"Yeah, well, you know, that's just, like, your opinion, man."
– "The Big Lebowski"
Analytics has never been sexier in the world of business. Big data, artificial intelligence, and machine learning are all terms that fill executives with excitement at their potential — or dread at falling behind. For companies investing in these technologies, how can they raise the odds of success?
Broadly, there are four critical factors for success in analytics. Obviously, these may be applied differently depending on the sort of analytics you are implementing, but the core principles are the same. Of the four, three are operational, and one is deeply cultural, even emotional:
1. Technology. A well-designed, flexible IT platform is critical for successful analytics. Most important is that the data be available to users in a consistent and reliable fashion. In the real world, a majority of resources (technology and people) are spent assembling the data for analysis.
The field of information management for analytics is about 25 years old — a youthful, immature engineering discipline compared with many others: civil engineering (2,000 years old; the Romans could grade roads); electrical engineering (150 years old); and computer engineering (50+ years).
2. Lean processes. Lean, Total Quality, Six Sigma — whatever methodology you use, a company's information is only as usable as the consistency and standardization of its processes. How can different departments agree on what numbers mean if they don't agree on what they are measuring? This pursuit of "a single version of the truth" has always been critical to business intelligence, and it continues to be critical in more-advanced forms of analytics such as data science and machine learning.
3. Alignment with business goals. In general, advanced analytics does better when it is built with an initial purpose (for example, to reduce a bank's bad loans by X percent), rather than simply to have a world-class analytics department.