What Will the Data Science World Look Like in 2020?

//What Will the Data Science World Look Like in 2020?

What Will the Data Science World Look Like in 2020?

There’s no shortage of opinions or predictions on what’s ahead for the field of data science, dubbed in 2012 as the “sexiest job of the 21st century.”  But first, let’s look at some picture-painting stats concerning the job market for this exploding field.

  • Job openings have skyrocketed by an incredible 650% since 2012.
  • By 2020, an IBM study predicts a need for 28% more data scientists worldwide
  • LinkedIn notes a current shortage of 151,717 people with data science skills in the U.S.

“Data scientists are needed in just about every industry,” states  Nadia Hlebowitsh. “By looking at the demand of different data science categories, we can see three industries jump out: finance, professional services, and IT. However, this leaves 41% of the demand in other industries, including health care and retail.”

Dan Wulin, Head of Data Science & Machine Learning at Wayfair, tackles the title question with a thought-provoking look into the future, noting three broad trends.

  1. Increasingly sophisticated data science algorithms will continue to be subsumed in packages and technologies that make them orders of magnitude easier to deploy.
  2. Winning companies will continue adopting machine learning, AI, and related techniques in ways that influence their businesses in fundamental ways.
  3. Academic programs will increasingly expose students to software engineering, statistics, and other related disciplines.

“As data science continues to proliferate in coming years,” shares Wulin, “there will be increasing value to being able to tie the underlying techniques to business problems in a meaningful way and technically trained individuals with solid communication skills and business common sense are probably in the best position to capitalize on this.” Check out his detailed analysis here.

Trends to watch for

1.  Data Analysis Automation

By the year 2020, we can expect to see over 40% of data-based tasks automated, resulting in a higher rate of productivity and broader usage of data by citizen data scientists.

“Automation will additionally assist decision-makers to efficiently see further ahead to assist in moving their organization forward with the correct analytics to drive decisions,” notes freelance data and analytics consultant Edward Huskin.

2. More Data Science Strategies

“Data Science is nothing but a quantitative approach to a problem,” declares Oleksii Kharkovyna. “In the past, due to lack of data or processing power, we relied on other things, like ‘authoritarian whim,’ ‘expert intuition,’ and ‘general consensus.’ Today that doesn’t work at all, and without question, it will be even less effective ten years from now.”

The solution? Data scientists must build systems that can speak, predict, anticipate, and give real results. Kharkovyna predicts the introduction of data-driven strategies will continue to gain prevalence. “More people will look at data, gain insights from it, and so it may lead to the use of the data science team as an integral part of any successful organization or, at least most of them. It may even cause competitiveness and desire to be on the top.”

3.  Growth in graph processing and databases

Gartner, a leading research and advisory company, predicts the application of graph processing and graph databases will grow at 100% annually through 2022 to continuously accelerate data preparation and enable more intricate and adaptive data science.

“Graph processing and graph databases enable data exploration in the way that most people think, revealing relationships between logical concepts and entities such as organizations, people, and transactions,” states Rita Sallam, Distinguished VP analyst and Gartner Fellow in the Data and Analytics team.

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By |2019-12-24T06:40:23+00:00December 24th, 2019|Blog|0 Comments

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