Data, Information and Insight

What exactly does data, information and insight mean in the context of BI?

data, information, insight
Data : The raw form from which information and Insight can be derived. Usually any recorded values, numbers, text, audio, video, stored in any form, any size and any location that gets generated in any event or transaction or just based on the current state or status. 

It could be internal data like employee related data ( Name, address, phone number, gender, etc.) or products, services related data or customer related data or server logs, web clicks, call center data , product reviews, ratings, etc. Anything and everything that can be used to derive information is data. The much hyped Big data is also Data. It could be stored as files or in a Database or just as logs.

Burger Chain Example 
For example, the event of a customer buying a burger from a fast food restaurant generates lot of data. Time of purchase, terminal used for payment, employee who served the customer, amount and currency of purchase, type of burger purchased, time taken between order completion and burger delivery, type of payment ( card or cash), if card then type of card (Visa, Mastercard, Amex, Rupay, JCB, etc),  static and slowy changing data about the restaurant location, owner details, employees data, supplier data, burger choices, prices and availability data. Employee master data, supplier data, daily sales data, payroll data, campaigns each of these are maintained in different systems.

Information : When you give context to data it becomes Information. Context to the data is given by metadata. If you are provided with lot of timestamps without an header you don't know what that timestamp means. You just know that you have lot of timestamps. But when a meaningful header is added and a description is provided to the column you know what that timestamp stands for.

data, information, insightInformation is the first level output from BI. This information triggers questions/ideas in the minds of users forcing them to ask more questions/look for more details.

Burger Chain Example 
If you are told that the timestamps provided to you is the burger purchase timestamps of a particular burger outlet then you have information. And now you have information about burger purchase timings. So the context here is burger purchase timings. Without this context all that you have is timestamp which could mean anything or nothing.

Often data and information is interchangeably used. And most often it doesn't look wrong. For example, if someone asks do we have data about our customers? They actually mean do we have information about our customers. Based on the context we know what they mean. In general day to business we don't differentiate between these two unless there is a real need. When we have to be technical about it then we use specific words. For example we don't say information migration project, we say data migration project.

And to be very clear we also use the term raw data to indicate that we are not talking about information.

Insight : Deeper understanding than that is obvious at first look. Knowledge based on which decisions can be taken. When you have collected lot of data, by carrying out data analysis you can derive trends and patterns. Identify outliers, understand more about the subject. Insight is very contextual, situational and could be subjective too. What is an insight for one category of users may not be an insight for another category of users. For example, an insight to sales manager need not necessarily be an insight to HR manager in the same company.

data, information, insight

Insight is the second level output from BI. Based on the information derived when users iteratively look for more answers/details in BI insight is obtained. It is based on this insight that the conclusions are made and decisions are taken. Insights triggers action.

Burger Chain Example 
When you have timestamp of several hundred burger purchases by different customers then with this collected information you can derive trends and patterns of purchase times, Example - More burgers are sold between 12 30 pm and 2 30 pm and more number of burgers are sold on Wednesdays and least on Tuesdays. Now you have insight into customers burger purchase timings, and therefore could come up with some changes in the business to improve the business.


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