Big Data Scientists- Demand outgrowing Supply?

Posted in Human Resources Articles, Total Reads: 350 , Published on 28 July 2016
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Big Data Analytics is one of the raging topics of this century. But before we go on to discuss this topic, let us first shed some light on what we mean by Big Data. Contrary to popular saying that Big Data doesn’t really mean ‘big’ data, I would say that yes, Big Data does signify big amount of data, here in volume terms. And analysts and data engineers who analyze and prepare this data for visualization are called data scientists. We will come to terms with these words and discuss them in details in the following paragraphs.


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There are huge amounts of data in the world. Much of these data is raw data, in unprocessed format. You may have a storehouse of knowledge, but if you do not know how to put that knowledge to use, then that is of no use. Similarly data accumulated would be of no use until and unless that data is processed and analyzed to gain insights into everyday business problems. Data scientists work on this raw data, organize this data and derive meaningful information out of them.


Fig 1 – Rise Of Big Data

The above figure shows how research work on big data has increased manifold in the last few years and it is set to rise. Big data applications have huge impacts on business and that is why organizations are increasingly focusing on working on collecting and analyzing this data and drawing out useful information for themselves.


Now there is data and then Big Data. Big Data comprises volumes of data from various fields and business. There are tons of example on Big Data and Big Data analytics. These data include everything from what consumers do, their income, buying potential, what kind of products they use, consumer preferences, etc. Analysis of such data is required in every stage of a firm’s lifecycle – be it introduction, growth, maturity or decline. Having an idea of the industry, competitors, how your product is faring in the market, customer sentiments and feedback provide valuable insights which is used to bring improvements and changes in your business.


We will discuss a few cases of data science applications in different industries. Manufacturing industries for instance has a lot of analytics involved. Organizations are using analytics to integrate their processes, increase co-ordination between various processes and resources, reduce costs and increase efficiency. Statistical control charts, inventory management, machine maintenance, allocation of resources, demand forecasting, managing storage in warehouses all have extensive applications of analytics.


Next we come to retail industry and supermarkets. Guest count, customer history details, sales volume, effectiveness of ads and promotions are analyzed to draw customer buying patterns, demand forecast, organizing storage layout and managing resources. Heat maps are used to find where customers are spending more time and which all products they are preferring and their order of buying. All these details are crucial to improve store layout, crowd management and understand customers. Security checks and unauthorized access are also handled by data scientists.


Social media has gained a lot of ground in the last few years. Organizations are increasingly focusing on social media promotions, gauging customer sentiments in popular social networking sites, travel websites, e-commerce sites and others and analyzing those reviews. Sentiment Analytics is done to understand the consumer behavior. Other cases include weather analytics to understand the impact of weather on businesses and change in customer buying patterns. Popular cases are impact of weather and different events like festivals, international events like cricket tournaments, NBL, football tournaments on hotel bookings, restaurant footfall, etc.


We can also take the case of healthcare industry. Here data on diseases occurred, rate of occurrence of various diseases, medicines prescribed, percentage of ingredients present in medicine are collected and analyzed to derive useful data for the medical industry. Chunks of data are collected and acted on to arrive at results through Big Data tools.


Fig 2: Demand for Big Data Analytics

Fig 2  throws light on the requirement of data scientists and big data analysts in organizations. Organizations are increasingly gaining knowledge of the power of Big Data Analytics and the effects in businesses and hiring skilled manpower to perform analytics. There are different tools required to do big Data Analytics. Tableau, QlikView, R analytics, Excel, SAS are the skills in demand in Big Data Analytics.


Organizations are increasingly hiring people with an analytical mindset and often with a strong mathematical or statistical background. But at present there is a dearth of people with the required skillset. Organizations are planning to hire thousands of data scientists by 2017, but the demand is not met with the supply. This has incentivized people from various fields to move into the world of Big Data analytics. The supply being less the pay is also comparatively higher compared to other jobs in this domain and people are flocking like moths to the flame to this world of Big Data Analytics.


The term ‘Big Data’ is also like a fancy term for many nowadays. People hear about the advent of Big Data and are interested in working in this field without doing an initial research or homework on this. Don’t be surprised if you come across someone thinking of Big Data as a tool. The aura and chorus around Big Data is drawing people towards analytics specific jobs, but one needs to be prepare himself/herself for this job. There is nothing very fancy or complex about Big Data. It is a major storehouse of data that has to be processed and analyzed to derive meaningful information from them. These results are then depicted through different visualization tools like Tableau, QlikView and others to clients and action points are discussed. Clients based on this suggestions and results decide on the constructive changes they need to introduce in their businesses.


This industry is definitely on the rise and there will be a huge demand for workforce in the coming years. But for that you need to be prepared! You need to have a thorough knowledge of what is in vogue in the market, which tools are being used, demand in specific segments, attend training programs and equip yourself with the required skills and knowledge accordingly. If you want to win the race, you definitely need to put a step forward to stay ahead of others, plan, prepare and execute it well to achieve success.


This article has been authored by Sourav Saha from IIM Raipur


References:

http://edition.cnn.com/2014/11/04/tech/gallery/big-data-techonomics-graphs/

http://www.edureka.co/blog/10-reasons-why-big-data-analytics-is-the-best-career-move


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