foundations of your data governance framework can start to be deployed, but without the burden and resistance associated with a much larger program.<\/span><\/p>\n<h2><span style=\"font-weight: 400;\"><br \/>\nSetting up your data governance strategy for success<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">We typically aim to apply a 3-stage process when our <\/span><a href=\"https:\/\/www.theoaklandgroup.co.uk\/data-strategy-governance\/data-strategy\/\"><span><span style=\"font-weight: 400;\">data governance team<\/span><\/span><\/a><span style=\"font-weight: 400;\"> works with clients:<\/span><\/p>\n<h4><span style=\"font-weight: 400;\">Stage 1: Set the Direction (Defining the Data Governance Strategy)<\/span><\/h4>\n<p><span style=\"font-weight: 400;\">The best approaches to data governance and the &#8216;exploitation&#8217; of data must first seek to capture &#8216;management intent\u2019. Once this is understood, you can then &#8216;smuggle&#8217; data governance into the business using projects that engage and excite people whilst laying the necessary real-world data governance policies and supporting structures, such as data stewardship and accountability frameworks for data stakeholders.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Technical skills and tools are important but should support, not lead, your data governance strategy. For example, a data governance tool is meaningless if you don&#8217;t have a committed and trained group of data stewards capable of building out a corporate data stewardship capability.<\/span><\/p>\n<h6><strong>Key Dimensions of Data Governance Strategy Readiness<\/strong><\/h6>\n<p><span style=\"font-weight: 400;\">There are three key dimensions that will provide the focus for defining the data governance strategy:<\/span><\/p>\n<ol>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Business Capabilities:<\/b><span style=\"font-weight: 400;\"> Understand current business priorities for data and test coherence through policies, governance and leadership structures.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Operational Capabilities:<\/b><span style=\"font-weight: 400;\"> Sample key processes and data lifecycles to determine alignment with key data management principles. Gather evidence of operational behaviours, both positive and negative.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Data Capabilities<\/b><span style=\"font-weight: 400;\">: Understand the data architecture and its footprint, together with any existing data management capabilities (e.g. tooling for data quality, master data management, metadata, data maturity metrics, data dictionary of data assets, data definitions completeness, data domains under governance, data owners assigned to existing data stewardship controls etc.)<\/span><\/li>\n<\/ol>\n<h4><\/h4>\n<h4><\/h4>\n<h4><span style=\"font-weight: 400;\"><br \/>\nStage 2: Plan the Execution (Implement the Data Governance Strategy)<\/span><\/h4>\n<p><span style=\"font-weight: 400;\">By appling some intentional planning in a multi-faceted approach, you will be able to:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Balance the various leadership (and political) drivers, whilst establishing clear ownership of the key levers of change (e.g. financial approvals)<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Manage the narrative, particularly around the tendency for the inflated expectations that are so often associated large data initiatives<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Curate and support your portfolio of \u2018Lighthouse Projects\u2019 (POCs) on two-fronts:<\/span>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Business outcome success stories that everyone can get excited about<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Targeted interventions to address areas of poor practice\/high risk (e.g. regulatory compliance and data protection weaknesses)<\/span><\/li>\n<\/ul>\n<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Drive the necessary data governance policy, standards, frameworks and overall data governance process, that support the strategic direction of travel<\/span><\/li>\n<\/ul>\n<h6><strong><br \/>\nInputs to Stage 2<\/strong><\/h6>\n<p><strong>To prepare your Stage 2 planning activity, you will typically require various supporting documentation and insights such as:<\/strong><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Existing data strategy and data foundation plans<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">The known &#8216;whats&#8217; of a preferred data governance process\/strategy<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Previous data maturity assessments, baseline reviews and data audits<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Any other supporting documentation<\/span><\/li>\n<\/ul>\n<h6><strong>Preparing the Data Governance Strategy Plan<\/strong><\/h6>\n<p><span style=\"font-weight: 