Definitions: Demystifying data analytics jargon

In my last blog I wrote about the number of incredible opportunities with great potential for mind+machine. If this topic resonates with you and you are keen to explore the possibilities within your organization, let’s take a step back and define some of the basic terms.

If you have some experience in the world of data analytics, you might be familiar with the term ‘use case’, but for everyone else, this is an important one:

“An analytics use case is the end-to-end analytics support solution applied once or repeatedly to a single business issue faced by an end user or homogeneous group of end users who need to make decisions, take actions, or deliver a product or service on time based on the insights delivered.”

What are the implications of this definition? First and foremost, use cases are about the end users and their needs, not about data scientists, informaticians, or analytics vendors. Second, the definition does not specify the data. Whether humans or machines or a combination thereof deliver the solution is also not defined. However, it is specific on the need for timely insights and on the end-to-end character of the solution, which means the complete workflow from data creation to delivery of the insights to the decision maker. An example for a use case is the monthly analysis of the sales force performance for a specific oncology brand in the pharmaceutical industry in Germany, which will influence the local marketing spent. The marketing manager needs the insight of whether a certain campaign had an impact in order to decide whether to continue investment.

Next, we should consider the difference between data, information, insight and knowledge. Some of these words seem interchangeable, but in the world of data analytics you should use them correctly:

Data: This can be raw or cleansed data, is a set of quantitative or qualitative variables, which without further analysis don’t reveal any meaning. Raw data is of little value to a decision maker. An example of data could be a set of credit card transactions.

Information: This is data that has been analyzed to a certain extent and it has some value, but it still isn’t what a decision maker needs to inform a critical business decision. An example would be the discovery that the set of credit card transactions has five unexpected outliers where payments were made in a different country.

Insight: This is the ‘so-what’ resulting from the information, which allows the decision maker to make informed decisions. To continue our example, it might be impossible to cross time zones quick enough for each payment to be legitimate and therefore the card will be blocked.

Knowledge: This is the essence of what analytics should aim for: insights that have been made re-usable over time; multiple people in multiple locations can apply them to make successful business decisions. To finish the example, the decision maker can produce a trigger that automatically disables payments if they are made over different time zones in to shorter time period.

Having defined these terms, I am looking forward to delve deeper into the world of mind+machine in the next blogs. You might already want to ask yourself, is mind is more important in the creation of data, information, insight or knowledge?

If you are keen to learn more about the topic, you might be interested in my book.

Marc Vollenweider Headshot: How to Gain a Competitive Edge
Marc Vollenweider
Co-Founder and Chief Strategist Posts

Marc Vollenweider is Chief Strategist, Board Member and Co-founder of Evalueserve. Prior to starting Evalueserve, Marc was with McKinsey & Co for 10 years, including two years as a partner in Switzerland and India. Marc has vast consulting experience in various industries, such as telecom, pharmaceuticals / healthcare, banking, insurance, transportation, and logistics.

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