turning data into information

Today’s A:360 talks about how you can data into information. Data is simply raw numbers. Information has meaning and explains the raw numbers. Understanding the difference between data and information is critical. Knowing how to turn data into information is even more valuable.

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Hey everyone. Welcome to today’s A:360. My name is Brewster Knowlton, and today we’re going to be talking about how you can turn data into information.

Just about everybody whose read anything at all about data, analytics, or big data has heard W. Edwards Deming’s quote, “Without data, you’re just another person with an opinion”. But what about an interesting twist on that quote, “Without an opinion, you’re just another person with data”?

The converse of Deming’s quote is an interesting play on the difference between data and information. Information has meaning. It has a hypothesis, or an analysis behind it, and it explains what the raw data says. Data is just numbers, right? So, there’s a key difference between data and information. In this podcast, we’re going to talk about a few different ways, or what’s the process, that will turn data (raw numbers) into information.

The first step in the process of turning data into information is by asking a question. Data for the sake of data is meaningless. But if you use data to try and answer a question, then it starts to gain meaning. So, the first step is to ask a question of your data.

The next step is to form a hypothesis. Sounds a lot like the scientific method, right? We want to form a hypothesis so we have something to test against. Even for relatively simple questions like, “How many members do we have?”, ask yourself “what number do you think we should have?” Are you right or are you wrong? It gives you something as a benchmark – a baseline – very similar in statistics to the null hypothesis. You want to be sure confirmation bias doesn’t come into this. So, it’s good to write down a hypothesis to say, “This is what I think the answer is” and then go about either proving or disproving that hypothesis.

Once you’ve formed your hypothesis, you then want to go and test that hypothesis. Perhaps, depending on your analytics environment, you have to go to multiple different sources to get data. If you have a data warehouse or a strong analytics platform with centralized and integrated data, you can just go there. The thing to be careful of is not to gather too much data. Don’t gather more data than you need. Paralysis by analysis is a real thing. Only gather the data that is appropriate and relevant to testing the hypothesis.

Unfortunately, a lot of people tend to stop once they’ve tested their hypothesis believing that it is the end of the process of turning data into information. But, there is a critical last step that is often overlooked. That’s the part where you analyze your findings and provide an explanation.

You can provide all the charts, algorithms, and statistical analyses that you want. However, unless you provide a simple, concise written explanation of the data, you’re missing the most crucial step in turning data into information. You can put big binders together with all these spreadsheets and all those charts, but, in that scenario, you’ve only written a lot and said a little. You have to provide an explanation and form an opinion of what the data is showing.

If you want to turn data into information, follow this process:

  • Ask a question
  • Form a hypothesis
  • Test your hypothesis
  • Analyze your findings
  • Provide a clear, written explanation of what the data is telling you

If you can do all of this, you will have successfully turned data into information.

That’s it for today! Thanks again for listening to today’s A:360.

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  1. […] users to get data on their own. If your analytics team is required to constantly create all the new reports and analysis, you’re not going to be able to scale as the data needs and analytics requests rise. Therefore, […]

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