In our four-part series, we’ll explain the different components necessary to carry out an effective data governance policy and how dbSeer can help further level up your business. This blog post is the second in the series, it explains the importance of developing a process in constructing a data governance policy for your company. The first part of the series, that defines what data governance is, can be found here.
Developing an effective data governance strategy is key to a successful digital transformation. We live in a world where data growth is inevitable and only expected to increase as time goes on. A comprehensive approach to managing data through its life cycle therefore must be established and understood by both employer and employee alike. This means building out best practices for your data.
Table of Contents
Establishing Process
dbSeer has identified three components to implementing a proper data governance strategy: process, people, and policy. The three components working seamlessly alongside the technology is what comprises an effective data governance strategy.
On average, companies use only about 32% of their data, the remaining 68% is left unused, and more importantly: not taken advantage of. Therefore, it’s critical to identify the main obstacles to establishing a proper data governance management system and building the proper process accordingly. Strategic objectives can only be met with a proper data governance framework in place.
Process, in this context, is defined as understanding the tasks required to build your data governance strategy.
Process should be seen as the first step of multiple in your journey to creating a data governance strategy that works.
Process to Find Purpose
Process helps unpack the purpose behind data governance. This means your company must look at the big picture and be clear on its objectives. The scope of the data being used must be clearly defined to line up with the company goals. If this is not clear from the beginning, it will make achieving an effective data governance strategy impossible.
Once objective & scope are clearly defined, answering some of the questions below will help establish your data governance approach:
- Data Accessibility: Is your data readily available? How much of your data is accessible?
- Your data governance strategy should complement your overall data strategy, they should be working side-by-side.
- Ensure that the systems in place are working- there’s no point to having a data governance strategy if you have no data to govern. This includes, but is not limited to:
- Having proper documentation for your data
- Checking the readability of the data
- Ensuring effective data sharing is in place
- Data Policy: Are data definitions being upheld across the board?
- Your data strategy should be clearly defining what data means for every actor in your business. Your data governance strategy ensures that the definitions are being respected: does everyone understand how your company defines different types of data and their respective uses?
- This is also a good opportunity to take inventory of your data across your organization which can be done with technology such as data discovery tools or data mapping tools.
- Data Quality: How do you ensure the data quality is right? What procedures can be put in place?
- Has your company created a plan for data accuracy?. While achieving 100% data accuracy may not be attainable, it remains critical to measure data quality. This can easily be done with technology at your side, data quality tools can address data cleansing, data inconsistencies, data errors or necessary data updates
- Data Lifecycle Management: What is the life cycle of your data?
- Acquiring, using, and disposing of data is customized dependent on the need of the organization. Defining this approach is critical for your data governance framework and proper management of the data your company has acquired (and must dispose of over time.)
- Automating your data to respect these lifecycle measures should be done. Technology can help you with automated data archiving, scheduled deletion, or data backups.
- Data Governance Community: Who are the people on your team responsible for your data? What are their tasks?
- Governing data effectively is truly a collaborative approach, this requires building out an effective data culture with a proper team to support these data goals.
The answer to the final question may require a restructuring component to the way your team works or allocating a specific team to the tasks you’ve laid out. Our next blog piece in the series will unpack how best to build your team around the concept of proper data governance.
Ultimately, data governance is providing structure for the overwhelming amount of data your company handles on a day-to-day basis.
Choose dbSeer to Guide Your Data Governance Strategy
Establishing process is a first step towards understanding what is required to make your data work for you. Asking the above questions may leave you and your team feeling overwhelmed, and while the task may seem insurmountable at first: you don’t have to be alone doing it. dbSeer is equipped to help you understand your data needs, and guide you to the answers you need, and tasks you need to allocate to ensure you’re leveraging your data appropriately.
As a leading business analytic consulting firm that specializes in analytics, data engineering, infrastructure, and migration dbSeer has served as a crucial partner for myriad businesses in building out their data road maps.
dbSeer integrates itself seamlessly into your team to improve your business and data practices. Ensuring long-term success, dbSeer provides recommendations to achieve short-, medium- and long-term goals when it comes to your data management.
An effective data governance model is certain to deliver impressive results for your business. To take your company to the next level, place an inquiry with dbSeer today.
Make sure you tune in to our next Demystifying Data Governance blog post explaining how best to build a team and culture that understands how best to properly manage data.