400;\">During this phase, we would typically expect to carry out the following activities:<\/span><\/p>\n<ol>\n<li><b>In-depth interviews<\/b><span style=\"font-weight: 400;\">: Exploring possible root-causes of issues and areas of opportunity<\/span><\/li>\n<li><b>Landscape review<\/b><span style=\"font-weight: 400;\">: Investigate known data-related initiatives to identify focus areas and avenues for collaboration\/harmonisation<\/span><\/li>\n<li><b>Deep dives and sampling<\/b><span style=\"font-weight: 400;\">: Reviewing the key data lifecycles and data management processes to identify good\/poor practices as a reference point<\/span><\/li>\n<li><b>Data management maturity review<\/b><span style=\"font-weight: 400;\">: Provide a baseline <\/span><a href=\"https:\/\/www.theoaklandgroup.co.uk\/data-insights-applications\/ml-ai-analytics\/\"><span><span style=\"font-weight: 400;\">data maturity assessment<\/span><\/span><\/a><span style=\"font-weight: 400;\"> through quantitative and qualitative metrics, covering all aspects of people, <\/span><span style=\"font-weight: 400;\">process, systems, technology and data governance (at Oakland, our clients leverage our proprietary <\/span><a href=\"https:\/\/www.theoaklandgroup.co.uk\/data-insights-applications\/ml-ai-analytics\/\"><span><span style=\"font-weight: 400;\">rapid data maturity assessment<\/span><\/span><\/a><span style=\"font-weight: 400;\"> &#8211; to request details <\/span><a href=\"https:\/\/www.theoaklandgroup.co.uk\/get-in-touch\/\"><span><span style=\"font-weight: 400;\">click here<\/span><\/span><\/a><span style=\"font-weight: 400;\">)<\/span><\/li>\n<li><b>Central capability review: <\/b><span style=\"font-weight: 400;\">You will need to review the available capacity and capability of your central data function against their ability to deliver on your vision for data.<\/span><\/li>\n<\/ol>\n<p><span style=\"font-weight: 400;\">Following this initial preparation work, you will have some clear indication of where the major gaps, opportunities and readiness exists within your current data landscape.<\/span><\/p>\n<h6><strong>Creating the \u2018Commissioning Document\u2019<\/strong><\/h6>\n<p><span style=\"font-weight: 400;\">At Oakland, we recommend creating a &#8216;commissioning document&#8217; that aligns senior management and various stakeholders around a set of common goals and plan to move forward.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">This document is likely to include:<\/span><\/p>\n<ul>\n<li><b>Components<\/b><span style=\"font-weight: 400;\"> of the overall data governance strategy with an order and intensity in which they should be progressed<\/span><\/li>\n<li><b>Assessment<\/b><span style=\"font-weight: 400;\"> of the central support required (e.g. additional comms, policy development, standards)<\/span><\/li>\n<li><b>Identification<\/b><span style=\"font-weight: 400;\"> of any technical needs, beyond the POCs (e.g. Master Data Management (MDM), Data Quality, Metadata and Data Dictionary for Data Definitions and Data Lineage etc.)<\/span><\/li>\n<li><b>Key delivery<\/b><span style=\"font-weight: 400;\"> risks and issues<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">The plan needs to be resilient enough to cope with any organisational or leadership changes, and coordinated in a way that is most likely to succeed in the medium-term.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Typically, a core team will continue to support delivery of the plan as it rolls on into future phases.<\/span><\/p>\n<h4><span style=\"font-weight: 400;\"><br \/>\nStage 3: Executing your Lighthouse Projects (Proofs of Concept):<\/span><\/h4>\n<p><span style=\"font-weight: 400;\">For the remainder of this article, we&#8217;re going to focus on the process of launching &#8216;Lighthouse Projects&#8217; because we find that when executed correctly, they form the driving force for an effective data governance strategy.<\/span><\/p>\n<h2><span style=\"font-weight: 400;\">A deeper dive into the benefits of Lighthouse Projects<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Lighthouse Projects (POCs) form a crucial component of your overall data governance strategy as they engage the business in tactical improvements that capture attention. These improvements create success stories that build momentum, reduce risk and establish confidence in your approach.<\/span><\/p>\n<h4><span style=\"font-weight: 400;\">Overcoming Resistance to Data Management Change<\/span><\/h4>\n<p><span style=\"font-weight: 400;\">Many organisations suffer from &#8216;change fatigue&#8217;.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">When starting out, you&#8217;re going to be &#8216;smuggling&#8217; data governance into the organisation so make sure your project&#8217;s mission is 100% focused on the &#8216;pains and gains&#8217; that those in the business already recognise.