How AI and Machine Learning are Bringing New Opportunities to Data and Analytics

How Ai And Machine Learning Are Bringing New Opportunities To Data And Analytics 800x800

​Today, advancements in artificial intelligence (AI) are giving companies opportunities to expand, innovate, and improve productivity and efficiency in ways that never existed in the past—all while creating exciting growth for jobs in the data analytics sector in the process.

Although AI itself isn’t new, the ability for companies to apply AI into their workplace environments is a more recent concept, due to the power of computing and cloud-based service options. Its application is also causing a great deal of uncertainty about how it will change the way we work. By all indications, however, AI has huge potential to enhance human life, bring more jobs to the global labor market, start a new wave of innovation, and create new opportunities for the data and analytics sector.

The Increasing Role of AI in the Workplace

While it’s true that some jobs will likely be replaced as AI deepens its hold in the workplace, estimates point to a net positive in new-job creation. Social science and business journalist Marina Krakovsky states that as a whole, AI will most likely take the route of “complementing people, rather than replicating people.” In essence, the goal of AI is to make workers’ lives better, rather than become the workers. Ideally, AI will take over many of the mundane and repetitive processes taking up workers’ time to free them up to focus on higher-value, relationship-building tasks.

In many areas of the workplace, such as highly cognitive jobs that require a deep level of expertise and interpretation, machines have yet to match human performance, which helps jobs in the data and analytics sphere stand apart. Two roles in this area, data analysts and data scientists, are seeing explosive growth, but each has a distinct skill set to offer businesses.

Two Sides of the Coin: Data Analysts and Data Scientists

We as a society have more data than ever before, and there’s a huge demand for experts to organize, understand, and derive meaning from that data. The World Economic Forum’s“Future of Jobs 2018” report found that data analysts, data scientists, and software and applications developers (roles significantly based on and enhanced by the use of technology) are among the established jobs expected to experience increasing demand in the period up to 2022. Both data analysts and data scientists work to make sense of data to solve business problems and improve products and processes, though in different ways.

The Data Analyst

A data analyst answers specific business questions (e.g., “What are the demographics of our most engaged customers?”) and needs to know how to organize and sort data to help a business make

more-informed decisions. The role of a data analyst requires critical thinking, interpretation of data, and recognition of patterns and trends, based on dynamically changing situations.

The data analyst role requires a combination of analytical and leadership skills, including statistics, communications, and business skills, as data analysts need to help stakeholders make data-driven business decisions while also effectively communicating the value of this information.Data analyst positions are in high demand: IBM predicts that by 2020, there will be more than 2.7 million job openings for those with data skills, with nearly 40 percent of those jobs being in advanced data analytics and requiring a master’s degree or higher. Entry-level salaries can start at $60,000, with senior positions topping $135,000.

The Data Scientist

Data scientists, on the other hand, focus more on the predictive analytics subset of data. They formulate and test hypotheses, and form organizational and business insights from their findings. They often work with unstructured data from disconnected sources, versus data analysts’ primary use of structured data from a single source. Necessary skills for this role are math, programming, and communications.

Named “sexiest role of the 21st century” by Harvard Business Review in 2012, the data scientist role continues to be a much-buzzed-about role, and its increased demand is due in part to AI, machine learning, non-ML predictive analytics, and other “big data” approaches used to gain traction in the corporate space. BLS predicts 16 percent growth for the role from 2018–2028, much faster than the average growth rate. The median salary for a data scientist is $118,370 a year,according to BLS data.

Today’s data scientist looks much different than the “sexy” data scientist of 2012, although it’s arguably a more interesting job in present day. Data scientists, more and more, are expected to bring “domain expertise, communication skills, and an innovative mindset to the AI strategy table,” and a deep understanding of AI is moving from a nice-to-have to a necessary requirement. Companies are rapidly adopting AI solutions and looking to data scientists to steer their AI strategies.

As analytics techniques grow more advanced in the face of AI, many data science tasks will become automated, including data integration and model building. With machines doing much of this data science analysis, the need for humans who are able to understand, interpret, and implement solutions based on the analysis is essential.

On Oracle’s blog, Jacqueline Berkman discusses how data scientists bring three distinct human elements to their role that AI cannot: curiosity, discernment, and creativity. She says that there’s no reason for data scientists to be afraid of AI, but, rather, to view the advancement of technology as an opportunity to expand what’s possible within the scope of their roles. This advice can also apply to data analysts and the workplace as a whole.

Rising to Meet the Challenges and Opportunities of AI

Policy makers, business leaders, and workers all have a responsibility and a role to play in the future workplace, where AI and humans will be increasingly intertwined. The question for all is how to use AI to their benefit, to strengthen job stability, and adopt a mindset of innovation. As Rachel Russell, executive director of corporate strategy for Allegis Group, says in a CMSWire article, “In any case, as AI takes on more of the work we do, continuous learning and a willingness to develop new skills will likely be a requirement for nearly every worker to maintain their job. This fact has always been true for workers in many fields, but now it’s more important than ever.”

AI may actually help some workers’ jobs become more marketable and competitive, as their lesser-value tasks get moved to AI and higher-value tasks start to make up a bigger part of their role.

The World Economic Forum’s “The Future of Jobs 2018” report predicts that by 2022, no less than 54 percent of all employees will require significant re-skilling and up-skilling. Imperative to this vision, the report adds, is “an economic and societal move by governments, businesses and individuals towards agile lifelong learning, as well as inclusive strategies and programs for skills retraining and upgrading across the entire occupational spectrum.” Successful businesses will spend less time on activities that machines are learning to do, and more time helping their employees develop highly cognitive skills that will increase in value over time.

That seems key to maximizing success in an increasingly AI world: gaining a new perspective on what’s possible when it comes to our jobs, careers, and businesses, and doing the higher-value work that AI cannot accomplish. If AI can take on some of the tasks that have prevented us from doing deeper, cognitively demanding work, it will free us up to take on new challenges, push ourselves further, and create new spaces for ourselves in a disruptive workspace.

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