25 years, one repeating problem

Been There, Charted That, No. 1  ·  For: anyone who makes decisions with data  ·  Reading time: 5 min

People have data. They do not have decisions.

Our practice is built on more than 25 years of work across six continents, in universities, charities, companies and partnerships. The sectors changed. The currencies changed. The software changed three or four times. One problem never did.

Here are the six places it breaks.

• 1. The data arrives after the decision. The meeting is on Thursday. The report lands on Friday. So Thursday runs on memory and confidence.
• 2. Three systems, three answers. Finance has one number for revenue, the CRM has another and the board pack has a third. Each one can be defended. Together they stop the room.
• 3. Nobody owns the number. Everyone uses it. Nobody can say where it came from or when it was last checked.
• 4. The chart that proves the point of whoever made it. A cropped axis, a convenient year, no word on what the data cannot show. After a few of these, people learn to distrust every chart, including the honest ones.
• 5. No time to read. The pack is 40 pages. The reader has 12 minutes. Microsoft’s Work Trend Index, which analysed signals from 31,000 workers in 31 countries, found people are interrupted roughly every two minutes, and 80% say they lack the time and energy to do their work.
• 6. More output, not more clarity. AI now writes the summary of the summary in seconds. The pile is bigger. The decision is not any closer.

Show and tell: the four steps from data to decision. Most organisations do not stall at the data. They stall between the steps.

What the numbers say, and what they do not

You will often see a headline figure that poor data quality costs the average organisation $12.9 million a year. It is a Gartner estimate. It is worth knowing where it comes from: a 2020 survey of 154 customers of data quality software, who estimated their own costs. Gartner also reports that 59% of organisations do not measure data quality at all.

So the cost of the problem is itself poorly measured. That tells you something about the problem.

What we changed in how we work

After enough years of watching this, we hold to four habits.

• Start with the decision. Before we open any data, we write down the decision it should inform, who makes it and by when.
• Go to the primary source. If we cannot trace a number to the body that published it, it does not go in.
• Say what the data cannot show. An average is not anyone’s odds.
• One idea per chart. If a chart makes two points, it becomes two charts.

Why we built B4A

Build 4 Abundance is not here to make more charts. It is here to close the gap between having data and knowing what to do next. Everything we make, from the public data stories in the B4A Data & AI Playground to dashboards and forecasts for teams, follows the same six steps.

Your turn: which of the six breaks hurts most in your week? Tell us. We read every reply.

Have data? Let us talk