Analytics work, as you know it, is quickly changing. Soon, machines guided by humans will tremendously scale analytics capabilities to extract extraordinary value from data. According to McKinsey & Company, "analytics presents an enormous opportunity: an estimated global value of $9.5-$15.4 trillion. About 40% of this value can be realized using the most advanced AI techniques."

Cloud, automation, artificial intelligence, and emerging intelligent platforms are just beginning to reshape the analytics landscape. Cloud is already a formidable force reducing time to market, altering business models, solution architecture, project delivery, and team structures. Automation is expediting previous manual processes and disrupting traditional database, business intelligence, and analytics roles. Don’t fear these changes. Explore analytics innovation and learn new skills.

From artificial intelligence-driven data integration and analytics bots to automated machine learning, there has been a plethora of sophisticated and intelligent automation capabilities coming to life across every aspect of the analytics life-cycle. ETL, data integration, and self-service data preparation platforms are adding savvy plug-and-play data sources, automated repairs for errors, machine managed data pipeline maintenance, and data quality fixes based on knowledge learned collectively from data and human interactions. Modern business intelligence solutions have already added basic quick insights and extensions for using predictive analytics. Automated machine learning platforms with pre-built, best-practice solution blueprints and partially automated feature engineering capabilities are starting to deliver on the promise of democratizing artificial intelligence.

Analytics Skills for the Future

As artificial intelligence and automation drives more analytics and decision-making processes, different skills will become much more important for analytics professionals to master. Gartner is urging organizations worldwide to foster data literacy programs. It is becoming crucial for everyone to “speak data” as a common language.

Today only a tiny segment of the global population truly understands artificial intelligence development processes, machine learning algorithms, potential biases, ethics considerations, and the pitfalls to avoid to reliably solve problems. Even fewer people can effectively interpret, communicate, and explain machine learning insights in a manner that makes sense to decision makers. Despite exponential returns on investment that artificial intelligence can bring, early adopters are struggling to adopt and expand it because of these challenges.

Recognizing complex problems that machine learning can reliably answer, feeding in the right data, and evaluating results are all important next era analytics skills. Automated analytics relies on statistical techniques. Inaccurate, biased, or poor-quality data that doesn’t sufficiently represent business processes will deliver low quality results. Thus, you should expect to learn the art of preparing data for automated analysis and be ready for scrutiny of your findings.

When organizations consider using artificial intelligence and analytics automation, they are essentially embarking on a transformation project that requires change navigation prowess. According to a recent Harvard Business Review study, the biggest obstacles to innovation in large companies were politics, turf wars, cultural issues, and the inability to action on critical signals or developments. For those of us that have been through the first wave of change, shifting from IT-led traditional business intelligence to self-service reporting, many of the same anxieties are surfacing again with the citizen data science movement.

Champions and skeptics of analytics automation will need to have valid and invalid concerns eased. Black box magic will be rejected. Artificial intelligence and automated insights used for decision making will need to be transparent and explainable. Algorithm hacking attempts to trick the machines will escalate. Thus, automation monitoring, regulatory compliance, data security, ethics, and governance will remain hot topics.

Machines Empower Humans

Machines will not replace analytics professionals. They simply change what we do and how we do it. BI and analytics professionals helped organizations adopt self-service analytics tools and clean up accidental messes in the last wave of disruption. Again, we will be tasked to help others select the right cool new tools for the job and responsibly lead the way forward. Humans will continue doing analytical work with the aid of machines in an imperfect, constantly changing world.

I know there is fear and skepticism when it comes to automation. My advice – don’t ignore or resist it. Embrace this wonderful opportunity to get in early, dig in, and understand the analytics and machine learning automation strengths and shortcomings. Data gurus are needed now more than ever.

Regardless of what role you play today, don’t get complacent. Hard core technology and manual analytics skills that are highly valued right now might lose relevance. You may need to reevaluate your learning path for the future of work. Soft skills such as management, storytelling, critical thinking, creativity, emotional intelligence, and negotiation are expected to become more important. To learn more about global future skills demand forecasts, please read The World Economic Forum “The Future of Jobs” report and McKinsey Global Institute’s “Jobs Lost, Jobs Gained: Workforce Transitions in a Time of Automation“.