A little while ago, I was giving a talk on predictive analytics, and a young professional in the audience interrupted me: “What exactly is a data scientist, anyway?” he asked.
Given the number of people who suddenly perked up to hear the answer, I realized he wasn’t the only one in the dark. If you’re wondering, too, allow me to shine a little light science on the topic.
Given the number of people who suddenly perked up to hear the answer, I realized he wasn’t the only one in the dark. If you’re wondering, too, allow me to shine a little light science on the topic.
As the Chief
Analytics Officer of AdvantEdge Analytics, I
direct some very smart people who are busy conducting data science for credit
unions. Let’s start there. Data science is the process of
extracting meaningful patterns from large sets of data. These days, data science has
proven important to all businesses, including credit unions. This is because it employs
reliable methods of analyzing data, discovering trends and identifying
business insights.
To be sure, many
analysts are involved in similar activities, but a data scientist is,
naturally, diving deeper into the data. In many ways, the data scientist spans the gap
between IT and the business. For most analysts, the data has already been
prepared for them, whereas data scientists discover data in the systems
themselves. They extract it, transform it and identify connections.
While working with
the data is part of data scientists’ activities, they also take that
exploratory analysis one step further. They codify and predict the outcomes of
the information they are studying. They constantly search for opportunities to build repeatable, technical
assets that we call predictive models.
Predictive models
are where the greatest business value lies for the credit union. It’s one thing
to use data to understand what’s been happening in the business. But, it is
quite another to use data to make accurate predictions of future outcomes. Once
these models are created, business areas can use the information to support actions like building marketing campaigns or
creating specific business initiatives.
For example,
let’s say you’re the credit union: You want to predict which members are
likely to churn from your portfolio and isolate the causes for leaving. Predictive
models allow you to do that. What’s more, they can help you identify the
appropriate actions and programs that can help you retain those members. And, they provide the
means to measure the success of the programs.
So, a data
scientist is a multidisciplinary expert with deep skills in mathematics,
computer science, statistics and computer programming. In my role, I am always looking
out for well-rounded data scientists. They typically must possess a great deal
of business acumen to go with their technical expertise. They also need to have
strong communication skills because they operate at so many different levels of
the business. In my
experience in the trade, it's typically a small cohort of experts that fit the
bill.
Unfortunately,
it’s not very likely that a credit union has the resources to employ a
full-time team of first-rate data scientists. The good news is that there are
now options in the marketplace. If you're looking to capitalize on the value
that data science can create for your organization, take a look at industry
partners like AdvantEdge Analytics. We have a full complement of
data scientists on staff, with the processes, practices and experience to help
deliver the full power of data science to your organization.
Want
to learn more? Watch this short
video. Read Data
and Analytics Toolkit: Practical Success Factors for Your Data Management
Solution. Or, connect
with AdvantEdge on Twitter or LinkedIn.
