CHAPTER 10: Advancing Data Quality

A fair-minded reader, scanning this book the first time, may well react in horror! “We don’t have anything like this. No provocateurs, no embedded data managers, no leadership. People don’t recognize themselves as data creators and customers. Indeed, we have a data quality team. But it is buried in Tech and is completely ineffective.”

It should be no surprise that today’s organizations are unfit for data—they were designed for a different time. They were perfectly suited for industrialization and concepts such as division of labor that came with it. These organizations have become enormously effective! So let’s not cast them aside lightly. At the same time, recognize that they are not well-suited to data—they’ve led to silos that get in the way of data sharing and make the sorts of customer-with-creator conversations more difficult, they’ve led to incorrectly assigning the data to IT, and they’ve made it more difficult to unleash the power of data. Putting the people and structures called for here and following these instructions is a powerful step in the right direction.

Horrified or not, you only have three choices when it comes to the instructions laid out in this book: You can ignore them, you can start to work on them, or you can try something different. Ignoring them is risky and it gets riskier every day. Data is changing everything, as no less an authority on strategy and competition than Michael Porter41 observed, “…All this has major implications for the classic organizational structure of manufacturers. What is under way is perhaps the most substantial change in the manufacturing firm since the second Industrial Revolution, more than a century ago.” And that is only manufacturers! You just can’t participate with bad data.

If you wish try something different, I encourage you to do so. We need a lot of innovation in this space!

That said, consider giving the getting in front approach and these instructions a solid try. Anyone can be a provocateur, so start with instructions that appeal the most, and learn as you go. If you’re low in the organizational hierarchy, start with something you can control. If you’re higher up, seed as many data quality initiatives as you can!

This book features dozens, maybe hundreds, of instructions. But let’s not make this any more complicated than it needs to be. Which are most important? I nominate the following five, because they include everyone. Indeed, an entire company can take them as rallying cry:

1. Grow increasingly intolerant of bad data, the hidden data factories and other bad results it engenders.
2. Adopt the “get in front on data quality” philosophy, focusing on creating data, and data definitions, correctly the first time. More than a simple philosophy, getting in front on data quality represents a profound cultural shift.
3. Data customers and data creators must play the most important roles—customers must define what’s most important, and creators must focus on delivering that most important data. Recognize that each of us is both a customer and creator, often simultaneously.
4. Build a federated organization for data quality, featuring embedded data managers who help customers and creators in their roles; data quality managers, who help them connect; leadership, which must bear the mantle of ensuring the new philosophy is followed; and Tech, which builds the supporting infrastructure.
5. No matter who you are, let your inner data provocateur out.