We live in the information age, where the amounts of raw information created by us and available to us have been growing exponentially. Just a few decades ago, the Internet swooped in, making unprecedented amounts of textual and visual information available. Now we are confronted with the question of how we can put all that information to use in a business environment. This made me think about the relationship of data and information and how to transform them into knowledge and insight. This blog will deal with the distinction between the two–how raw data or information differs from knowledge or insight. The former consists of raw bits and bytes available to us wherever we look. The latter helps us to use the raw bits and bytes to make decisions and take action.
The word data, the plural of Latin datum, is defined/translated by most dictionaries as something given. In our modern lexicon, the data leads a life of its own, independent of datum. Luciano Floridi, a top researcher in the field of the philosophy of information, originally defined datum as “anything that makes a difference”: Light in the dark, a black dot on white paper, 1 in field full of 0s and even a sound in silence. For example, according to this definition, a data point is a binary “1” in a sea of “0”‘s.
Another example which explains the datum concept further is the number 42. It is a datum, but without asking a question, the right question– What is the meaning of life? –it, like all other numbers, is meaningless. For that reason, among others, Luciano Floridi redefines his own definition of datum to “an answer without a question.” Floridi argues that a datum becomes informative once we know it is the answer to our question. Therefore, information can be defined as Datum + A relevant question.
The term information is used quite often in our language. It denotes many different forms of data, including in some cases incorrect data, such as misinformation or propaganda. NYU Professor Neil Postman was a social critic who wrote, “Information has become a form of garbage, not only incapable of answering the most fundamental human questions but barely useful in providing coherent direction to the solution of even mundane problems” and “Like the Sorcerer’s Apprentice, we are awash in information. And all the sorcerer has left us is a broom.”
The late Dr. Postman published this in 1992, before most of the world has even heard of the World Wide Web. But he points to a key problem we are faced with–there is way too much information around.
The way to convert information to knowledge is to find connections between the outcome and the information we have. Knowledge involves understanding, not just gathering the information. For example, if we understand that SaaS companies tend to buy our product, we converted the information Company X is a SaaS company to the knowledge that Company X is likely to buy from us.
Oftentimes we find that too much data only serves to hide what we are looking for. In the Big Data age, scientists, researchers and marketers have lots more information available than ever. The truth is that even accurate data is nothing by itself; information is what really matters. Floridi also noted that it is why we want ‘the data, all the data, the only data which best answers our specific queries.” Only from properly explained information can one gain knowledge and/or insight.
In everyday life, the Internet has become the biggest source of information for educational, entertainment and commercial purposes. To use that data at scale in a commercial environment, we must convert the data into relevant insights. Historically marketers tried to use information based on past hunches and guesstimates. Today marketers can use predictive marketing platforms to collect information and gain insight to act on specific goals they are looking to achieve.
As Neil Postman wrote, “We are awash in information.” This is why randomly collecting data on the web is almost useless absent a question we are trying to answer. Those questions we want answered must be specific and relevant to gain the knowledge that can be acted upon correctly.
The process described in this post converts raw data into information (data + relevant question) and then into knowledge or insight (information + explanation). Predictive marketing platforms perform this process for today’s marketers, by taking the following steps:
- Data collection: Get all the datum available using web data collection methods. This stage has an ever existing competition between the huge data amounts that keep growing, and the more and more efficient algorithms to analyze it. The important piece in data collection is to isolate the signal from background noise.
- Data to Information: This is done by asking the right questions and answering them with the data collected. Just as a search engine anticipates the questions it will be asked (e.g. when you search for “Video Conference” in Google, they don’t search the entire web that very second for “Video Conference.” They anticipated you will ask that, and they pre-indexed their entire database for this query).
- Information to Insight: This is where the predictive marketing platform becomes the marketer’s expert. It helps the marketer understand the causes and the explanations for the outcomes using the available information. This in turn helps the marketer define the right actions.
Using data correctly can boost business performance. Sports teams use it extensively. Look no further than the Golden State Warriors’ reinvention of the role of the three-point shot, and the demise of the midrange shot from two points. Statistics show that, with good shooters, it makes sense to take another step back and shoot a three, even at the expense of a slightly lower percentage.
Marketers adopting predictive marketing are doing just that—using information and knowledge to get much better outcomes.