Contained in April’s editorial column of The Entrepreneur’s with editor Jason Feifer was the problem-solving framework that I came across. Jason writes about the monkey and the pedestal (M&P) framework. He asks you to imagine getting a crazy assignment at work. “You must teach a monkey to recite Shakespeare’s while standing on pedestal. What is the first you should do first? The most logical and general response you get is to build a pedestal.
Implementing data governance to solve a business problem is like what Jason says about the monkey and the pedestal. Putting data governance in practice is indeed a crazy assignment at work. Mostly, I observe people spending time building a pedestal. Why?
The corporate world treats Data Governance practice as a project, and when it comes to a project, we all have grown fond of MVP, the Most Viable Product. The MVP is the low-hanging fruit, the small and easy stuff that we hold pride in saying – These MVPs give us a sense of progress. These MVPs serve as an excellent example for Jason’s M&P problem-solving framework. When it comes to building a data governance practice, most simply focus on its capabilities in developing the data governance platform. Some of its pedestal-based initiatives are – standing up a business glossary, harvesting data dictionary, data lineage, and cataloguing endpoints. Once these pedestals are set up, the progress card is handed over to the sponsors, highlighting how well you have built the array of pedestals.
But the business and the program sponsors are looking for something else. Where is the monkey? Without focusing on the adoption of data governance, just building capabilities will not take you far. The solid core that brings value from building the data governance practice is all about its adoption by businesses, and technology and operations.
Building the pedestals, aka capabilities, is less scary and easier compared to the monkey, which sounds crazy and scary. We all have seen how it is difficult to engage with multiple stakeholders, process and application owners, SMEs, data consumers and data custodians and proceed to then build a strategic, sustainable road map aided by a high degree of return on investment. The success of a data governance program depends on how successfully you solve business use cases.
In the age of AI technology and its models and agents, building capabilities might become easier and so the standing on the pedestal, but what about teaching a monkey to recite Shakespeare? “We will figure it out” is the most common theme coming from the technology development warehouses. But the question that we must ask is about economics, about the cost and value being generated by a data governance practice.
Jason Feifer, in his editorial article, brings out a message, this important message. He says that to find your monkey, you should ask this question. If you solved this problem and it was a remarkable success, what major change would have gotten you there? Building data governance practice is bringing change in data culture; the way we acquire, produce, process, store, distribute and archive data. To bring about a change in that data pipeline, adoption is the key, and that, in fact, is the monkey of data governance.
