In the data science / statistical consulting space, I think there are 3 kinds of “need,” whether that’s individual or organizational.
1. Someone who is drowning in data
2. The “we already have analytics” crowd
3. Working in their business (e-myth) data scientists
Drowning in Data
This is the person who is always putting out fires in their life and business–always working in one of two “quadrants”: important-urgent or urgent-not important. They may or may not know that they are drowning in data, but it’s taking all their effort to just tread water.
For example, when the flight sciences department asks for another test sortie to measure X, and you are over 1,000 sorties into the test program–maybe, just maybe, they could find that data in the many terabytes of data already saved–but the algorithms or processes or expertise to mine that data just isn’t there.
Or the Wing Commander who wants to know “how this happened,” an airplane crash that exposes the rest of the iceberg, a breakdown in risk management practices: ORM has been part of the culture for years, but every day, the ORM data–written in marker–gets wiped off the ORM checklist, so they can start with a clean slate.
“We already have analytics”
These are the skeptics, either because they don’t know what value analytical tools of mathematics and statistics bring or because they have been burned by other purveyors of the trade. I believe these customers have the tools and need advice for mastering them.
The (e-myth) Data Scientists
Let’s be honest, many of us would rather work in our business, continuing to practice our technical expertise, rather than “on our business” as Michael Gerber, author of the E-Myth, put it so profoundly. Data scientists, statistical consultants, etc., might not know where to start. If you would rather learn a new computer language than learn to speak the language of the customer, we can help.
If you don’t know where to start, perhaps you can start be reading the white paper manifesto below…
ATOMs is a monthly column that introduces analytical tools of mathematics and statistics and illustrates their application. To read more about ATOMs, go to the incomplete index, read Where Do We Go From Here, or view the online workbook here.