Market for Big Data Jobs Expected to Surge 30% By 2020


The big data profession has exploded in recent years. The trend is expected to accelerate even further in the near future.

A number of experts have indicated that more big data jobs will be created by 2020. One of the most compelling studies was conducted by IBM. This study showed that the number of big data jobs is expected to rise by 28% by 2020.

This study was very encouraging. A newer study from PwC is even more optimistic about the growth rate for big data jobs. The IBM report predicted that the number of big data job openings would grow from around 360,000 to 2.7 million by 2020. In other words, around 2.3 million new big data jobs would be created. The PwC report predicts that 2.7 million new jobs would be created. This means that the total number of big data positions will grow by around 30%.

Murray de Villiers, the expert that oversees academic programs for SAS, said that a growing number of campuses are developing new curriculums to address the growing need for big data specialists.

“Academia and industry must work together to develop courses that adequately prepare students for ‘real-world work’. This then should help to address the globalized, technology-driven business landscape that we now see in our rapid-paced world. It is clear that these new fields are long-lasting and valuable. Analytics are the skills, technologies, and practices that drive decision-making, and are vital for deriving maximum business value from organizational investments.”

Demand for Big Data Solutions Propels Growth for Job Market in the Years to Come

It is easy to see why the need for big data experts is growing so rapidly. There are many new applications of big data. They include:

  • Using machine learning to enhance the effectiveness of various algorithms
  • Using predictive analytics to forecast likely events and develop better models for preparing for them
  • Helping organizations develop more nuanced understandings of their customers with more insightful analytics data
  • Helping companies assess future demand in light of likely events (such as oil companies needing to scale production in light of a major natural disaster or global energy crisis)
  • Identifying likely threats and contingency plans with historical data and predictive analytics algorithms
  • Giving healthcare providers more detailed insights into the health of their patients

The applications of big data are virtually endless. As algorithms and data storage capabilities evolve, the demand for data scientists to help address them will grow.

Most Big Data Job Growth is in the Financial Sector

There are countless applications for big data, which is one of the reasons why the demand for jobs in the field is growing so rapidly. However, despite the growing demand for big data jobs in many sectors, the demand for big data jobs is mostly consolidated in a couple of fields.

Healthcare and finance account for most of the jobs and the future growth. IBM found that 59% of big data jobs are in the financial sector.

There are a number of reasons that the financial sector is creating more big data positions than ever. Here are some of the reasons the financial industry is so dependent on big data and is hiring more data scientists to meet its needs.

Developing Better Automated Trading Solutions

Automated trading is one of the fastest growing arenas of the financial services sector. It depends on very complicated algorithms that are meant to streamline the trading process. Automated trading systems depend on the following variables:

  • Historical trends based on similar price patterns, irrespective of other developments in the market
  • The impact of major geopolitical events on asset valuation
  • Changes in other asset values, along with the beta coefficients between the different assets
  • The amount of interest that institutional buyers have shown in a specific asset or asset class and the probability that those buyers will make a purchase based on available information
  • The probability that a market will experience a correction after a major price increase or decrease in an asset

Automated trading systems account for all of these variables and utilize predictive analytics models to make educated decisions. They make trades without needing to consult the traders. This allows them to maximize value for people that would otherwise make decisions based on emotion, rather than hard data.

Automated trading is transforming the market in other ways as well. In addition to helping traders reduce their risk profile and maximize the returns on their investments, automated trading systems also help reduce volatility in the market. As more traders use them, the number of people making spontaneous trades based on hype will drop considerably. This will stabilize the markets, which will be more responsive to long-term indicators.

However, the algorithms used to operate automated trading systems are still far from perfect. This means that there will still be a need for talented big data professionals. In addition to understanding the nuances of big data technology, they will need a strong grasp on the following:

  • A clear understanding of the variables that affect the long-term value of financial assets. They will want to develop algorithms that focus on long-term profitability. If the system is designed to make trades based on the same short-term factors that day traders use, then it will lead to the same market volatility and low returns.
  • A detailed knowledge of various financial markets, which means they must know how the markets for different assets and regions work.
  • An understanding of the benefits and drawbacks associated with time delays for some of the trades that are placed. If all trades were programmed to go through at the same time, that could cause a huge level of volatility. This would also lead to customers paying higher prices for assets if they were all purchased at the same time or selling at larger volumes if they were all sold at the same time. However, delaying some trades could create the perception of nepotism among customers, even if it led to higher returns for all of them.

Data scientists understand that automated trading is very complicated. They must develop the right solutions for it.

Better Financial Planning Strategies

Improving financial planning for customers is also very important. In fact, for 90% of the population, having dependable financial planners is more important than getting a great ROI from any given trade. Most people don’t trade assets on their own. However, everybody needs to understand the financial needs they will face during retirement and plan accordingly.

Big data has played a very important role in the financial planning profession. Data scientists have aided their clients by performing the following:

  • Predicting the life expectancy of any given client, based on the life expectancy of the general population, the client’s career history, gender, genetic conditions, medical history and lifestyle patterns. This is important to help clients understand how much they need to save in retirement to last the rest of their lives.
  • Algorithms that account for future Social Security cuts or increases. This can be done by evaluating the long-term history of the Social Security program, the political decisions that affected changes in the past and demographic variables that are changing it.
  • Future budgets for customers that account for inflation.
  • Algorithms that help customers determine whether or not moving is a prudent financial decision. These algorithms will take into consideration a variety of variables, including the average salary for their careers, the regional cost of living and the cost of moving. Financial planners usually have difficulty helping clients make these decisions without the support of computer algorithms, because they are very complex. It is much easier to use computerized models to help people make these decisions.

Big data is going to play an even more important role in financial planning in the future.

What Other Sectors Depend on Big Data?

Big data is becoming increasingly important in almost every sector. Here are some of the industries that depend on it the most.


After financial services, the healthcare sector is the biggest employer for big data professionals. While the financial services sector is the biggest employer for the time being, Rasmussen University has predicted that the big data job growth will be faster in the healthcare sector than financial services or any other industry.

Supply-chain logistics

Supply-chain management is another field that is very dependent on big data. Supply-chain management specialists need to look at a number of factors, which include:

  • Weather patterns in various areas that could stall deliveries
  • The safest routes to take when handling large freights
  • The performance of various drivers to determine whether they need additional training or support
  • The safety risks associated with handling certain cargo and the best approaches to keeping it safe

Data scientists have helped create sophisticated algorithms to address all of these concerns.





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