Pen-and-calculator-on-spreadsheet-budgets-accounting-close-budgeting-data-finance

Market Insights: How to Develop Data Science Expertise

Market Insights: How to Develop Data Science Expertise

In many market insights teams, the typical researcher will probably have a good grounding in statistics along with having studied consumer behavior, and they almost certainly feel quite comfortable being asked to analyze data to find important themes and patterns.

However, most people with that background today wouldn’t be hired by one of the large market research firms, because they’re now looking for “data scientists.”

Calling someone a data scientist is not simply fancy semantics or just another name for a really good data analyst. Instead, data scientists combine an array of talents, including statistical expertise, computer science, hacking skills, business knowledge, data visualization, and storytelling.

This combination of diverse skills with deep specialization can make it a daunting task for in-house market insights teams to build out their analytic capabilities. There are three steps that will help them hire employees or develop existing employees to do this (CEB Market Insights Leadership Council members can learn more from this research).

Read Also:
GoDaddy Revamps BI Tools To Enable Self-Service

Evaluate your current analytic maturity: There are four main stages of increasingly advanced analytics capabilities, and identifying where your team lies is a critical first step. Descriptive analytics: This answers the question, “What happened?”. Most companies have fairly strong capabilities at this fundamental level. Diagnostic analytics: More than just what happened, this stage understands “Why it happened?”. This stage begins to broaden the team’s understanding of customer behavior. Predictive analytics: This is a case of looking forward and answering, “What is likely to happen?”. CEB research found that only 13% of companies use predictive analytics consistently. Prescriptive analytics: “How can we make it happen?” This is the holy grail of data science. Using prescriptive analytics, researchers are able to understand what happened, why it happened, what is likely to happen in the future and, most importantly, how to influence that outcome to maximize the company’s success.;

 



Data Science Congress 2017

5
Jun
2017
Data Science Congress 2017

20% off with code 7wdata_DSC2017

Read Also:
5 Signs Your Healthcare Organization Needs Data Governance
Read Also:
Not up for a data lake? Analyze in place

AI Paris

6
Jun
2017
AI Paris

20% off with code AIP17-7WDATA-20

Read Also:
How Big Data is Transforming the Restaurant Industry

Chief Data Officer Summit San Francisco

7
Jun
2017
Chief Data Officer Summit San Francisco

$200 off with code DATA200

Read Also:
Why You’re Not Getting Value from Your Data Science

Customer Analytics Innovation Summit Chicago

7
Jun
2017
Customer Analytics Innovation Summit Chicago

$200 off with code DATA200

Read Also:
Using AI, Microsoft hopes to treat blindness

HR & Workforce Analytics Innovation Summit 2017 London

12
Jun
2017
HR & Workforce Analytics Innovation Summit 2017 London

$200 off with code DATA200

Read Also:
Can big data catch the bad guys?

Leave a Reply

Your email address will not be published. Required fields are marked *