The Difference Between Data, Information, and Insight and How They Drive Analytics

The Difference Between Data, Information, and Insight and How They Drive Analytics
Jim Rushton

The following is adapted from Guaranteed Analytics.

Businesses new to utilizing analytics often get mired in the subtle differences between data, information, and insights. Understanding those differences, however, is key to making the most of analytics and tailoring your program to best benefit your business.

What exactly is data? How does that feed into information, and what's the process for turning both into actionable insights? The differences might seem confusing at first, but the distinctions are simple once you see how they work together.

Here’s a look at the three key analytics components. Once you learn how they fit together, you’ll be able to apply these concepts to your own unique scenario and actually get valuable results with your analytics.

Data: The Foundation

Data is the area where most people start their analytics journey. These are facts. Ones and zeroes. You can never change the facts. For instance, on a particular Sunday, 30 customers bought a total of 35 blocks of Velveeta from a particular grocery store. No matter what spin you put on it, that’s a fact.

Depending on how you’ve set up and automated data collection, you may already have reams of valuable data at your fingertips, like the details of every sales transaction—which SKUs sold, the dollar amount, time of sale, store location, payment method, etc.

Often data are facts you are already collecting via the likes of cash register receipts or online purchase logs. Other times you have to seek out the data, via surveys for example.

Information: The Story Behind the Data

Information makes data more useful. It’s where the data start to tell a story.

For example, in addition to seeing the time of sales in your retail outlets, you can also see a pattern: every fall Sunday around noon, your sales go way up, especially for items like soda, cheese, salsa, and chips.

To find information in data, it is often beneficial to employ a certain amount of automation. The more data, the more laborious it is to pick through and find patterns. Technology makes it possible to do this without human manpower, provided your data are purposefully gathered and stored.

Insight: Where Analytics Yields Results

Insights are the opportunities information helps to shine a light on. They are things you can take action on. Only when you are able to make different decisions and take new actions does analytics begin to truly monetize your data and generate a return on your investment.

Looking at the information above, you might ascertain that the reason your grocery store sales peak every Sunday at noon during the fall is because people are buying snacks before televised NFL games.

What can you do with that insight? You could offer special football-related offerings or suggest items to buy together, like soda and chips. You could create targeted advertising or perhaps enhance your rewards program to feature a football-centric promotion that encourages shoppers to rack up purchases to get a freebie.

You can also use that insight to make sure your store is fully staffed for the Sunday pre-game rush. You can brief workers so they know where typical pre-football purchases are located in the aisles or set up an extra football-related feature near the front door. The idea is to parlay the original information into something that helps you reach your business goals, like increasing weekend revenue.

How They Work Together

As you’re probably starting to surmise, data, information, and insight work together to complete the analytics package. Let’s examine another hypothetical scenario and see how they connect in a way that helps to drive better business decisions.

Imagine you manage a public water utility. At the data level, you learn that pipe number 17 has experienced 265 gallons of flow in the last hour. It’s a fact, but it’s essentially meaningless on its own.

However, if you compare that flow with previous hours, previous days, other pipes, or flow necessary for clean water production, you are starting to get information.

The next step is to derive insight from your information. Depending on what you discover, you may need to slow down pipe 17 or conversely, open a valve. You may need to inspect a pipe if the flow is abnormal.

This can affect your budget or perhaps trigger a requirement to notify public officials about a reduction in water availability while the inspection takes place. You might need to bring in extra workers or hire an engineering consultant. Or everything might be fine, in which case you can sign off on flow reports for the week with confidence.

Another Case Study

Sometimes the insight gleaned from the right use of data and information can prevent you from losing customers or committing a terrible mistake with a client. Imagine you work for a power company that serves 10 area Pizza Hut restaurants. Each of these customer locations has the option to buy their electricity from a competing power company if they become dissatisfied.

At the end of the month, you do not receive timely payment from one of the Pizza Hut accounts (data), which is puzzling because quick research reveals that account usually pays on time, in full (information). Your standard protocol is to issue a reminder and cut power if payment is not made within a certain time frame.

But do you really want to be so quick to cut power to an account that hasn’t been problematic in the past? What if it turns out that there was a reasonable explanation, but your action has already driven them to take their business to a competitor?

Taking a deeper dive into the information, an insight is revealed: all 10 of the Pizza Huts are owned by the same individual, making the lone delinquent account a small facet of one strategically significant customer in good standing. Instead of applying a “rules are rules” reaction to an isolated piece of data, analytic insight affords you the opportunity to treat a high-value client appropriately.

You call the restaurant that hasn’t paid, and sure enough, you find out they have a new accountant or a computer system on the fritz that explains the one unpaid bill. They quickly resolve the issue and are thankful for the call. Customer satisfaction is actually improved rather than damaged!

Good thing you used analytics at the insight level and didn’t act solely on what the raw data was telling you. This is the benefit of analytics done right.

For more advice on making the most of analytics, you can find Guaranteed Analytics on Amazon.

Jim Rushton began his career in analytics working with some of the biggest consulting companies in the world, including Accenture, Deloitte Consulting, and IBM Global Services. Jim then moved to an executive position with Verizon, where he oversaw the company’s customer and marketing information. Leveraging his experience across corporate America, he helped found Armeta Analytics, and in the past decade, his team has helped dozens of Fortune 1000 companies learn how to monetize their data.

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