Managers — Don’t be Deaf to Analytics

Within today’s diverse, cut-throat, and edgy corporate topography are multifarious success stories where every individual is taking a plunge into the deep end — revolutionizing businesses, banking on ideas, kick-starting initiatives, and swimming with the sharks. But what these and 91% of corporates around the world (According to the Avanade Global Survey, June 2012) have in common is one formula:

Big Data and Analytics = Big Knowledge = Big Returns

Most companies, no matter their standing or their revenue, are harnessing the latest tools and processes to collate and access data from around the world. These data management tools allow companies to leverage their business, so as to reach their goal of maximizing productivity and profitability. Data which:

1. Provides them with insights into the trends and needs of the target market in relation to their competitors
2. Tracks the company’s Return on Investment (ROI), performance, recruitment patterns and needs.

While data collation and segregation is one half of the equation, accurate data analysis is the other. Manish Parashar, Director of Rutgers Discovery Informatics Institute rightly tweeted, “Traditional decision-making structures must be adapted to incorporate data scientists in business and research.”

The decision makers of a company, be it the CEO, CFO, CXO, or mid-management who work closely with data scientists, must take precautions to arrive at the right, structured analysis, which not only promotes short-term success but also has a residual effect on the long-term agenda of the organization.

Every manager’s core strategy must leverage big data and predictive data analyses and thereby:

1. Increase revenues and shape the business’ internal and external operations with respect to the market and its competitors
2. Accomplish the deed in hand not only efficiently but also effectively because an accurate analysis leads to the formulation of a well defined goal/target
3. Identify loopholes, minimize risks, and reduce shortfalls of the company to strengthen B2C relationships and retain customers while expanding its reach
4. Optimize the operational performance of the team and achieve the projected goal in the desired business direction
5. Experiment and innovate in a controlled atmosphere, so as to to act on emerging opportunities and map potential leads
6. Utilize the company’s time and resources optimally by being at the top of their game and avoiding analytical mistakes
7. Arrive at a real-time conclusion based on mathematical reasoning and empirical values as opposed to hypothetical scenarios and a methodology based on the principles of trial and error
8. Lead a team where they can trust the employees with their data analyses because the managers can verify the analyses themselves and gather insights from it themselves

With the gap between the demand and supply of data scientists widening, companies have begun to insist that their management team fine tune their analytics skills themselves. After all, it is imperative that managers at all levels have a comprehensive understanding of analytical tools, processes, and infrastructure as well as its interpretation, implementation, and management. Only if they do, will they be able to gain a firm understanding of operations and channel the data analyses in the right direction. Only then will managers be able to recruit and lead a team of data scientists whose job it will be to talk to data intimately and whose insights will help define the company’s growth trajectory and ascertain success.

This is it. This is the new management mantra: Get Smart, Get Big. Don’t turn a deaf ear to the future. 

Interested in a career in Big Data or Data Science? Check out Jigsaw Academy’s courses and find out how you can get started:

Data Scientist Course

Big Data Specialist Course 

Contributions by Smit Zaveri.
Image courtesy: Gunjan Joshi and anatom5
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