16 patterns you see in pre-data-driven companies

Contents
  1. Unused data
  2. Debilitating delays
  3. Wasted resources
  4. Lost knowledge
  5. Homogeneity
  6. Needless repetition
  7. Puzzling barriers
  8. Lethargic data literacy
  9. Competing definitions
  10. Dominating HiPPOs
  11. Reproducing nothing
  12. Leaps of faith
  13. Dark data
  14. Separate lanes for data, analysis, and context
  15. Analysis paralysis
  16. Low trust

This is Part 3 in a series about Collective Data Empowerment.

1. Unused data

People don’t know what data your company has, and can’t find data when they need it. How can you use something if you don’t know it exists?

2. Debilitating delays

Data and analysis arrive too late to capitalize on opportunities. People struggle to understand it, and their questions aren’t answered fast enough.

3. Wasted resources

High-paid data people spend most of the day making data more usable, answering the same questions, and refreshing reports. They can’t focus on the advanced tasks that only they can do.

Data scientists, according to interviews and expert estimates, spend from 50 percent to 80 percent of their time mired in this more mundane labor of collecting and preparing unruly digital data, before it can be explored for useful nuggets. — Steve Lohr, The New York Times

4. Lost knowledge

Teams lose the knowledge they create if it falls outside of narrowly-defined project goals. If it doesn’t have immediate value, and there’s no obvious home for it, it falls through the cracks.

5. Homogeneity

Project outcomes reflect the knowledge, abilities, and biases of a small, homogeneous group. These advanced practitioners convey insights that seem cryptic to most of their colleagues.

“Very few companies expect only professional writers to know how to write. So why ask only professional data scientists to understand and analyze data, at least at a basic level?” — Jonathan Cornelissen, Harvard Business Review

6. Needless repetition

When people can’t see what others are working on, they waste time and money on duplicative work.

7. Puzzling barriers

Access controls and other restrictions persist without regular reconsideration and changes. It’s the classic battle between legacy and logic.

8. Lethargic data literacy

Data and insights do little to elevate data literacy if people can’t trace the path from question to answer. Fancy tools can boost the productivity of data elites, but they don’t lift the general baseline by themselves.

9. Competing definitions

You are entitled to your own opinions, but not your own facts. What about your own definitions? When Team A defines something one way, and Team B defines it another way, an innocent disconnect can cause serious problems.

10. Dominating HiPPOs

Data-driven cultures do not put the Highest Paid Person’s Opinions above the data. Carl Anderson describes the HiPPO’s destructive tendencies:

These are the know-it-alls who, almost on principle, ignore the data, ignore the evidence and recommendations, and do what they want because they know best (after all, their paycheck proves it!). — Carl Anderson, oreilly.com

11. Reproducing nothing

The reproducibility crisis in the scientific community should make businesses interrogate their own data practices. When people can’t reproduce an analysis, they can’t check it for accuracy or reuse it.

12. Leaps of faith

People make choices based on insufficient, inaccurate, or outdated data and analysis — when they use data at all.

13. Dark data

Too much data work and analysis takes place without transparency, audibility, or accountability. Signs include rogue databases, unsanctioned software, and too many emailed spreadsheets.

14. Separate lanes for data, analysis, and context

Getting the full picture of an analysis or dataset requires looking for puzzle pieces in different places. The context people need so they can use data gets stuck within email threads, one-off DMs, and hallway conversations.

15. Analysis paralysis

People wait for the “perfect” data before deciding, even though the data they can access should be enough. A mix of factors drives this behavior, ranging from low trust to the steep learning curves typical of data tools.

16. Low trust

It’s hard to trust data if you don’t know things like who has touched it, where it comes from, and how it has changed over time. No wonder 56 percent of CEOs worry about the integrity of the data they use for decision-making.

Ready to get data-driven? Get right to it and download the complete guide to building a data-driven culture through Collective Data Empowerment.