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Wayne Eckerson

Welcome to Wayne's World, my blog that illuminates the latest thinking about how to deliver insights from business data and celebrates out-of-the-box thinkers and doers in the business intelligence (BI), performance management and data warehousing (DW) fields. Tune in here if you want to keep abreast of the latest trends, techniques, and technologies in this dynamic industry.

About the author >

Wayne has been a thought leader in the business intelligence field since the early 1990s. He has conducted numerous research studies and is a noted speaker, blogger, and consultant. He is the author of two widely read books: Performance Dashboards: Measuring, Monitoring, and Managing Your Business (2005, 2010) and The Secrets of Analytical Leaders: Insights from Information Insiders (2012).

Wayne is founder and principal consultant at Eckerson Group,a research and consulting company focused on business intelligence, analytics and big data.

One of the unwritten jobs of an industry analyst is to define industry terms. This is risky business because no matter what you say, most people will disagree.

Our industry (and most industries) has a semantics problem. The most commonly used terms are always the most abused semantically. Everyone creates definitions that align with their individual perspectives. This is especially true among software vendors which must ensure that definitions harmonize with their product portfolios.

One of the more popular terms in recent years is analytics. The root of the word is "analysis" or "analyze". Technically, to analyze something is to break it into its constituent parts. A less formal definition is to examine something critically to understand its essence or identify causes and key factors.

Who better then to define analytics than an industry "analyst"? We presumably spend every day "thinking critically" about software and vendors. (This is also a wonderful way to justify a liberal arts education, whose primary mission is to teach students to think critically.)

To increase my chances of gaining consensus, I'm offering two definitions of analytics. (Yes, this is wishy washy, but bear with me.) We need two definitions because every commonly used industry term has two major dimensions: an industry context and a technology context.

So, given this context, Analytics with a capital "A" is an umbrella term that represents our industry at a macro level, and analytics with a small "a" refers to technology used to analyze data.

Capital Analytics

From a macro perspective, Analytics is the processes, technologies, and best practices that turns data into information and knowledge that drives business decisions and actions.

The cool thing about such industry definitions is that you can reuse them every five years or so. (For example, I used the same definition to define "Data Warehousing" in 1995, "Business Intelligence" in 2000, and "Performance Management" in 2005.) Our industry perpetually recreates itself under a new moniker with a slightly different emphasis to expand its visibility and reenergize its base. (See my blog "What's in a Word? The Evolution of BI Semantics.")

Today, many people use the term Analytics as a proxy for everything we do in this space. The most prominent person who defines Analytics this way is Tom Davenport, whose Harvard Business Review articles and books on the subject have prompted many executives to pursue Analytics as a sustainable source of competitive advantage. Davenport is savvy enough to know that if he had called his book "Competing on Business Intelligence" instead of "Competing on Analytics", he would not be the industry rock star that he is today. (I still prefer the term "Business Intelligence" because it perfectly describes what we do: use information to make the business run more intelligently.)

Small Analytics

This leaves the term "analytics" to describe various technologies that business people use to analyze data. This is a broad category of tools that spans everything from Excel, OLAP, and visual analysis tools to statistical modeling and optimization tools. There is a natural divide within these technologies so I'm tempted to create two sub-definitions: deductive analytics and inductive analytics.

(Interestingly, all of our former capitalized terms now refer to a category of tools: data warehousing refers to data modeling and ETL tools; business intelligence refers to query and reporting tools; and performance management refers to dashboard, scorecard, and planning tools.)

Deductive and Inductive Analytics

With deductive analytics, business users use tools like Excel, OLAP, and visual analysis tools to explore a hypothesis. FIrst, they make an educated guess about what might be the root cause of some anomaly or performance alert. Then, they use analytical tools to explore the data and either verify or negate the hypothesis. If the hypothesis proves false, they come up with a new hypothesis and start looking in that direction.

Inductive analytics is the opposite. Business users don't start with a hypothesis, they start with a business outcome or goal, such as: "What are the top 10% of our customers and prospects who are most likely to respond to this offer." Then, they gather historical data that they think might correlate with the desired behavior. They then use analytics to create statistical or machine learning models that they can apply to data to prioritize their customers. In other words, they don't start with a hypothesis, they start with the data and let the analytical tools discover the patterns and anomalies for them.


As the saying goes, there are many ways to skin a cat. Although I've offered two definitions of analytics (or Analytics), you are welcome to define it however you want. And you probably already have.

But remember, words are very powerful. They are our primary modes of communication. The more people you can get to use the same meaning of things, the more power you have to communicate and get things done. (So I hope you use my definitions!)

Posted July 8, 2011 10:29 AM
Permalink | 6 Comments |


Thanks Wayne for this post.

Couple of remarks:

1.We have users that simply explore data by using already build reports or new reports that they build.

2.We have analysts that can start with a hypothesis an then go to the data.

3.My understanding to what "analysis" means is that BI in most cases isn't making new intelligence ,it's making information.

The ability to connect the dots,making the data into contexts and making an output\multipule outputs from questions like :why,how,in which ways,finding trends,similarities and finding reasons can bring some sense of intellignece to the data-user interaction.

"Account number" can only have one meaning in the DWH but can be used for different purposes:

How many customers like product X?,why did they like it ?,did thet like other things as well ?
and so on...

What i am missing is BI that can also offer an intergrated hypothesis and data exploration accordingly ,offer new ways how to analyse the data and ofcourse integrate data from different sources in the hypothesis and data exploration level and not just in the DWH level.



I briefly touch on analytics in every marketing and entrepreneurship class I teach and was struggling to come up with a tight definition appropriate for students. Starting with the data and letting the tools find the patterns is just what I was searching for. Thanks!

Thanks for defining the term and giving us the opportunity to think in our way and to validate with yours

"Analytics refers to the processes, technologies, and best practices that turns data into information and knowledge that drives business decisions and actions". I am not sure whether technology matters; so i am thinking like this: Also, optimization is fundemental that justifies the process of analysis and moreover analysis starts with observation/data, and it does not have to be a business decision.

"Analytics refers to data and best practices that turns data into information and knowledge to drive optimal decisions and actions"

Yes, I like that definition too!


Sounds like you are ahead of the curve and your using BI in the appropriate way. Data is just a raw material for intelligence. Or as someone said recently, data is the fuel of the new economy. Most users just view it as presented, but analysts are paid to dig deeper and repurpose data into new insights and objects.

Hello, sir.

I found your article really helpful. And, you've a great style of writing. It's tough to find such a genuine style on the internet nowadays. Thanks for your wisdom. I have taken down notes from this article.

Have a nice day!

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