“Data administration,” “data governance” and “data stewardship” are common buzzwords in the IT industry. Unfortunately, they are also commonly conflated and/or misunderstood. Let’s change that.
The last two decades have brought fundamental shifts in the standard operating procedures of large organizations, perhaps none more significant than the establishment of information systems to automate processes and handle massive amounts of data. But merely creating the bandwidth to handle data isn’t enough; there needs to be a way to make it actionable. That’s data administration.
Data governance and data stewardship fall under the umbrella of data administration. Data governance is the process of defining attributes, rules, metrics and standards to maintain order, efficiency and agility over data. It is important to think of data governance efforts as programs, rather than projects, since they are ongoing rather than having a specific end date – just like sales, production or any other activity in the organization. Data governance programs should become part of an enterprise’s DNA. Data stewardship, on the other hand, is the process of applying those metrics and attributes.
Data governance programs can be created by following these five steps.
Assign a Data Governance Team and Team Leader
Accountability is everything. Nine times out of ten, when a data project fails it is because no leader or team has been identified within the organization. Typically the leadership assignment goes to someone in the IT department, normally someone working with BI tools or data warehousing solutions. This is a common mistake. Organizations should appoint a person that understands the “big picture” of its business, including the nuances of its data. This person must champion the data governance program within the organization and be capable of changing the vision of it at the C-Suite level. It is no small job.
Determine the approach: Top-Down or Bottom-Up?
Data governance initiatives originate either from an executive mandate (“top down”) or as a grass roots reponse to tactical data challenges (“bottom up”). This is an important decision that will impact how quickly your data governance initiative progresses. The program will make much more progress with support from senior leadership, but sometimes it is required to establish the value of data governance with small wins at first. In either case, once the program is fully underway, the data team will then establish the focus and the efficient utilization of resources, as well as longer term objectives.
Identify the Data Object/Master Data
After determining which approach fits best, the organization needs to identify the scope of the governance organization, and needs to align the initial priorities to the strategic business objectives. This is critical for the success of the program. If a data object that has nothing or little impact within the organization is chosen, the project will not appreciably demonstrate ROÍ. Conversely, if an overly complex data object is chosen, the project will take too long to impact business in the short term.
Integrate with IT
Once the data object has been selected, the data governance team leader should connect with the organization’s IT leaders to identify the impact of the data object within the IT landscape. This will help the data governance team to determine timeline, applications involved in the process and the key users that participate in the creation and maintenance of the data. It will also pave the way to create policies, standards and metrics for the data creation. Once this task is accomplished, the need for a mater data governance tool can be determined.
Implement the Data Object Process
The final step is to implement the policies, standards, metrics and tools used in the data governance process. Carefully selecting these tools and defining the business process will ensure the success of the data governance program over time.