Remarkable growth can, at times, feel like walking a tightrope. The truth is that many companies reach great heights only to fall hard. For many, failure stems from having a haphazard strategy \u2014 or no strategy \u2014 for how to govern company data.<\/strong><\/p>\n For example, the project team driving the company\u2019s expansion may use and produce data without ever speaking to the informatics team or business execs about data strategy. This might not sound too bad if you\u2019re used to only calling IT when you spill coffee on your keyboard. But times have changed, and siloed IT is a thing of the past.<\/p>\n Today, you need to interweave informatics and business strategy<\/a>. Good business decisions rely on data, and good data require strong governance. Here\u2019s how to help your teams walk the tightrope \u2014 and reach new heights \u2014 using agile data governance.<\/p>\n Data governance refers to the creation of rules and processes based on strategy for how a company should collect, use and store their data. Project teams should include data governance strategy in all they do. Likewise, you can accelerate data governance implementations with project management approaches like agile<\/a>.<\/p>\n Agile involves bringing together your teams and stakeholders to design something called a minimally viable product (MVP). With agile, once you have an MVP in hand, you introduce it to your customers, even if it\u2019s not perfect or 100 percent ready. You then take customer feedback \u2014 good, bad and ugly \u2014 fix the product and release it again. Repeat until you reach a version your customers love. Even after formally launching a product, an Agile team will continue to refine it based on feedback over time.<\/p>\n When it comes to internal-facing projects like data governance, think of teams and employees as the customer to please within the agile process.<\/strong> Set your desired MVP as a working data governance strategy that you continuously refine based on employee feedback. Sprints and deliverables can include items like data governance policies, tools and processes.<\/p>\n Unlike agile data governance, traditional data governance implementations tend to be slow, siloed and frustrating. Reasons for their failure can include:<\/strong><\/p>\n Most organizations do not have a strong data governance plan in place. In fact, 90 percent of traditional data governance implementations fail<\/a> for the reasons listed above. Data governance is still a new and confusing process for many. It often takes a back seat to flashy, revenue-generating projects for business expansion.<\/p>\n Typically, a hierarchy prevails and the project management team will have access to key resources (people, funding, materials) first. Because of the challenges listed above, leaders may overlook the fact that project management<\/a> and data governance can and should happen together.<\/p>\n In many organizations, relationships are tense between the project team and the informatics team. Sound familiar? Conflict is almost inevitable when there are two (or more) teams that need the same resources at the same time and only one receives leadership support.<\/p>\n If leadership continues to place performance pressure on the under-resourced team, it can compound tensions further. This resource hierarchy not only places unnecessary stress on employees, it\u2019s bad business.<\/p>\n To succeed, leadership, project managers and informatics teams need to synergize.<\/strong> They need to use resources together for strategic purposes. Moving forward, all teams should walk the tightrope with the help of two balancing forces: Project management methods (agile) and data governance. Leadership strategy should always connect the two.<\/p>\n Imbalance often stems from a reluctance to involve the people who have to live with and carry out data governance strategy. Agile methodology solves for this by bringing teams together and engaging key stakeholders in project design. It encourages listening and dialogue. Mutual understanding, in turn, can lead to solutions that are grounded in reality.<\/p>\n Agile works well, in part, because it requires teams to complete sprints. These are set bursts of productivity, with deadlines for teams to produce, review and approve deliverables. Sprints are structured, with expectations set at the beginning of each sprint cycle. Teams share status updates using routine, centralized communications. This clarity boosts cohesion, and it inherently precludes many of the failure scenarios listed above.<\/strong><\/p>\n Another benefit to working in sprints \u2014 and testing your work, cleaning it up and testing it again \u2014 means you don\u2019t have to wait for perfection. You can start to use and find value in your data governance strategy right away. Data governance is expansive, ever-changing work. You won’t finish your checklist in one day, and that\u2019s okay.<\/p>\n Set your priorities \u2014 and just start.<\/p>\n With agile, all the pieces will eventually fall into place.<\/p>\n You will notice important differences in how project vs. data governance teams implement agile methods, but many features will remain the same.<\/p>\n For example, agile project work is typically broken down into big chunks, medium hurdles and bite-sized nuggets (called an epic, a feature and a story). You can also break data governance down into these components and manage them within a sprint structure.<\/strong> However, the story arc, or theme, will vary and take longer than a two- or three-week sprint.<\/p>\n Fortunately, the steps of data governance implementation<\/a> can follow a straightforward prescription, with repeatable tasks, deadlines, sequences and so on. The screen capture from a Microsoft Project template below shows one plan for implementing data governance.<\/p>\n You can customize this approach according to your organization\u2019s needs. Not all tasks will have predictable levels of effort or predictable sprint structures. They should, however, all adapt to real-time needs, such as shifting calendars. The key is to give internal deadlines far enough in advance of when deliverables are needed so teams have time to plan and adjust.<\/strong><\/p>\nData Governance Meets Agile<\/h2>\n
The Problem with Traditional Data Governance<\/h2>\n
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Learn to Balance with Agile<\/h2>\n
Don\u2019t Let Perfect Be the Enemy of \u2018Good\u2019<\/h3>\n
Implementing Agile Data Governance<\/h2>\n
![\"Agile](\"https:\/\/centricconsulting.com\/wp-content\/uploads\/2023\/05\/Agile-Data-Governance-300x294.jpg\")
<\/a><\/p>\n![\"Agile](\"https:\/\/centricconsulting.com\/wp-content\/uploads\/2023\/05\/Data_Governance_Sprint_Planning_05112023-1024x576.png\")
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