According to a 2017 IBM report, "Every day, we create 2.5 quintillion bytes of data. To put that into perspective, 90 percent of the data in the world today has been created in the last two years alone." Those are some pretty staggering figures and businesses see the volume of their internally generated data growing all the time.

What we do with all that data can have varying degrees of impact, depending on a variety of factors. By analyzing data and developing insights that inform decision making we get a big bang for our data buck. Insights are they key to wringing the value out of data.

Data vs Insight: What's the Difference?

The contrast of data and insights reminds me of the phrase "Knowlege is power." The truth is, knowledge is only potential power and while many people say that data is valuable, it is only potentially valuable until we apply insights to it to unleash its power. Just what is this thing called insight and how can we use it to improve business results?

Data is simply raw numbers. Insights are the results of understanding the underlying nature of things and/or relationships between data. Insights typically allow us to make better decisions, which is why they are much more valuable than data. As defined in "The Art of Insight", by Charles Kiefer and Malcolm Constable, "Insights are really high-quality fresh thoughts. They result in a dramatically improved understanding of a situation or problem such that we see things more deeply and more accurately than before."

Another definition says, “Insight is the understanding of a specific cause and effect in a specific context.” Some people describe an insight as experiencing an “a-ha” moment, but don't let that make you think insights are serendipitous events out of our control. The specific wording of a definition or insight is less important than the common denominator, which is insights allow us to improve decision making. That's the lever that unleashes raw data's power.

Going from data to insights can take many forms, but there are some steps we can take to make it a systematic and repeatable process.

What is Your Goal?

Define what your end goal is. In other words, what issue are you trying to resolve? What question are you trying to answer? What problem are you trying to solve? If you are just running calculations or iterating models aimlessly, your chances of gleaning meaningful insights from your data are pretty slim. Don't misunderstand this to mean there is no value in adjusting as you go. If you start to see patterns that lead you somewhere, don't ignore them. Just as a scientists starts with a hypothesis, but if the evidence is clear that the data says otherwise, they continue to test their hypothesis, but they pay attention to what the data says. 

Analyze It

Analyze your data. Do some modeling, and I don't mean the type that requires a runway and designer clothes. Use what you do know to elicit things you don't know from your data. If you're trying to develop insights from financial information and you know there are certain patterns, that is a good starting point for modeling future financial performance. You may be familiar with disclaimers like the one the United States Securities and Exchange Commission requires for mutual funds which says, "past performance does not necessarily predict future results." Models based on known data don't necessarily predict future results, but they are a good starting point for discovering insights about your data and what it means.

There are tools beyond simple spreadsheets that make this type of modeling quicker, easier, and more effective than ever before. These tools are more affordable and easier to use than ever before, so you should explore them and figure out which tools might be a fit to give you leverage and move your analysis and insights to the next level.

Questions, Questions, Questions

Once you have done some analysis, review and analyze the output with an eye towards you end goal. What does the analysis mean in the context of the question to be answered or the problem to be solved? If the question you are trying to answer is "What will our sales forecast be for the coming 12 months?", what does your analysis tell you about the answer to that question. If you're working with high-quality data and you built a model wisely, with the end goal in mind your analysis should help you develop some quality insights rather quickly. That being said, don't rush the process. Take some time to think about your results and what they mean. Allow yourself to think outside the box. That's where some people think their best.

By approaching data systematically, you can gain insights you can use to make better decisions and that is when you get the real value from your data.

The article was first published in Prevero Blog