By: Ernst Renner, CEO & Managing Partner
Two weeks ago, I had the opportunity to speak on a panel regarding data management and governance adoption and business alignment at the Financial Information Management (FIMA) Conference in Boston. It was a pleasure being able to team up with other execs from innovative companies to discuss our ideas about delivering value to the business through data strategies.
I closed my comments with three takeaways that I thought meshed with my colleagues’ comments and were also net-new to what the attendees had heard whilst at the conference. I’ve since been asked by multiple people to elaborate on those takeaways, and I thought I would attempt to do so here.
1. Associate data management goals and tactics directly to business aims and value delivery. It is simple to pencil in that data management action is being undertaken for somewhat broad and generic efforts: to support compliance, make analytics usable, and catalog data. That simplicity also makes data management activities, especially data governance, a big target to strike out when budgets are being considered, and requests for participation ignored when adoption should be critical. Make 100% sure that your data management tactics have clear lines of sight to tangible, simple, and understandable business outcomes. Sought after types of examples could be: identify inefficiency for NIGO processing with a KPI of 25% reduction, self-service reporting for plan sponsors to reduce call center requests by 3.5%, increase producer effectiveness for product X by >4.5%. In other words, look for specific, metric-based, goals that you can point to definitively with your focused efforts.
2. Implement and enforce data governance in practical, incremental steps, when ingesting large amounts of data. I am not referring just to project work. In the current and foreseeable future, we will be required to consume vast amounts of data as quickly as possible. So, how do you govern that data? Practically, you cannot identify, define, negotiate, certify, and publish as you might with your internal, enterprise, data (even doing that, IMO, can be an easy waste of time and cost; another post for another day perhaps) “in the good old days”. The concept of “elastic governance” should apply. This is the act of fast, light, identification of data upon ingestion, that is built upon and further certified as it gains intrinsic business value; i.e. as that data is curated and used for “consumption.” Think of it as applying governance in layers as the importance of that data is realized. This approach is practical; you are handling what you can handle, and you are paying for data certification as the need and value increase closer to consumption and use.
3. Consider process governance in lock step with data governance. Process drives business and data is what gives that process meaning. If we take the time to govern the quality, usability, and security of data, we should pay equal attention to the processes that use it. It, therefore, makes sense that when there are quality issues with data, that the supporting business processes be evaluated for improvement. Just doing that exercise, in that order, will lead to system improvements if necessary. Minimally, this leads to a better operating environment that your business partners will appreciate. Once you have this bond with your business partners, it will be much easier to broaden opportunity for you and your team.
I’d love to hear your thoughts or questions about these points. If you would like, please feel free to leave a comment below.