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.
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.)
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 | 5 Comments |