The need for citizen data scientists — people who can use real-time, data-powered insights — will be on the rise with the widespread availability of data and AI-enabled, self-service analytics platforms. So, the best job of the century may be at risk. Smart machine learning algorithms can analyze and interpret data, and even produce content for dashboards and printable reports. So, who needs a data analyst or data scientist now? Amid all speculations, you may be surprised to know that the U.S. Bureau of Labor Statistics has indicated 11.5 million Data Science job openings will be created by 2026

The Biggest Obstacle to Being a Perfect Data Technologist

As the evolutions of Data Science and associated data technologies over the years have proved, it is very hard for a single expert to keep up with diverse fields of study such as computer science, computer engineering, neural science, statistics, mathematics, artificial intelligence and associated fields, and even the newer technologies like Big Data, Hadoop, IoT, serverless, edge — the list goes on and on. And yet, the global IT industry expects the data scientists to be a “jack of all trades,” which precludes the warning “master of none.”

Although well-educated and experienced data scientists — who often actually fill the roles of data engineers, data analysts, or advanced programmers — are lured into attractive job titles with loosely structured job descriptions, in many cases, these data nerds eventually get bored or exhausted with never-ending technology learning curves. The reality of Data Science is that it indicates an “intersection of too many disparate disciplines,” and it is humanly impossible for one person to be an expert in so many fields.

If autonomous technologies did not take over, the human data technologists would have soon abandoned their careers out of exhaustion. In a way, it is good that AL has ushered in a broad spectrum of autonomous technologies and tools that are here to work along with the humans to provide the best possible outcomes. The humans are best at being “thinkers,” and no one other worldly entity can possibly grab that role from them in the near future. The machines, however, are “doers,” who can take advantage of human knowledge and wisdom, and autonomously act based on their own learning. Machine learning has shown the way.

Predictions 2019: Data Analytics Trends to Watchdiscusses that in the post-Big Data world of business analytics, it is less about hoarding data and more about acting intelligently on the data-driven insights.

Let’s Begin with the Good News

Here’s some good news for Data Analysts:

• The Tag Innovation School, which conducted a survey of 550 Italian SME businesses, found that 50 percent of the surveyed businesses have plans to hire a data analyst in the next three years through 2021.
• The World Economic Forum has forecast that data analysts will be in high demand by 2020.
• Women are giving tough competition to men in data analysis field — the female to male data analyst ratio is 41 to 59.
• There is a growing demand for “interpretation of data,” which machines have not fully mastered as yet.

The Future Impact of Data Science on Business Analytics seems to suggest that the most important role a data scientist will play in the near future is that of a data analyst. The reasons behind this growing trend are the full automation of many Data Science tasks and the growing importance of data analysis by humans.

How to Future-Proof Human Data Technologists

Every global business acknowledges that “data and analytics” will be the key drivers of market competition in the future years. Given the strategic challenges posed by autonomous technologies in the modern workforce, are industry leaders really concerned that tomorrow’s semi- or fully-automated bots and robots will gradually displace all human experts? Will the humans absorb and make use of smart solutions or will the smart solutions devour the humans?

In an ever-evolving human workforce, now the industry operators are talking about the induction of a data ethicist (who judges what should be done with the data), algorithmic business-domain experts, and algorithmic business trailblazers. Who are these title-holders? Humans or bots? Or both? If future technology roles are blended with business roles, will automated tools be able to fulfill the requirements of such complex roles?

Cheer up, human data technologists. Review the Gartner insight titled Future-Proof Your Data and Analytics Workforce to understand what’s keeping the industry thought leaders busy these days.

The Top Data Industry Career Predictions for 2019

According to Rita Sallam, Vice President of Research at Gartner, technologies like augmented analytics, continuous intelligence, and explainable artificial intelligence will disrupt the business ecosystem in the next three to five years. She thinks that unless the “data and analytics leaders” prepare for this disruptive future and suitably adapt their business models, they will risk losing in global competition.

These technologies have the potential to automate many major data analytics tasks. Is that something to worry about for human data analysts? With the ever-growing popularity of self-service analytics and BI platforms, will the future data analysts preserve their traditional roles or will they have to re-think their job descriptions?

The McKinsey Report The Age of Analytics: Competing in a Data-Driven World indicates the growing sophistication of ML algorithms is slowly taking the mystery out of advanced business analytics. This insightful report also indicates that while petabytes of data are available now, the sensor-driven culture has not captured mainstream businesses. Although the potentials for extracting “value” from business data is enormous, the current business models are not adequately prepared to extract value from their data piles. Businesses have not made a significant leap forward since 2011.

In the future, companies will have to redesign their business models in order to make the best use of technology infrastructure and available talent to commoditize “insights or competitive intelligence” to create additional revenue channels.

A final thought: If enterprises float new business units offering competitive intelligence as a service (CIaS), then maybe the human data analysts will find new advisory roles in these units.

Machine learning has the potential to displace many job roles including that of a data analyst. Thus, these human experts will have to re-engineer their existing roles and invent more contributory functions in a machine-enabled business world. Another looming threat is deep learning, which will enable machines to think and solve problems like humans.

How Does Big Data Impact the Data-Driven Business World?

Here is a statistic to help explain the phenomenal impact of Big Data: In Europe, government bodies have the potential to save over “€100 billion ($149 billion) in operational efficiency” by using Big Data, besides using this technology to reduce fraud in tax collections.  Advanced Analytics: Exploration of Some Transformative Future Trends provides a business case for governments adopting advanced Big Data technologies.

On one end of the value chain, businesses are struggling over Crossing the Big Data / Data Science Analytics Chasm, which is becoming a moving barrier to value creation. So the net result is that uninformed organizations are continuously investing in newer technologies without “understanding how what it takes to cross the Analytics Chasm.

On the other end of the value chain, there is Big Data — conquering one data-related challenge after another. According to the results collected from Peer Research – Big Data Analytics survey, 74 percent of the survey respondents believe that Big Data Analytics is adding value to their organizations by enabling timely data-driven decision-making. With the future growth of semi- and unstructured data, Big Data analysts will be high in demand.

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