Tell me if this sounds familiar.
You join your weekly update meeting, someone throws a slide deck up on the screen, and you spend the next hour combing through spreadsheets, graphs, and charts tracking all the KPIs you need to keep an eye on. There’s a lot of information to digest, and people are digging into the data trying to figure out what it says.
It’s like a game of Where’s Waldo? (Wally if you’re British), with everyone searching for the key issues that are buried in all that data. The challenge isn’t that the data isn’t there — it’s that your org suffers from data immaturity.
The Three Stages of Data Maturity
Most founders are committed to continuously improving their product, their tech platform, and even their internal processes. But somehow, the way their company uses data doesn’t get the same level of attention.
Stage 1: Data Toddlers
In the early days, the data in your company is frequently incomplete, late, poorly labelled, internally conflicting, or simply non-existent. That’s a big problem if you’re trying to build a culture of data-driven decision making (and most good startups are).
The natural response to this is to amass more and more data…but that doesn’t necessarily solve the problem, as the quantity of data can easily become overwhelming. In addition to collecting more, better-quality data, your organization needs to learn how to use it wisely.
It’s pretty much inevitable that your startup will have to pass through this phase as you mature. The risk is that you get stuck here, and you would be surprised at how many companies do, despite how awful it is. Why? Because people get used to it.
Stage 2: Data Teens
At some point, you’ve solved the problems of Stage 1 by building extensive data capture capabilities supported by a team that helps to collate and validate the data. Hopefully your organization has also internalized the idea that good data is a prerequisite for making good decisions.
But your data is still mostly presented ‘in the raw,’ in unprocessed formats like tables and spreadsheets. People have to work hard to make sense of it. Discussions frequently revolve around clarifying exactly what the data means: What time period does this relate to? What is the denominator in this percentage? Which costs are included and which are excluded?
This is prime time for the data nerds (and let’s be honest: many techies are data nerds). Meetings often run for an hour or two as people wade through tremendous amounts of data, trying to figure out what it really means. The data nerds are happy. But it’s a very inefficient way to run your company.
It’s not just the data nerds who love this phase. Many senior managers also enjoy the process of wading through tons of data, trying to spot the key issues (e.g. searching for Waldo). Why? Because the process of spotting the key issue in all that data makes them feel smart and valuable.
But this is not the best use of your senior managers’ time, and this work shouldn’t be done in meetings with large numbers of people. Instead, the people closest to the data should be the ones who find Waldo. This means empowering a less senior group of people to identify the issues that their bosses — the senior managers — should be discussing.
Yes, they will make some mistakes early on: either by overlooking some issues that need to be discussed (a meaningful mistake), or by surfacing issues that don’t warrant a discussion (a minor mistake). But if you allow your senior managers (including you!) to do this work instead of entrusting it to folks lower down in your organization, they’ll never learn this crucial skill, and you and your managers will keep wasting time doing a job that’s not actually yours to do.
Another subtle but important problem with getting stuck in the ‘teenage years’ is that meetings tend to become dominated by the folks who are highly numerate. Because so much of the discussion is about the data itself, people who are more conceptual or intuitive thinkers may struggle to join in — but their perspectives are also important.
Most startups get stuck as ‘data teenagers’ for a long time. Why? Because people get used to it.
Stage 3: Data Grownups
In data-mature organizations, data fades into the background and issues come to the forefront. People understand that data is a raw material for making great decisions, but it’s not the only one.
In data-mature organizations, meetings revolve around discussing specific issues that have been identified in advance, and only the data that supports that discussion gets reviewed in the meeting. The discussion starts not by reviewing the data but by ensuring everyone understands the issue at hand and the goal of the meeting. The data is then presented in a concise, helpful format — usually not in raw tabular form but in more ‘insightful’ formats such as graphs that highlight the issue — more like a McKinsey presentation.
Of course there may still be some discussion of the data itself, but in most cases the data is taken as read, and the focus is on the issue and what can be done about it. This focus on issues and solutions makes it much easier for the conceptual and intuitive thinkers to contribute fully.
In Data Grownup organizations, the people who chair meetings are highly skilled and play an important role in moving the discussion forward. Part of that role is to ensure that the data doesn’t hijack the meeting. To keep the focus on the issue, they ask questions that broaden the discussion beyond the data itself and bring in more qualitative perspectives. Questions like:
- Are we framing the issue correctly?
- What do people think about this?
- Does anyone have any other information or experience that would help us understand or evaluate this?
- Does anyone have any ideas we haven’t considered yet?
What About Amazon’s Silent Meeting Memos?
Writing up issues in prose form (i.e. in Word or GoogleDocs) instead of relying on slides can be a huge step forward. It forces the author to be much clearer in their thinking and to focus on the issue rather than just present the data. So I’m a big fan of memos to support discussions. Whether the memo is shared in advance or read at the meeting, Amazon-style, is far less important.
One problem is that many startups — seeking to be more like Amazon — adopt the Silent Meeting memo approach when they’re still Data Toddlers or Teenagers. This may be better than nothing, but it’s not a cure-all. It doesn’t teach your organization how to run effective, issue-driven meetings or push the responsibility for finding Waldo onto the right people.
Six Steps to Boost Your Data Maturity
- Ensure that everyone understands the cost to the organization of having five, ten or more people sit around while a few people try to make sense of data.
- Emphasize the importance of making data easily digestible — and hire people who focus on this if needed. Someone with a few years of management consulting experience would be perfect for this role.
- Tell your managers that it’s actually not their job to find Waldo. It’s everyone’s job, and the more people lower down the organization that can do it , the better off everyone will be.
- Reassure those who are newly empowered to find Waldo that this will be a learning process, and mistakes will be forgiven.
- Train everyone to turn data into issues and to present data in formats that clarify those issues.
- Train your managers to become highly effective meeting chairs.
Reaching data maturity is only partly about having the technical capabilities to collect, analyze, and present data. It’s also about training your people to use data to make decisions, especially in groups. Becoming a Data Grownup means focusing on the issues the data reveals, not the data itself, and viewing data as just one source of information in the problem-solving process.