“The dog did nothing in the night-time. That was the curious incident” Sherlock Holmes

Q: What is data?

A: Data is everything.

That was the conclusion myself and a room full of Data Scientists came up with. Here was one exchange:

Person A: Data is everything.

Person B: By everything, do we mean the everything that does exist, or everything that might exist?

Person A: Everything that might exist.

Person B: And that includes the idea that we might just be dreaming all this. That dream’s data, too?

Person A: Absolutely.

Person C: Obviously.

Data: is everything. The room was unanimous.

Let’s face it, it’s hard to find utility in a definition this broad. Everything is, after all, a lot of stuff.

Well, yes, it is, but whilst the definition itself may not make you gird your loins, the discussion itself might. Look what wasn’t mentioned: database, analytics, intelligence, spreadsheets, big data (sigh), machine learning. The so-called stuff of data. By defining data so broadly the conversation never became biased by the tools.

This is one the biggest problems I see in the data industry today. The tools leading the discussion. That is, the discussion has become about the box the toy comes in, not the toy itself. For example: “we need to migrate to [technical platform] because of [technical reason]”. Maybe but this discussion is operational, not strategic.

Data, however could not be more strategic. We apply context to data and we get information. We apply context to information and we get knowledge. We get knowledge, we get power.

The best way for a business to think of its data is as a strategic resource. Something you ask questions of, as well as demand answers from. Look at data in the right way and it will offer up both.

The first question you should ask of your data: “what is the problem that I am trying to solve?”

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