Data lies at the heart of digital transformation. Not all data. But data that’s easily accessible, high quality in nature and relevant. Unfortunately, without adequate organizational data governance measures, data can easily devolve into data for data’s sake. Something that degenerates into policies and guidance made into a support function executed by organizational IT teams, and not to mention not widely followed organizationally.

Therefore, high-quality data governance ensures organizations don’t miss out on data-driven opportunities. In addition, well-governed, maintained, clean and processed data brings indirect value. By leveraging this and putting it to work, larger organizations can eliminate costs by millions.

How does an organization drive excellent data-governance practices? In this blog, we will explore a few ideas.

1. Sign-off at the highest level

Significant C-suite level buy-in is essential. The designated Data personnel has to engage the top management, translate current data challenges and advocate the need for efficient data governance practices. Ideally, this person may also explore the formation of a data-governance council that has, in its ranks, senior management leaders. This has benefits in terms of the initiative visibility and gearing the governance strategy to the business’s top priorities. Well-aligned data governance initiatives get quicker approval at all levels.

Once approved, the assigned data management personnel must brainstorm with the C-suite to define data domains. Next, executives have to be selected to lead these domains. This ensures the day-to-day is taken care of. Data elements are defined.

In addition, the benefits of this distributed approach appear in the form of a decentralized approach to data governance. Senior management’s mandate is to ensure that selected leaders are afforded enough time to pick up their responsibilities. New responsibilities must be reflected in these leaders’ KRAs generating interest and effort to fulfill these parameters. The value generated is long-term. Over time, the leaders will understand regulations and data architecture elements.

This is possible only with executive buy-in at the highest possible levels. Data governance can be executed right only with a top-down approach. 

2. Synergy with larger transformation goals

Pre-existing digital transformation efforts within an organization provide the easiest clues to the firm’s priorities. These efforts come with priorly obtained executive support. Therefore, linking data governance initiatives with these transformation goals ensures success, adequate resource allocation and executive attention. Finding these synergies may require alignment from the individual entrusted with data governance. One of the ways to achieve this is by engaging with product owners who are leading/supervising the digital transformation push within the organization.

3. Assign hierarchies to data assets right at the top

It is key to define the scope of the data governance efforts right at the onset. A holistic, all-around look at organizational data governance may be ideal and feel like the right thing to do to start with. But it is important to pause and ask if the governance efforts are tied to business needs? Will they prove ROI positive in the short or long term? 

On the other hand, a laser-sharp focus on data assets – prioritized by domains and serving specific business needs, may see an ROI sooner. In addition, such initiatives are easier to explain to executives and achieve necessary buy-ins.

Plus, other factors like transformational efforts and regulation needs should be considered before creating a domain-wise data governance map. Once prioritized, roll out of priority domains can serve as a beacon to subsequent efforts. Further, this approach allows these domains the required months to be fully functional after the efforts have been expended in the right direction.

Let’s say a Jamaican F&B company aims to transform itself with advanced analytics. The company’s current data is sizable. It may even be a hurdle, given its size and historical relevance. So, the company decided to establish a Data Management Office (DMO) that will study the nature of the data and, based on results, provide a set of data domains. The DMO works to identify ten domains across the enterprise. Of these, it prioritizes three domains – Vendor Data, Transaction Data and Product Data. The next step is to break down the domain further. For example, Product Data could be broken down into different types of products by use-case/price/or any other relevant differentiator. Or, Vendor data could be broken down into Temps/Permanent Partner Level Vendors/or other such differentiators.

Doing this gives the company a set goal of sorting data for three domains and achieving trackable results in the first six months. Data governance can be overwhelming for beginners, especially those with tons of legacy data gathered over the years. But a priority-based approach can help simplify matters.

4. Compliance

Historically, one chief driver of data governance initiatives were regulatory needs. Regulatory needs can vary from industry to industry. For instance, the banking industry faces more stringent regulatory mechanisms. However, many industries are not subjected to the same levels of stringency. Therefore, it is good to adopt a ‘needs-based’ approach to data governance efforts in compliance with the existing regulatory framework in the said industry. It also depends on the type of organization under consideration. Organizations that operate multiple, independent businesses in many countries may have more complex needs. In contrast, organizations with simpler structures may have simpler requirements. 

5. Incremental, iterative implementations

Bespoke priorities are needed to suit the domain and context in question to extract the most value from data governance efforts. Incrementally, the efforts can be iterative for quick adaption. Priority use cases can be given a push even if the entire solution is nowhere near perfect. A tailored approach will accelerate efforts, solve pertinent problems, and develop quality incrementally. 

Argusoft, Your Technology Enabler

Argusoft understands data and how it has the potential to transform organizational synergies. We are working with multiple governments, government agencies, and worldwide organizations to strengthen their data governance. We can step in, quickly offer an analytical view of the pressing problems of organizations, and offer bespoke solutions that can be implemented swiftly to tackle the most pressing problems first. Then, eventually, we can suggest and architect a longer-term data governance paradigm that will stand the test of time. To discuss your data governance needs, feel free to consult with us.