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:
Five reasons industrial IoT demands the edge

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.;

 



Predictive Analytics Innovation summit San Diego
22 Feb

$200 off with code DATA200

Read Also:
Ensuring the quality of 'fit for purpose' data
Read Also:
How to apply natural language processing in the enterprise
Read Also:
Artificial intelligence is the next giant leap in education
Big Data Paris 2017
6 Mar
Big Data Paris 2017

15% off with code BDP17-7WDATA

Read Also:
Google secures five-year access to health data of 1.6m people
Read Also:
Does Your Customer Success Manager Need Data Science Skills? Part 2

Leave a Reply

Your email address will not be published.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