You may
have recently hired a data scientist or two to put some analytic punch behind
one of your current offerings. Guess what? Your customers are doing the same
thing. So if you plan to meet with a customer to talk about your new analytic
product or service, don’t be surprised if they show up with their own whip-smart
data scientists to evaluate your product.

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What’s driving
the demand for more data scientists?

As the
base competence for science, technology, engineering, and mathematics (STEM) students
grows, there will be an increasing demand to cater to their needs. The
progression started when insightful STEM students started to realize the need
for processing huge volumes of data in different, unconventional formats at
very high rates of speed.

Pure-play big data solutions (i.e., products like
Hadoop that specifically cater to big
data needs) entered the scene. From there, visionaries saw the competitive
implications of using big data analytics in their offerings (e.g., Progressive rewards good drivers by
analyzing real-time driving data). Now, as adoption accelerates, people want to
know what’s under the hood, and they don’t mind learning and/or buying the
analytic competence to get their questions answered.

It’s a
fair demand — everyone knows it’s naive to invest in something you don’t
understand. The recent financial meltdown in 2008 was caused in large part by
people buying obscure financial derivatives like Collateralized Debt Obligations (CDOs) that they didn’t understand. We all know how that
turned out.

fancy financial instruments remind me of some of the big data analytic
solutions available today. There are a lot of colorful brochures with people in
white coats explaining how dazzling analytics can solve your biggest problems.
That’s not enough anymore — smart people want to know more details, and your
offerings should make it easy to find those answers. You’d never buy a car that
had its hood sealed shut, so why would you expect your customers to invest in
your black-box analytic solution on blind faith?

Something for everyone

should build your analytic solutions to cater to multiple needs, including
other data scientists who want to know how the machine works. Consider how a
database management system (e.g., Oracle) is built. It caters to the needs of
developers who need to create tables and manage data; however, instrumentation
features (e.g., logs and trace files) are also critical for database administrators
(DBAs) to keep the lights on.

no need to divulge proprietary information. Oracle doesn’t share the intricate
details of how it does cost-based optimization, although it does allow you to
generate a trace file with an amazing amount of detail on how the optimizer
decided to execute a query. This attitude can be applied to the design of your
analytic solution as well. Think about the kinds of questions other data
scientists might ask about your offering, such as:

  • If your solution involves causation, are you
    using logistical regression, neural networks, or something else?
  • How did you come up with your variable set, both
    independent and dependent?
  • Did you do qualitative research, or did you use
    another data mining tool?

In your
design, keep in mind that you’re building commercial features into your
solution and not some backdoor for the intellectually curious. You must treat
the analytic community as actual end users, not just as hobbyists who take an
interest in your solution.

Oracle’s cost-based optimizer first rolled out, you could get details on its
logic, though it required an estimable degree of knowledge in the obscure art
of reading trace files. Furthermore, these techniques weren’t in the official
documentation — you needed a connection with the underground DBA network to
garner this knowledge. Don’t make it this difficult for data scientists to get
basic information about how your analytic solution operates; you should build
these features right into the solution.


considering the design of products that involve big data analytics, remember to
address the customers’ functional needs and the data scientists’ non-functional
needs, because the analysts are going to help the customers evaluate your
product. You also need to be ready for a cross-examination from a fellow data

bottom line is make sure you have a great engine to show off when customers and
analysts lift your product’s hood.

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