8 Ways to Think Like a ‘Quant’

Data analytics is a skill that has applications at every level of the enterprise. For marketers, there are opportunities to slice and dice customer segments to better understand their purchasing patterns. For CEOs and CFOs, data analytics opens up insights into financial trends. For IT managers, data analytics provides insights into system performance and website usage.

That’s why many enterprises and pundits are enamored with the emerging concept of the “data scientist” – a well-trained individual capable of diving into big data stores and building models on how their businesses will fare if certain actions are taken.

However, data analytics shouldn’t be limited to data scientists, and their fellow quants and analysts. Everyone working on significant business problems needs to have that quant within them. Tom Davenport, visiting Harvard professor and co-author of “Keeping Up With The Quants: Your Guide to Understanding and Using Analytics,” puts it well – there’s no reason why everyone in the organization can’t have analytical skills. Here are Davenport’s recommendations for thinking more like a quant:

1) Ask questions. Don’t be afraid to dig in and challenge information being presented. “Many people are reluctant to ask questions about numbers because they’re afraid of appearing foolish; this fear is exaggerated.” some good questions to ask: “Do you have any data to support this hypothesis?” “Can you tell me something about the source of data you used in your analyses?” “Are you sure that the sample data is representative of the population?”

2) Learn to tell a story with data. To entice business leaders, data-based presentations should have a compelling story to tell, versus simply being rows of numbers.  “Come up with your ending before you figure out your middle,” he explains. “Endings are hard, get yours working up front.” And most of all, Davenport urges, be creative with the questions to be asked and the eventual analysis. “The essence of creative data analysis is in finding a pattern among all the variables in the data.” An analysis might suggest, for example, that “customers with a certain purchase pattern are likely to stop buying altogether.” Or, patterns may emerge, such as the classic beer and diapers placement in grocery stores – it’s often men coming in to restock on diapers for their children.

3) Be curious. Part of the creative process with data analytics is having a healthy yearning to keep digging into data. “When you are curious about numbers, your understanding of them deepens and your knowledge increases considerably,” says Davenport. “Curiosity about all aspects of numbers is the hallmark of a good quantitative analyst.”

4) Demand numbers. Every proposal, presentation, idea or observation seen within the enterprise should have some backing with hard data, Davenport states. “Good quantitative thinkers – and organizations that want to nourish them – should always demand numbers when someone presents ideas, hunches, theories, and casual observations.” The same is also true for presenting information to others: “You should have the urge to seek hard data before you fully develop your theories.” Davenport adds that “innumeracy plagues too many otherwise knowledgeable people.” However, don’t become too enamored with the numbers themselves – data analysis requires something above and beyond mathematics skills. “Math is not the key to quantitative thinking, but rather their approach to classification of quantitative information.”

5) Never trust numbers. Paradoxically, while data analysis skills calls for a love of numbers, at the same time, they should never take figures at face value. “Just as with a new acquaintance, don’t trust data until you know more about it.” The numbers may have come from a biased source, or too small of a sample. “The surest way to be confident about any numbers presented to you is to be skeptical of them, and learn more about their background.”

6) Make Google (or Bing) your friend. Having a great deal of curiosity about the date behind a business problem is what separates analytical thinkers from the rest of the pack, Davenport states. Any time you hear something you don’t understand, go to a search engine and learn more about it. “Because many people are not familiar with many terms and numbers, they generally just let them slide and move on,” he writes. “Those aiming to become quantitative analysts, however, should always memorize or jot down those terms and numbers, and Google them later to clearly understand what they mean.”

7) Don’t trust causation arguments. While two trends may appear connected, one thing doesn’t automatically lead to another. “If you simply find a statistical relationship between two factors, it’s unlikely to be a causal relationship.” To illustrate this point, Davenport says a newspaper headline may announce that “heavy drinking causes cancer in ten-year study.” It’s unlikely, he says, that “test subjects would be randomly assigned to groups and asked either to drink heavily or not drink for ten years.” More likely is that the researcher found a correlation against self-reported information.

8) Go back to school. Continuing education will help you get up to speed with quantitative analysis and statistics. There is an abundance of course materials – often free, through online courses – that will help you develop your data analytics skills.

Joe McKendrick is an author and independent researcher, covering innovation, information technology trends and markets.  Much of his research work is in conjunction with Unisphere Research/ Information Today, Inc. for user groups including SHARE, Oracle Applications Users Group, Independent Oracle Users Group and International DB2 Users Group. He is also research analyst with GigaOM Pro Research.

He is a regular contributor to Forbes.com, and well as a contributor to CBS interactive, authoring the ZDNet “Service Oriented” site, and CBS interactive’s SmartPlanet site.

Joe is a co-author of the SOA Manifesto, which outlines the values and guiding principles of service orientation in business and IT.

In a previous life, he served as communications and research manager of the Administrative Management Society (AMS), an international professional association dedicated to advancing knowledge within the IT and business management fields. He is a graduate of Temple University.