Rapid growth can feel like a dangerous balancing act. Too many companies rise fast — and fall hard. Often, a lack of data governance strategy is to blame. We share straightforward tactics to help you quickly implement an agile data governance strategy.
If your company recently discovered the power of data-informed decision making, the possibilities can seem endless. The right data can help you understand and reach customers with precision, streamline processes, reduce overhead, boost output and more. Companies across industries and sectors, from big-box to mom-and-pop to the neighborhood kids’ lemonade stand, are moving fast to realize this potential. Organizations are experiencing unprecedented growth and working smarter than ever before. But how many have an agile data governance strategy in place? 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 — or no strategy — for how to govern company data. For example, the project team driving the company’s 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’re 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. Today, you need to interweave informatics and business strategy. Good business decisions rely on data, and good data require strong governance. Here’s how to help your teams walk the tightrope — and reach new heights — using agile data governance.
Data Governance Meets Agile
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. 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’s not perfect or 100 percent ready. You then take customer feedback — good, bad and ugly — 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. When it comes to internal-facing projects like data governance, think of teams and employees as the customer to please within the agile process. 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.The Problem with Traditional Data Governance
Unlike agile data governance, traditional data governance implementations tend to be slow, siloed and frustrating. Reasons for their failure can include:- Uninformed or disinterested leadership
- Unclear communication or goals
- Chaotic or inequitable division of labor
- Confusing rules and systems
- Inadequate amount of data experts
- Reactivity instead of proactivity toward the company’s data needs
- Governing data for singular projects instead of the company as a whole
- Siloed governance practices.
Learn to Balance with Agile
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. 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’s bad business. To succeed, leadership, project managers and informatics teams need to synergize. 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. 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.Don’t Let Perfect Be the Enemy of ‘Good’
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. Another benefit to working in sprints — and testing your work, cleaning it up and testing it again — means you don’t 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’s okay. Set your priorities — and just start. With agile, all the pieces will eventually fall into place.Implementing Agile Data Governance
You will notice important differences in how project vs. data governance teams implement agile methods, but many features will remain the same. 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. However, the story arc, or theme, will vary and take longer than a two- or three-week sprint. Fortunately, the steps of data governance implementation 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. You can customize this approach according to your organization’s 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.
The template is based on work by John Ladley in, “How to Design, Deploy, and Sustain an Effective Data Governance Program,” 2nd Edition, 2020.
Walk the Tightrope
When bringing teams together to pursue an agile data governance strategy, it can help to use a work management system and visuals to show project phases and timelines. Below is only one example.
Notice how it breaks down a data governance implementation plan according to an overall timeline and individual sprint sequences. See how each sprint has dates?
The circle sizes indicate the amount or scale of work to be done. Within circles, font color helps to break down which team is responsible for each deliverable. As you can see, tasks sometimes overlap for scheduling flexibility. This is meant to ensure work may continue in certain areas, even if it falls behind in others.
At the same time, the graphic depicts all data governance tasks in the timeline as sequential. That’s because some work is linear. Data governance tasks in particular tend to be dependent on earlier work. By showing these tasks in a straight line, it can help the project manager know which items their team will need to perform first. It can also help them to allocate resources appropriately within or across teams without negatively impacting others.
