How to Advance Your Business With Analytics & Build the Right Team

by April 19, 2018 1 comment

By now, we’re all aware of how analytics is steadily shaping various industries & technologies. Its applications range from academia to financial markets, government to manufacturing. Despite the extensive usage of big data analytics worldwide, there is still ambiguity in forming a successful & precise data analytics team.

First of all, we need to understand that data scientist today are found with a different mix of skill-sets. Once a company sees the need to form a data analytics team, it needs to understand the following.


1. There Is A Direct Way To Form A Data Analytics Team – Simplifying various roles required to build a successful team.


DATA ENGINEER: They form the base of the team. An entry point. Technically speaking, their job involves data warehousing, ETL (Extract, Transform, Load) Process, and similar tasks. Data Engineers prepare, update & optimize data for data scientists and data analysts who are at the succeeding levels of the team.

BUSINESS ANALYSTS: Also known as Business Intelligence Analysts or Data Analysts. Business Analysts use the preprocessed data and develop insights in order to make informed decisions for the business. These insights also act as solutions to company oriented problems.

DATA SCIENTISTS: There was a need to distinguish between an analyst and a data scientist because the latter group performs high-level tasks involving Machine Learning & Artificial Intelligence. They work on unstructured data to solve complex problems using algorithms. A data scientist is capable of doing all the above roles of the team by himself but dividing the tasks will only mean faster utilization of his or her time and more focus on optimizing the algorithms.

As a whole, these three roles can act as blocks wherein the bottom one supports the one on top and the upper block gives meaning to the tasks completed by the blocks at the bottom.


2. A Rather Indirect Way of Forming A Team Is By Giving Emphasis To The T-Shaped Skills Concept.


The T-Shaped Skills Concept → The horizontal bar in the ‘T’ represents a broader knowledge set whereas the vertical bar represents in-depth specialized knowledge of a particular skill. As per an introspective survey of data scientists and their work, “T-shaped” data scientists have an advantage in breadth and depth of skills.

 The aim is to find the right balance in the mix of people wherein the team has the precise amount of skills required for expertise along with a broader knowledge of skills representing the horizontal bar that will provide effective collaboration amidst the team members.

For instance, you may have a mathematics expert with sufficient domain knowledge who can collaborate with the domain experts in using the right statistical techniques and formulae. Or you may have a programming expert with extensive knowledge of machine learning who can collaborate with members having domain expertise and adequate programming skills. Domain experts are also responsible for communicating with stakeholders & bridge the gap between the team & all external entities.

The crucial skill-sets required in a data analytics team are as follows –

•  Business or Domain Knowledge

•  Machine Learning and/or Big Data

•  Programming

•  Statistics and Mathematics

It is all about knowing whether you already have them on your team or not.


A Need to Involve Leaders in Analytics

Team leaders and managers also need to be comfortable in analytics. The leaders need to have knowledge about what data is to be used, from where is it accessed & governed and how will it be effective to the project and subsequently in the decision-making process. After all, it is all about improving the business using data. So the leaders need to be involved in the process of solidifying numbers. As per experts, a person need not obtain a degree in computer science. A “working knowledge” of data science is enough for a leader to be involved thoroughly in the process.

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  1. jimothy halpert
    #1 jimothy halpert 21 April, 2018, 04:00

    insightful. succinct. clever.

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