Rise of the citizen data scientist

Innovative technology is transforming the way we consume data and is creating a new type of user within organizations: the citizen data scientist. This capability emerges from a new breed of artificial intelligence that puts powerful predictive analytics in the hands of front-line users, helping them address day-to-day needs and delivering greater insights.

It is no longer necessary to turn to software developers to create use-case-specific applications. Now, with functionality assured through solid back-end code and application programming interfaces, users can delve into the data they care about with an unprecedented array of automated analytic tools. The toolset -- easy to use on the front end and  powered by sophisticated technology on the back end -- makes it simple for users to gather, deploy, integrate and consume information at a level scarcely imagined a decade ago. For government agencies struggling to contain costs and hire skilled data technicians, this software innovation is game-changing.

First, some background

It is been nearly a decade since analysts and technology providers began pointing to a convergence between IT departments and their internal clients. By mid-decade, the emergence of low-code platforms for citizen developers signaled fundamental paradigm shift. This supported and empowered non-IT staff who lacked traditional coding skills but still had the ability and motivation to solve day-to-day business challenges by developing their own apps.

Fast forward to today, and that trend has escalated and moved beyond citizen developers to create a new role for the citizen data scientist. The fundamental advantages that distinguish citizen data scientists from citizen developers are the massive amount of data at their disposal and the sophisticated new tools available to help them deploy and draw insights from it.

An added dimension for AI

AI can be used for more than automating some simple processes. The true potential of AI technology comes from applying machine learning and predictive analytics to a variety of practical and personalized 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. These predictive insights can help 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 software, user tools must be intuitive, requiring common sense, not IT expertise and hands-on users must be engaged in defining the objectives, assigning priorities and setting parameters and conditions. This opens the door for citizen data scientists who draw on their own application knowledge and use tailored IT tools to create AI and machine learning models at scale.

It is a new development approach that is just beginning to gain traction. But it is emerging as an exciting new way to bring a wider development force into synergy with AI.

Connecting AI to non-specialists

For citizen data scientists to make good use of AI, the technology has to offer a high degree of intuitive, automated functionality, with a user experience that would be familiar to a manager or executive with no experience in a coding environment.

That means algorithmic functions must work seamlessly, in a way that requires only occasional input or support from the in-house IT team. The system should also be completely invisible to a user who can define the desired outcome, but not the technical path to achieving it.

The most accessible, user-friendly AI systems have automated the process of hyperparameter optimization or tuning, so that the business user never has to think about the 20 or 30 different policies or factors that might go into the algorithm. At the level of the user interface, the citizen data scientist can just open the system, drag and drop the data and count on receiving an optimized result.

Behind the curtain, AI is autonomously running multiple options and permutations to come up with the best answer to the user’s question, drawing on underlying data patterns to spot issues, opportunities or unknowns that simply would never be visible through non-automated analytics. By freeing employees up to focus on the tasks they are good at and that deliver the greatest value, the technology provides the heightened functionality and performance agencies need to stay efficient, innovative and on budget.

Final advice

Agencies generate massive amounts of data. Rather than be overwhelmed by it, they should invest in AI-driven analytics that help them formulate meaningful, practical applications for generating data insights. Automated solutions can enable users -- with no data science experience ---  to slice, dice and combine agency data with other sources of internal and external knowledge, organizing it to meet the needs of different levels and functions across the organization.

A well-thought-out AI strategy can enhance and accelerate the flow of mission-critical information across the enterprise. With the right solution in place, it is possible to turn everyday users into citizen data scientists, enhancing their ability to work smarter and more efficiently with data.