If you look under the hood of most company’s data strategy — if they have one, that is — you’ll find that most data is woefully unmanaged. It’s unclear how data is meant to be used and who is responsible for it. Businesses must craft better systems and approaches to working with data, and that starts with clarifying management responsibilities for everyone who touches data in any way, across the entire company. They can start with five steps: 1) get everyone involved, 2) build infrastructure that can overcome the limitations created by silos, 3) stop asking IT to manage data, 4) charge data teams with coaching and coordinating, and 5) get senior leadership off the sidelines.
Most companies continue to struggle in managing their data and putting it to work. They expend a lot of time and energy, but don’t get much for their efforts. Quality is low, people don’t trust the data, technical debt is out-of-control, and they miss opportunities to become data-driven, take advantage of advanced analytics and AI, and compete with data. Indeed, most organizations are simply not attuned to the rigors of working with data.
This, of course, is a problem. At this point, practically everyone’s job involves using, interpreting, and creating data. Yet somehow this seems to get lost at all levels of organizations — the structure, the culture, the people. It’s often unclear whose responsibility data is (the CDO? IT? Everyone? No one?), and because data tend to be hidden, in customer orders, logistics, and management reports, the power of the status quo prevails. Without clear expectations, chaos reigns. People don’t know what to do, basic tasks are left undone, and much of the work that is undertaken is done poorly. The unfortunate reality: more often than not, data is essentially unmanaged.
Businesses must craft better systems and approaches to working with data, and that starts with clarifying management responsibilities for everyone who touches data in any way, across the entire company. Here are five guidelines for deciding who should do what when it comes to data.
Most of the real data action involves “regular people,” who don’t have “data” in their titles. They create the stuff; interpret the stuff; use the stuff to satisfy customers and regulators, keep track of inventory and money, and make plans and decisions, and so forth. These people are effectively the front line of any larger data project, program, or strategy, and are essential to its function. Yet they’re almost always left out at the planning stage. Given the excitement about Big Data, artificial intelligence, and digital transformation, you might be surprised that including regular people is the single most important step companies can take to accelerate their data programs.
There’s huge potential here. To unleash it, companies need to clarify regular people’s roles and responsibilities, as data customers and creators when it comes to data quality, as small data scientists, as contributors to larger data projects, as better decision-makers, and as guardians of the company’s data. The first step leaders should take is putting regular people and these responsibilities front and center. They must also follow through, training and supporting people to help them become effective in their newly articulated responsibilities.
While companies reap the most value from data when it’s used across departments, siloes get in the way of the data sharing. Despite the fact that almost everyone depends on data created by other departments to do their jobs (e.g., sales uses lead data generated by marketing, and then passes sales dataon to operations for fulfillment, and so on), departments are often unconcerned with the quality of data they pass on. Companies are gigantic daisy chains of data flows, and when bad data gets passed along, it mucks up everything.
For better or worse, silos probably aren’t going anywhere soon.
