Data vs. Information: When does a fact become an insight?
It's undeniable that, as a culture, we have entered into an Age of Data.
We're surrounded by stories of data breaches, data theft, data usage and data ownership. But while these phrases have become commonplace, many of us still carry misconceptions about these words. What is data, exactly? Is there a difference between data and information? And at what point does data become knowledge? These may seem like technical distinctions, but by examining the process of how facts are transferred into actionable intelligence, we can understand the value of data to your company.
Just the Facts
So what is data? Data is a recording of a fact. It might be a number, an image, an audio clip or a transcription, among other things. It simply states that something happened. You bought a bag of coffee. A plane entered the airport's airspace. The temperature of the nuclear reactor rose by four degrees. Data might be structured ' translated into numbers ' like the amount on the receipt you got at the grocery store. Or it might be unstructured, like the post you left on social media, recommending that grocery store to your friends. On their own, these points of data are not that interesting or that useful. At this stage, they are raw and unprocessed. But as these bits of facts accumulate and grow, companies can use them to piece together assumptions about your behavior, or the arrival time of the airplane, or the safety of the nuclear reactor. That process of analyzing and contextualizing these facts is what turns data into information.
How Do Companies Collect Data?
According to Liam Hanham, director of data science at Elicit, companies collect data in three basic ways: 'By directly asking customers, by indirectly tracking customers, and by appending other sources of customer data to your own.' So, of course, if you submit to a survey, your opinion registers as data, but just as useful are the company's requests for your name and contact or shipping information. As we mentioned above, every time we interact with a product or its website, we are creating a data stream for companies, as they register what we bought or what we looked at. With increasingly localized GPS tracking devices, retailers can even understand how we move through their stores, which locations we spent more time in, and whether we accessed their competitors' websites as we shopped.
If you are a company looking for more information on your current customer base, one place to look is your customer service records. There, you should be able to trace how customers have interacted with your products in the past, what they have enjoyed and what they've struggled with. Extrapolating from those facts can give you a clearer picture of what your company is doing well and how to strengthen your consumer base.
Turning Raw Data Into Information
If we think of data as raw facts, information places those facts into a usable context. Instead of a single blip of data, we have a series of data points from which we can begin to glean a story. For example, let's go back to the purchase of the bag of coffee. The single data point tells us that you purchased an eight-dollar bag of coffee. But other factors, such as which other items you bought, the time of your purchase and your method of payment, begin to give us a deeper understanding of that single act. If you used a loyalty card, then the store probably knows your gender, your age and your contact information. From all this, they can begin to construct a picture of your buying habits. When do you like to shop? What items do you buy most often? Which items do you tend to buy together? Do you use coupons or other sales promotions? By comparing your buying habits with the habits of other customers across the country, a company can alter its sales and promotions, adjust its hours or decide whether to expand into a new location.
This type of data processing isn't just useful for marketing. It can also be used to improve business processes and increase efficiencies within a company. If you are a sales manager, you need to know which districts are performing well, which are struggling and how to best use company resources to expand or deepen the consumer base. If you are a plant operator, you want to have a clear picture of each step of your operation, the time it takes, and the expected strain on your equipment or labor force. Of course, much of the data processing and data mining is automated, allowing users to take advantage of deep beds of data, extracting the trends and forecasts that are most useful for their specific needs. As machine learning algorithms and other forms of AI improve, a company's data analytics become a more reliable indicator of future actions.
Putting Information to Work
The final stage of data gathering is actually using the data to inform business decisions. In this process, we see the transformation of information into knowledge or insight. If information gave us localized context for the initial fact or event, business insight gives us a broader perspective on that event, allowing us to understand how the initial action might affect the behavior and decisions of the overall company. In other words, business insight puts the information into action. This may mean that a company tries to improve their customers' experience by introducing new products or services, modifying their digital presence or expanding into new demographics. Predictive analytics try to foresee how each of these decisions might affect overall business goals. Analysts can then measure outcomes against expectations, starting the process of data collection all over again.
Interested in learning more about how to put data to use in your organization? Diligent can help. Contact a Diligent representative for more information.