<\/span><\/p>\n<h6><strong>The first rule of Data Governance \u2013 don&#8217;t talk about Data Governance<\/strong><\/h6>\n<p><span style=\"font-weight: 400;\">You don&#8217;t necessarily want to label your first Lighthouse Project as a &#8216;Data Governance Initiative&#8217; because you&#8217;ll often face enough resistance as it is, without introducing a phrase that no-one in the business understands.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">For example, with one client, we selected a project that addressed an asset data quality headache that had been frustrating the business for some time.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">We could have adopted a top-down approach and waited to build out accountability structures for data governance, create data quality assessment frameworks, train the business in data literacy and select a team of data stewards to manage the information chain.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Instead, we addressed the root-cause of why the data asset system was creating poor data quality.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">We then introduced a system for onboarding data stewards, discovering metadata, defining data quality metrics and controls \u2013 all the elements of a data governance capability, but in a much shorter timeframe than if we had waited for a full data governance program to be rolled out.<\/span><\/p>\n<h4><span style=\"font-weight: 400;\">Which data management improvements should you consider for a Lighthouse Project?\u00a0<\/span><\/h4>\n<p><span style=\"font-weight: 400;\">When considering the monetisation opportunities of your Lighthouse Projects, we recommend &#8216;two waves of attack&#8217;.<\/span><\/p>\n<h6><strong>Wave 1: Initial assessment against an agreed project selection criterion<\/strong><\/h6>\n<p><span style=\"font-weight: 400;\">This phase should identify a prioritised list of candidate POCs drawing on the selection criteria previously discussed. For example, you may collapse a list of candidate projects from 20 down to 5 possible options.<\/span><\/p>\n<p><strong>Sample criteria and considerations for Lighthouse Project selection<\/strong><\/p>\n<p><span style=\"font-weight: 400;\">Every organisation has scope for data improvement, so your initial challenge will be determining which Lighthouse Projects should be candidates for consideration.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Having delivered many of these projects, we&#8217;ve found the following advice to be a good starting point for selecting the best data management\/business improvement projects to kick off your strategy of &#8216;data governance by stealth&#8217;:<\/span><\/p>\n<ol>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Identify which projects are most likely to succeed<\/b><span style=\"font-weight: 400;\"> \u2013 reduce the scope rather than fail an overly ambitious target<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Clearly define the scope<\/b><span style=\"font-weight: 400;\"> \u2013 understand the specifics of the business problem being solved and its technical \/ data viability<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Focus on business-led projects<\/b><span style=\"font-weight: 400;\"> \u2013 look for projects that rely on active contribution from the business<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Look for balance across the POCs<\/b><span style=\"font-weight: 400;\"> \u2013 shortlist a &#8216;project portfolio&#8217; that will nurture good ideas and opportunities, whilst tackling areas of poor data practice<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Consider what happens at the end of the POC<\/b><span style=\"font-weight: 400;\"> \u2013 is the purpose to prove viability (and perhaps discard afterwards) or build a fully supported solution?<\/span><\/li>\n<\/ol>\n<h6><\/h6>\n<h6><\/h6>\n<h6><strong><br \/>\nWave 2: Investigation of each candidate POC<\/strong><\/h6>\n<p><span style=\"font-weight: 400;\">In this phase, you will start by validating project feasibility and scope, before arriving at an attractive set of options for more detailed investigation that will be completed at the end of Wave 2.<\/span><\/p>\n<p><strong>Investigation focus areas<\/strong><\/p>\n<p><span style=\"font-weight: 400;\">Each investigation will typically focus on areas such as:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Use case definition<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Business processes<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Technology<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Data availability<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Data quality and associated metrics<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Risks and challenges for delivery<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Delivery planning<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Upon completion, Wave 2 will select a focused set of projects that fully address several frustrating business problems. These projects will allow you to introduce key elements of your data governance roadmap as a side-benefit, but without the fanfare and resistance so often associated with a major data governance program.