Data Governance Core Team
- The data governance leader (DG lead) is the sole decision maker related to governance strategy and execution decisions.
- Data governance analysts should be engaged to support the DG lead based upon organisation size.
- Team member(s) should develop an enterprise view, based upon performing a survey across the organisation. That collateral is used to build a foundational approach for developing data governance activities.
Data Governance Council
A council of business and IT leaders drives data governance and management decisions. There are three main responsibilities for members on the data governance council.
- Provide recommendations related to decisions which will ultimately be made by the DG leader.
- Advocate on governance topics to their teams.
- Raise any items requiring governance discussion (e.g., issues identified, or challenges with underlying data set usage).
Data Owners and Data Stewards
The underlying business unit or department representatives (data owners as accountability and management leads and data stewards which generally support the execution of required tasks), from both the business and IT, are responsible for executing on day-to-day activities associated with data usage standards, data consistency, and ultimately data quality monitoring.
These roles are necessary to ensure data governance goals are clearly defined, vetted, and executed upon. Furthermore, it is critical that all users of data in-scope for governance activities are given the right level of awareness and training related to any governance items developed.
Adopting Sprints for Quicker Wins
Properly defining the scope of data governance is critical for the success of the overarching programme. For example, a two-month period of activity proves to be a good yardstick to establish and organise the foundation. This period is intended to act as a base to help survey organisations in a timely fashion and may need to be adjusted based on complexity and organisational size. Ultimately, we recommend this process can be timeboxed to not exceed one quarter’s worth of discovery, as information gathered should be surface level as deeper information related to each group is always subject to change over time.
The first month of post-foundation should be focused on an identified issue for a manageable data set as decided by the DG leader, based upon advice recommended from the DG council (e.g., one table within a data warehouse, one report, etc.) — this month will act as the first ‘data area sprint’ and clear timelines should be established to ensure takeaways and next steps are documented at the end of the sprint. Furthermore, the structures that are applied should continue past the end date of the sprint – this just indicates the time where data stewards are no longer focused directly on investigation and process adjustment areas. Post an initial sprint, a separate (similarly small, but significant) area should be evaluated for a one-month period.
After two complete sprints, the team should focus its third sprint on identifying ways to augment automation and/or additional analytics/reports to proactively identify issues based upon lessons learned. This pattern can ensure that data governance is created from an enterprise perspective but is applied in a localised manner where change agents are identified and prioritised. Ultimately, this will allow for the organisation to have quicker wins (and occasionally losses) that will allow for tangible examples of the benefits of data governance performed within earlier iterations, while also allowing for quicker adjustments or pivots that should be managed if challenges are discovered.
Agility Allows for Flexibility
Stronger governance implemented in an agile manner will allow organisations to focus attention on their core business drivers. Ultimately, agile concepts allow organisations the ability to baseline the most meaningful and impactful data sets — this does require upfront attention and time; however, it saves significant effort going forward as the structure and foundation of data governance can be leveraged on all projects and changes in the future, bolstering sustainability within the data governance model.
How Protiviti Can Help
Managing the amount of data being collected and utilised while protecting against potential vulnerabilities and data loss, takes strong data governance and management processes to help organisations continuously monitor the effectiveness of their data governance policies
Our subject matter experts work with our clients to customise a repeatable approach to data governance activities that will support each organisation’s goals. While we work first to make sure processes are independent of tools, we do have technology partnerships that can help optimise a data governance programme once fully defined while accelerating future growth and business value.
Our specific capabilities include:
- Assessing your current data governance processes and determining the maturity level of your programme
- Developing a practical data governance roadmap that you can easily follow
- Evaluating the quality of your data providing visibility into data quality challenges and mechanisms to track, report and fix issues
- Establishing a data champion network to ensure proper business involvement
- Carving out bite-sized segments of prioritised data to focus on short, iterative cycles enabling the most meaningful data sets to receive governance quickly
- Staffing support resources for business-as-usual functions or gaps in your programme to meet your on-going needs