Will data analytics jobs get automated?

Will analytics get automated?
General

Will data analytics jobs get automated?

In the various industries, there’s concern over how artificial intelligence (AI) could affect millions of jobs.

As environmental parameters change, will machines come to mimic the “rational thinking” of a human brain and alter their reactions? Can artificial intelligence and machine learning (AI/ML) systems branch off to different thinking modes, as humans do, based on changing parameters?

Should data analysts and other “knowledge sector” employees feel threatened by AI? While AI will indeed bring significant changes, AI advances will continue to require human attention to ultimately make efficient and productive decisions.

The Data Flood Can Be Handled By AI

While most existing BI tools can process and store large amounts of data with several dimensions, they do not provide a simple way to extract insights from the data. Data analysts simply do not have the capacity to keep up with the increased demand to crunch all of the data in order to uncover solutions for the organization to improve its key KPIs. In truth, business intelligence (BI) solutions have mainly left the “I” – intelligence – in the hands and thoughts of data analysts. The quantity of data points that the human brain can process and correlate is restricted.

“By 2023, more than 40% of data science tasks will be automated,” according to Gartner, Inc., “leading in higher productivity and expanded use of data and analytics by citizen data scientists.”

In BI, where intelligent computers pore over more data than any human could fairly study, AI is likely to play a larger role. “With millions of metrics pouring in every day, firms don’t have the capacity to efficiently track enormous volumes of consumer data without risking missing crucial insights, resulting in financial and reputational damage,” stated David Drai, CEO, and Co-founder of Anodot. The better data analysts are at identifying good and poor deviations from the norm, the faster they can react to business changes and take appropriate action.

Same Disruptions, New Tools

AI analysis is similar to earlier technical disruptions: the printing press rendered calligraphers obsolete while introducing the professional printer as a new occupation. While AI analysis has the potential to disrupt business intelligence, it also has the potential to create new professions.

“The task of an analyst, however, does not merely require conducting data analysis within confined environments,” writes David Crawford in VentureBeat. The analysis must be applied to the outside world, where the interpretation is influenced by a lot more context. While AI attached to sensors may be able to assess the soil on a plot of land and optimize yield more efficiently than a person, it has no idea how the soil conditions affect the flavor of the crop.”

By looking more deeply into data and recognizing patterns, AI will help data analysts give more targeted insights in the future. Exploratory tasks, such as identifying specific flaws or untapped opportunities in data, will aid human professionals in interpreting these findings and making better-educated decisions.

The Importance of Data Analytics is Increasing

“As the value of data analytics becomes obvious in all domains of activity, a rising number of people will want to be able to derive insights from their data,” says Bernard Marr, a Big Data thought leader. They may not want to devote three or four years to learning complex computer science and statistics, and with developments in cognitive computing, they will not be required to do so. “A basic introduction to NLP technologies may be all that is required.”

“Analytics still rests essentially on solid critical thinking skills —how to ask good questions and carefully examine evidence that might lead to action,” says Joel Shapiro, executive director of Northwestern University’s Kellogg School of Management’s data analytics program.

Because human analysts are unable to sort through all of this data without assistance, artificial intelligence addresses today’s data flood better than humans. To defend a brand, you can’t have a person or even a team sitting there watching dashboards and expecting them to spot business disasters as they happen. You’ll require AI tools.

AI Enhances Data Analyst Job Security

This isn’t to say that AI will remove jobs in the business intelligence field. While AI can do the work that no one has time for, corporations will begin to perceive much stronger benefits in BI and will be more likely to invest more time and effort in the field, resulting in the creation of more jobs in the sector. Job security is aided by AI.

Humans invented AI and data analytics for our own gain. “Understanding what it means to be human and caring about the human experience are integrally tied to the analysis process,” says David Crawford. Human data analysts will continue to exist as long as other humans are the ultimate customers of their work. Data analysts will become managers of AI ‘workers,’ utilizing the AI’s algorithms to dig through data and even find answers to queries that were never asked.

These systems progress as they collect and understand larger amounts of data than we ever could, learning from previous analysis to see what works best. “All improvements in A.I. are founded on the concept that if we can educate machines to learn from their “experiences,” then they would be able to more efficiently sort through incoming information and assist us to identify the portions that we need to know about quickly,” David Drai wrote in VentureBeat. Simple steps forward, such as the ability to recognize seasonality more effectively or anticipate “unexpected,” would help reduce the number of false positives and enable a far greater reliance on BI.”

These systems nevertheless require a human to build and manage them, as well as to ask them the most crucial business questions and transmit their findings to colleagues in other fields.

Because AI solutions can delve deeper and link a cause to an effect more rapidly, they can substantially cut the time it takes to prevent or respond to a disaster. This empowers the organization by revealing previously unknown opportunities, generating new revenue streams, and allowing data analysts to make considerably more informed decisions.

We now have software that can learn the typical pattern of any number of data points and correlate different signals to properly identify anomalies that demand action or investigation – by data analysts – thanks to extremely scalable machine learning-based algorithms.

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