What data should we collect? Part 1: Why bother?

It’s time for a confession: we are data scientists but very frustrated ones. Data can shine a light on so much, yet there seems to be much confusion and even anxiety about it. In the face of mounting options of easy and automated methods of workplace data collection, we often get asked what data a business should collect. So here comes our answer in several parts, in the shape of a series of blog posts under the heading ‘What data what should we collect’. Welcome to Part 1: Why bother?

Businesses have taken decisions for hundreds of years, some good ones, some bad ones. So why all this fuss about data, analytics and data science now? Why should businesses collect and analyse their own data, for reasons other than that it appears trendy (and because Google does it, and that must mean it’s right)?

We would argue that data helps business leaders to take better decisions. In workplace design, a data-driven approach can address the needs of an organisation more profoundly than relying on intuition, opinion or office politics alone. The same goes for business strategies, where data can bring intelligence to the table. Ex-CEO of Netscape, Jim Barksdale’s purported statement ‘If we have data, let’s look at data. If all we have are opinions, let’s go with mine’ (as quoted in: ‘How Google works‘) emphasises the point.

But what role does data play exactly in decision-making?

Data can be seen as the raw material of bits and bytes that can contribute to information, as human beings begin to contextualise data and understand what they mean. For instance, 15 degrees Celsius in outside temperature is a data point. In the context of a day in August in London, that data point turns into information which tells us about a lousy British summer. Further interpretation turns information into knowledge, which includes guidance on how to act (in this case: wear a coat. Or go abroad for your holiday). Knowledge therefore means ‘knowing what could be done’ and as such has action possibilities already embedded. Through decision-making, possibilities turn into realities and have consequences in the world (if I wear a coat, I won’t freeze). The relationship between data and decision making is visualised below. If you want to read more about the data – information – knowledge relationship, I would recommend chapter 2 of Tina Chini’s ‘Effective Knowledge Transfer in Multinational Corporations‘.

You might now argue that you do not need data in decision making, since good leaders can decide based on their intuition or experience. That’s of course correct (at least partially) and many business decisions are made exactly like that. However, if you think about it very carefully, both intuition and experience follow the same logic from data to information to knowledge to decision making with the only difference that the process is more hidden and less obvious. If I decide based on intuition, I might have a hunch about something and might not be able to verbalise exactly why and how I think this is the right thing to do, but implicitly and subconsciously, I’m likely to follow the above logic. For experience, this is even clearer. Experience comes from accumulated knowledge over time. If I’ve experienced many British summers, I know what to expect and will be prepared. This means the difference between a data-driven decision making strategy and an intuitive, experience-based one is transparency and openness. Collecting data in a logic, open and rigorous way allows others to follow decision making processes. But it might also lead to challenging the unknown biases we all have. Nobel prize winner Daniel Kahnemann has explained the inevitable prejudices built into our reasoning as a problem of ‘Thinking fast and slow‘. Fast thinking, or intuitive judgments are often biased, because we don’t have the full story and processing statistics for example requires slow, effortful thinking. Thus a data-driven approach might not only be more transparent, but also more likely to lead to better results.

A particularly concise way of describing the relationship between data and deciding comes from Scott Berkun in his excellent book “A year without pants“, which tells the story of his work at WordPress.com. Scott contends that:

“Data can’t decide things for you. It can help you see things more clearly if captured carefully, but that’s not the same as deciding.”

This sums up nicely what data does: allowing you to see things more clearly. Berkun is also addressing another important point – the need to capture data carefully according to scientific principles. So data may lead to decision making by turning data into information and then knowledge, but data alone does not decide. It is human beings that do that and, hopefully, powered by data. 

Read part 2 of this series of blogs here

3 thoughts on “What data should we collect? Part 1: Why bother?

  1. Pingback: What data should we collect? Part 2: It depends… | brainybirdz

  2. Pingback: What data should we collect? Part 3: Big data vs small data | brainybirdz

  3. Pingback: What data should we collect? Part 4: The organisational reality | brainybirdz

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