Several industries, most notably manufacturing, have seen robotics and automation disrupt their production operations, leading to the loss of even highly skilled individuals. Likewise, 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? Analytical jobs such as those of a data analyst requires critical thinking, and rethinking decisions, based on dynamically changing situations. 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? According to many prominent experts observing the AI industry, there’s no need to worry. While AI will indeed bring significant changes, AI advances will continue to require human attention to ultimately make efficient and productive decisions.
AI Can Deal With the Data Deluge
While most of the existing BI solutions can process and store a huge amount of data with many dimensions, they don’t offer an easy way to get insights from the data. To find ways for the business to improve its key KPIs, data analysts simply don’t have the capacity to keep up with the increasing demand to crunch all the data. In fact, BI solutions have largely left the “I” – the intelligence – completely in the hands and minds of the data analysts. The human brain is limited in the number of data points it can process and correlate.
According to Gartner, Inc., “More than 40 percent of data science tasks will be automated by 2020, resulting in increased productivity and broader usage of data and analytics by citizen data scientists.”
AI stands to play a greater role in BI, where intelligent systems pour over more data than any human could reasonably examine. “With millions of metrics coming in daily, companies don’t have the ability to efficiently track vast amounts of customer data without risking the potential of missing essential insights, which leads to damage monetarily and reputationally,” said David Drai, CEO and Co-founder of Anodot. The more data analysts can identify good and bad deviations from the norm, the more quickly they can react to changes in the business and take necessary action.
New Tools, Same Disruptions
AI analysis is not unlike previous technological disruptions; the printing press made calligraphers obsolete, but introduced the new role of the professional printer. While AI analysis stands to disrupt BI, it opens the door for new jobs.
David Crawford writes in VentureBeat, “The work of an analyst, however, does not just involve conducting data analysis within closed environments. The analysis must be applied to the outside world where there is much more context influencing the interpretation. For example, while AI connected to sensors might be able to analyze the soil on a plot of land and optimize yield more efficiently than a human, it doesn’t know what impact the soil conditions have on the flavor of the resulting crop.”
Going forwards, AI will help provide focused insights for data analysts, by reading more deeply into data and identifying patterns. Carrying out exploratory tasks, such as recognizing specific deficiencies or untapped opportunities among the data, will help human professionals to interpret these discoveries to make more informed decisions.
The Value of Data Analytics is Growing
Big Data thought leader, Bernard Marr adds, “As the value of data analytics becomes apparent in all fields of activity, a growing number of people will want to be able to extract insights from their data. They might not want to take three or four years out to learn advanced computer science and statistics, and with the advances in cognitive computing that won’t be necessary. All that is required might be a brief introduction to NLP technologies.”
Joel Shapiro, executive director of the data analytics program at Northwestern University’s Kellogg School of Management says, “Analytics still rests fundamentally on good critical thinking skills —how to ask good questions and rigorously assess evidence that can lead to action.”
Artificial intelligence addresses today’s data deluge better than humans, since human analyst can’t sift through all of this data unaided. You can’t have a person sitting there or even whole teams watching dashboards to protect a brand or expect them to zero in on business incidents as they happen. You need AI tools.
AI Enhances Data Analyst Job Security
This doesn’t mean AI is coming to eliminate jobs for those involved in BI. While AI can do the work that no one has the time for, companies will come to see much stronger benefits in BI and be more inclined to further invest time and effort — creating more jobs in the field as a result. AI is good for job security.
AI and data analytics were developed by humans, for our own benefit. David Crawford adds that, “Understanding what it means to be human and caring about the human experience are intrinsically related to the analysis process.” Human data analysts aren’t going away as long as other humans remain their ultimate consumers. Data analysts will become ‘managers’ of teams of AI ‘employees’, leveraging the AI’s algorithms to comb through data and to even get answer to questions that weren’t asked.
As these systems collect and interpret greater volumes of data than we ever could, they advance, learning from past analyses to see what’s worked well. As David Drai observed in VentureBeat, “All advances in A.I. are built on the premise that if we can teach machines to learn from their “experiences,” then they will be able to more effectively sort through new information and help us flag the pieces that we need to know about immediately. Obvious steps forward, like the capacity to more effectively recognize seasonality or expect “unexpecteds,” will help lower the number of false positives and enable a far greater reliance on BI.”
These systems still require a human to design and maintain them, to ask them the most important questions for the business, and to communicate their results with colleagues in other specialties.
As AI solutions are able to dig deeper and more quickly link a cause with an effect, they can drastically reduce the time it takes to prevent or handle a crisis. This empowers the business, uncovering unforeseen opportunities while creating new means of driving revenue and enabling far more insightful decisions for data analysts.
With highly scalable machine learning-based algorithms, we now have software that can learn the normal pattern of any number of data points and correlate different signals to accurately identify anomalies that require action or investigation – by the data analysts.