For organizations dealing with massive volumes of product information, the need for data governance has never been more pressing. Organizations today need to constantly prioritize how they will use that data. They must ensure that it is handled consistently to support business outcomes. It is important to put the right data governance measures in place to ensure the product content meets precise standards and business rules.
With data governance, organizations can exercise the necessary control over the management of data assets. They can do that by using the right blend of people, process, and technology.
Today, how your business performs depends largely on how you handle the data collected within your organization. And that’s why it is important to embrace the apt data governance best practices. That will drive security and compliance while extracting the maximum value from all the product information being generated, collected, and stored across the business.
Best practice 1: Codify a data governance framework
Every data governance initiative must begin with the formulation of a robust data governance framework; a framework that describes the set of data rules, organizational roles, and processes aimed at ensuring availability, consistency, and relevance of data. The task starts with understanding what the mission of the initiative is. It extends to identifying key data owners and stewards, how the undertaking will be funded, how the organization will measure success, and how the entire initiative will be managed and accounted for. It is only by building a strong framework that you can derive the most value from your data governance efforts.
Best practice 2: Measure your organization’s maturity
When it comes to data governance, not all organizations are alike. While some have basic data measures in place, others follow a bevy of modern practices to ensure data consistency and quality. Before you begin devising your data governance strategy, start by measuring your organization against a data governance maturity standard. Identify whether your data governance is at a low level of maturity or at a sophisticated level. This can help you understand where you stand today with respect to data governance. It will also help in building a roadmap, so you know what measures you need to take in educating stakeholders, drafting strategies, winning approval, and overcoming the common challenges.
Best Practice 3: Build a business case
Implementing data governance across your organization takes a lot more than just a plan. Executive buy-in and C-suite support are key to ensure the success of such a far-reaching initiative. Make sure you build a strong business case for your data governance initiative; identify the benefits and opportunities that data quality will bring to your organization. As far as possible, quantify the ROI from the data governance efforts in terms of better customer experience, more sales, and increased revenue. A strong business case can not only help you set expectations but it will also help you capitalize on opportunities and build a solid foundation to scale the data governance initiative.
Best Practice 4: Measure, measure, measure
As with any other organizational strategy, it is critical for you to be able to track the progress of your data governance undertaking and plan for improvements. Every step of the way needs to be supported with data metrics that reflect the success (or failure) of your initiative. Make sure to identify KPIs from the start and transparently track and report them on a continuous basis. Such measurement will throw much-needed light on the challenges you are facing across aspects such as data collection, storage, consistency, availability, and more. Not only will these KPIs show the overall progress of the initiative but they will serve as checkpoints along the way to ensure that the data governance process is effective in delivering on its promise.
Best Practice 5: Integrate data governance with MDM
At the strategic level, data governance is important. But what really matters is how you execute the strategy. Integrating your data governance strategy with MDM will enable you to put your product data in charge of your digital strategies. From customers and partners to your products, services – it is when your master data ties into your data governance that you will be able to meet your data quality and consistency objectives with ease. This high-quality integration with MDM will help you to make the right data-driven decisions; so, make sure to use the right tools to enable the integration to harness the power of your MDM efforts.
Best Practice 6: Make it a continuous activity
Lastly, it is important to understand that data governance is not a one-time project. Your product data will keep growing in volume and variety. Success will drive more use-cases. This will make product data even more critical to multiple business strategies and operational processes. In that scenario, the real benefits of data governance can be achieved only when it is made an ongoing practice, something you do on a continuous basis. When you look at data governance as a fundamental change that is woven into the organization, that is when you can guarantee quality, completeness, validity, accuracy, timeliness, and consistency of your data, make the right decisions, and drive digital success.
With an increasing number of organizations struggling to make the right business decisions because of a lack of confidence in the data at hand, data governance is becoming critically important. Data governance can eliminate false facts and out-of-date data that can hamstring data-driven environments. Data governance ensures consistency and compliance with growing data standards and regulations. The adoption of data governance best practices will allow you to react responsively to dynamically changing market requirements, cater to the growing volumes of data, and gain from the full power of MDM across your organization.