Can business users become data scientists overnight?

Profile picture for user Rick Rider By Rick Rider June 25, 2019
Summary:
New software tools empower end users to become citizen data scientists and create their own use cases of AI-driven analytics, writes Infor's Rick Rider

Data scientist in glasses and pink shirt through touchscreen display, with wood background © WAYHOME studio - shutterstock

What if a few clicks on the keyboard could transform an ordinary, mild-mannered business software user into a future-seeing data warrior? This scenario may sound like a far-fetched, super-hero story. But, this remarkable capability is within grasp. A new breed of artificial intelligence (AI) solutions are creating the role of citizen data scientist, a professional without code development skills, but the ability to apply automated insights to business questions.

Demand drives automation

Automation is a big driver of this trend. In 2017, Gartner analysts projected that more than 40% of data science tasks will be automated by 2020. Automating the work of data scientists helps make them more productive and more effective. Just as well. In a recent article in Harvard Business Review on democratizing data science, business consultancy Deloitte writes:

By some estimates, data scientists spend around 80% of their time on repetitive and tedious tasks that can be fully or partially automated. These tasks might include data preparation, feature engineering and selection, and algorithm selection and evaluation. 

Various tools and techniques have been introduced by software providers. Some oversimplify and try to provide out-of-the-box solutions that don't allow for the solution to ‘learn' protocols. Others are so complex that only a highly skilled data scientist could use the tool, negating one of the main objectives.

Fortunately, highly effective solutions are available. The user interface is the key to an easy-to-use tool that will empower personnel, from data novices to data experts who have a wide range of skill sets, to achieve productivity gains. With these solutions, complex objectives are broken down into natural language questions and drag-and-drop screens, making the front-end of the solution seem straightforward. Meanwhile, on the back-end, sophisticated functionality is at work interpreting the user's needs into a series of algorithms.

With powerful predictive analytics and machine learning (ML) built in, these solutions automate the process and speed execution, providing data insights in consumable charts and graphics. Multiple business factors, from machine output to general ledger data, can be brought into sharp focus.

The mass amounts of data now available to business managers and executives create a doubled-edged sword. The insights are valuable, but the overload can create chaos and confusion. As the digital transformation intensifies, so, too, has the demand for skilled IT professionals. Several industries have faced debilitating shortages of skilled applicants. Data scientist is one of the specialized roles in high demand. Deloitte's HBR article reports that, based on current demand and supply dynamics, the US alone is projected to face a shortfall of 250,000 data scientists by 2024.

How did we get here?

Over the past decade, analysts and technology providers had been predicting an era of convergence between corporate IT departments and business users. By mid-decade, the technology community began to refer to low-code platforms for citizen developers as a fundamental paradigm shift. Non-IT staff members who lacked traditional coding skills still had the ability and motivation to solve day-to-day business challenges by creating their own specialized applications. Demand from users for more tools, more functionality and more templates for applying AI technology has led providers to continue expanding user-defined forms and reporting.

Natural language generation (NLG), a subset of artificial intelligence, is one of the technologies driving this new era. NLG transforms structured data into clear, natural language. Forbes contributor Marc Zionts recently explained the role of NLG:

NLG supports citizen data scientists by allowing any employee, regardless of role, position or level of data expertise to interpret the data being presented, what it means and what action to take in a way that translates visuals using the most natural form of communication - the written word. 

Also, data visualization tools are another innovation empowering business users to consume data insights easily. These powerful BI solutions create dashboards and data gauges that can be personalized for the user's needs, whether tracking personal Key Performance Indicators (KPIs) or departmental responsibilities, like safety stock levels, capacity planning, or outstanding receivables. When data insights can be displayed in at-a-glance simplicity, users are more apt to find value, trust the AI-driven conclusions, and become conditioned to look closely at the influencing factors to improve outcomes.

Achieving maximum potential

The true potential of AI technology comes from applying machine leaning (ML) and predictive analytics to a variety of practical use cases. AI has the potential to provide advice, discover performance patterns, analyze multiple influencing factors, and draw complex conclusions about a specific question - including questions that require a window into the future.

The maximum potential is reached when AI can emulate and enhance human performance, offering advice that is reliable and intelligent. This predictive insight helps organizations anticipate, understand and prepare for future trends and outcomes. The solution reaches this level over time, analyzing patterns and tracking a variety of scenarios, learning which responses humans judge as appropriate and which are rejected.

For such practical applications, the hands-on users need to be engaged in defining the objectives, assigning priorities, and setting parameters and conditions. That's why citizen data scientists play such a critical role. They can draw on their own application knowledge and understanding of the business goals and context. A third-party consultant may struggle to grasp the nuances of the use-case.

Final advice

Organizations have amassed huge amounts of data. Rather than be overwhelmed by it, they should invest in AI-driven analytics to formulate meaningful, practical applications. A well-thought-out AI strategy can enhance and accelerate the flow of mission-critical information across the enterprise. With the right software solution in place, everyday business users can become citizen data scientists, working smarter and empowered by data.