In simple terms, data are a collection of numbers or facts that have the potential to provide information. Data, however, do not become informative until a person or process transforms or combines them in a manner that makes them useful to decision makers. Data are easy to collect and store, but good information is not. In the table below, we'll go into further detail about the differences between data and information with a few examples.
|Definition||Data is raw, unorganized facts that need to be processed. Data can be something simple and seemingly random and useless until it is organized.||When data is processed, organized, structured or presented in a given context so as to make it useful,
it is called information.
|Example||The number of visitors to a website in a day.||Seeing a spike in traffic for that day against the year (because its a holiday) is information.|
|Example||Your firms sales are up 20 percent!||Your firms sales are only up 20 percent compared to the industry’s growth rate of 40 percent.|
For example, the fact that your firm’s sales are up 20 percent sounds great! But it's not informative until you compare it with the industry’s growth rate of 40 percent. Its this this type of context that makes an individual data point an informative stat for decision makers.Thought leaders like BIll Gates have known for quite some time the role information plays in the success of a business.
Virtually everything in business today is an undifferentiated commodity, except how a company manages its information. How you manage information determines whether you win or lose. How you use information may be the one factor that determines its failure or success – or runaway success.
There are a few different ways to visualize the relationship between data and information. For this post, we're following Accenture's lead who identifies 5 distinct types of data/information according to an organizations digital maturity. Their process begins with the crudest most raw forms data and progresses all the way to a highly refined and monetarily valuable type of data which they call transact (we'll call this information). However, for our purposes, we're only interested in the first three. We'll use these to outline the 3 main types of data available in the marketplace today; raw, processed, and insights.
Check out Accenture's full spectrum of data monetization here.
|Raw Data||Data that has not been manipulated, “cleaned", analyzed, or processed in any way.||NASDAQs “Data on Demand”|
|Processed Data||Data that has been collected, stored, processed (through manipulation, cleaning, averaging, mixing, etc.) and analyzed.||MasterCard Advisors|
|Insights||Insights are the final product of data analysis that involve highest level data science. This includes things like agile insights, analytic solutions, what if analysis, etc.||Experian’s Mosaic|
When data leads to erroneous conclusions, it’s said to be misleading. Often times misleading data is the result of incomplete data or out of context data. Here are three tips to help you get the most information out of your data.
Manipulate it More - Step back and re-examine your scope.Your data could be misleading you because your focus is too narrow, or inversely too broad.
The Epistemology Questionnaire - Ask yourself “why do you have this data? How did you get it? Why is it important? Asking these questions will set you on a path to rediscovering the real relationships between you and your data.
Apply Your Experience - Applying your own experience and knowledge can be one of the best ways to begin the process of turning data into information. You may or may not be a data scientist by trade, but either way how you handle your data is an important part of the process no matter what once its deployed.
Data and information are two sides of the same coin. The difference between the two is subtle, but oh so important. Remember this the next time you're piecing together a marketing plan. Its valuable information you want, not seemingly random data. For data to become information, it needs to be put into the right context.
Are you one of the thousands of companies struggling with the data scientist shortage? Maybe you're just looking to fill a digital talent gap in your own workforce. Comment below and let us know what you think are the best ways to turn data into information. How are you taking advantage of all this data?