In the book Winning with Data: Transform Your Culture, Empower Your People, and Shape Your Future, authors Tomasz Tunguz and Frank Bien explore the culture changes data brings to the business. As someone who talks about data governance every day, I found this book very relevant to the discussions I have with customers and prospects every day.
The creation of a data-driven culture within an organization is key to the success of today’s companies. To enable this culture, management and employees expect answers to their data questions in a near real-time fashion. And while this demand of ‘instant data’ becomes the expectation rather than the exception, many organizations struggle with it. At their core, the best data-driven companies manage to operationalize their data to drive business decisions, optimize their market, drive growth, and predict where the company is going. Modern data infrastructure is a necessity, but is insufficient to convert a company into a data-driven market leader. The journey to become a real data-driven company requires a difficult change management exercise to transform the company culture and make data part of every discussion and decision.
Data breadlines, the typical track one needs to follow in the organization to request answers from the various data sources, are only serviced by a handful of data analysts. Therefore, every request goes through a system of queues and prioritization. As a result, teams have to wait extensive amounts of time to even ask their question to the analysts in the first place.
Once they have reached the front of the queue, ‘data obscurity’ prevents business users and analysts from finding a common language to define those questions and understand where the data providing answers exists in the organization. To make things worse, the above issues are often ‘solved’ by employees who create their own data stores and shadow analyst teams. This leads to ‘data fragmentation’ (data being spread across the organization). With those different data sources supposedly carrying the same data and feeding similar reports, meetings can quickly escalate to ‘data brawls’ where employees endlessly discuss which report or data stores contains the correct information. The conclusion is clear: “without a universal lexicon, confusion is inevitable and conflict unavoidable.”
Therefore, a key factor in creating the ability to operationalize the data is a data dictionary which contains a canonical definition of each metric and where the supporting data can be found. This enables productive and incisive conversations about data across teams, bolstering or refuting argument and accelerating decisions. In addition, true data-driven companies create equations that describe their business and capture all contributing parts to the revenue. Within these equations, the variables are the things to measure, the very same metrics that can be found within the data dictionary.
Traditionally, decisions were made by lots of opinionated debate, followed by the most senior person selecting a path forward. But true data-driven decision-makers acknowledge that great ideas do not always come from the most experienced or most senior member of the team. Great ideas can originate from everywhere, both internal as well as external to the company, and data is often the best tool to ensure those ideas rise the ranks.