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Depending on the scale of each candidate project, it typically requires 10-20 days to complete the POC investigation.\u00a0<\/span><\/p>\n<p><strong>What team will you need for POC investigation?<\/strong><\/p>\n<p><span style=\"font-weight: 400;\">Within our <\/span><a href=\"https:\/\/www.theoaklandgroup.co.uk\/data-strategy-governance\/data-strategy\/\"><span><span style=\"font-weight: 400;\">data strategy and governance consulting practice<\/span><\/span><\/a><span style=\"font-weight: 400;\">, we typically deploy a core investigation team consisting of:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Business translator (SME)<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Business analyst<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Technical data architect<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">The exact make-up of the investigation team needs to adapt to the specifics of your data, process, and technology landscape.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">For example, some projects may be deeply technical in nature, requiring expertise in data interface accessibility, workflow automation and Big Data platforms.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Other times, the project requires &#8216;softer&#8217; skills in the form of data literacy at the grassroots level, educating a team of stakeholders to take accountability for decision-making, or simply coordinating a team of data stewards to set more robust data definitions and assessment metrics for their data assets.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The key is to work with a <\/span><a href=\"https:\/\/www.theoaklandgroup.co.uk\/data-strategy-governance\/data-strategy\/\"><span><span style=\"font-weight: 400;\">data governance team<\/span><\/span><\/a><span style=\"font-weight: 400;\"> who have the experience to address the complex and varied types of data challenges that typify the modern enterprise.<\/span><\/p>\n<h2><span style=\"font-weight: 400;\">Next Steps<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Building out a data governance program for the first time is a challenging undertaking.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Our recommendation is to first gain an overview of the &#8216;whats&#8217; of data governance and then seek an experienced implementation partner who can guide you through the process of delivering smaller, business-driven, &#8216;Lighthouse Projects&#8217; that deliver incremental data governance milestones.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Don&#8217;t be taken in by the industry trend of building out all-encompassing data governance frameworks until you&#8217;ve at least demonstrated repeated value from lightweight data improvement projects that help you &#8216;smuggle&#8217; in the fundamentals of data governance.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">You&#8217;ll find that when you come to expand your reach with data governance, you already have a captive executive audience who are primed and eager to support your continued success.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">If you want to discuss any of the ideas presented, and how they would benefit your data initiative, please <\/span><a href=\"\/contact-us\/\"><span><span style=\"font-weight: 400;\">contact us<\/span><\/span><\/a><span style=\"font-weight: 400;\"> to arrange a call.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Chances are, you&#8217;re reading this article because you&#8217;ve been asked to launch, or support, some form of data governance initiative. Firstly, congratulations. You&#8217;re embarking on a worthy endeavour.\u00a0 Here at Oakland, our Data Governance Team have trodden a similar path as we guided our clients across multiple data governance implementations. The results have been profoundly&#8230;<\/p>\n","protected":false},"author":5,"featured_media":5326,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","footnotes":""},"categories":[9],"tags":[7],"class_list":["post-5331","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-insights","tag-data-governance"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.2 (Yoast SEO v27.2) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>How To Build A Data Governance Program | Oakland<\/title>\n<meta name=\"description\" content=\"In this article we provide a full account of what it takes to deliver a Data Governance Program by stealth using the &#039;Lighthouse Projects&#039;.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link 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