How does Augmented Analytics aids in promoting new roles?

From our personal data to the unfathomable depths of data sources, we are surrounded by a humongous amount of data. Every organization, starting from small to big organization, make use of the data which helps in achieving business outcomes. Some organizations use forecasting techniques based on traditional mathematical and statistical models, while others use predictive analytics that uses AI/ML techniques and other advanced analytics to predict/forecast the future. One such example is the prediction and prevention of driver churn in the company. So there is a need for a specialized predictive analytical solution to help companies run their business better. But these solutions require expert data scientists, who are expensive to hire and are scarce in supply. Thus, this has led to the emergence of citizen data scientists.

The emergence of Citizen Data Scientists

The emergence of citizen data scientists is happening for two reasons. First, they are proving to be a strong-complement-and cost-effective to expert data scientists, who are typically scarce in supply and expensive to hire. Second, data science is getting simple with Augmented Analytics. According to Gartner, “the use of new analytics and business intelligence tools are extending further into the enterprise.” Nowadays, many organizations have started using Augmented Analytics. It democratizes insights from analytics, including AI, to all business roles. It makes data science and ML/AI model building accessible to new citizen data science roles (business analysts, developers, and others). It will make existing expert data scientists more productive, freeing them for high-value tasks.

By 2020, the number of citizen data scientists will grow five times faster than the number of expert data scientists. Gartner predicts that, by 2020, more than 40% of data science tasks will be automated, resulting in increased productivity. Gartner also predicts that by 2024, a scarcity of data scientists will no longer hinder the adoption of data science and machine learning in an organization.

Who are Citizen Data Scientists??

Gartner defines the citizen data scientists as a person with emerging capabilities, who creates or generates models that use advanced diagnostic analytics or predictive and prescriptive capabilities, but whose primary job function is outside the field of statistics and analytics. They are “power users” who can perform both simple and moderately sophisticated analytical tasks that would previously have required more expertise. Citizen data scientists provide a complementary role to expert data scientists.

Typically, citizen data scientists do not have coding skills but need to develop strong domain expertise to understand the data. They can also build models using drag-and-drop tools, run pre-built data pipelines and models. They do not replace expert data scientists, as they do not have specific advanced data science expertise to do so. But they certainly bring their own business expertise and unique skills.

The citizen data scientist is a role that has evolved as an “extension” from the other roles within the organization. Their role will vary based on their skills, domain, and interest in data science and machine learning. Roles that filter into the citizen scientist category include:

  • Business Analysts
  • BI Analysts/Developers
  • Data Analysts
  • Data Engineers
  • Application Engineers
  • Business line manager

Developer data scientists, a type of citizen data scientist, is a significant development driven by augmented analytics. These are application developers armed with augmented data science tools, who can build ML and AI models to embed in their application. This will relieve the intense demand for expert data science skills and offer opportunities to upskill existing application developers.

Empowering the Citizen Data Scientist

As there are not enough qualified data scientists to meet the demand for data science and machine learning skills, citizen data scientists emerged to provide their unique capabilities to extract predictive and prescriptive insights from the data. This growth enabled by augmented analytics, will complement, and extend existing enterprise applications. It is also available to a broad range of users such as business analysts, decision-makers, etc. across the organization. This will drive new sources of business value. Citizen Data Scientists must collaborate with a specialist data scientist to gain necessary skillsets. Also, the organization must implement an upskilling program for developing citizen data scientists from existing roles. In addition to that, it is also important to ensure a data-driven culture across the company to increase their acceptance and bring about change amongst employees.

Citizen Data Scientist will change the workflows

As mentioned before, citizen data scientists are empowered to do their own data analysis, build models using drag-and-drop, and without any prior coding experience. This will allow them to make decisions based on what they find. Citizen data science eases the burden on existing expert data scientists and analysts, making them more productive and collaborative and freeing them for high-value tasks such as model building, validation, testing, delivery, and operationalization. Business roles can get quick returns on their data-based questions which increases efficiency.

The rise of citizen data scientists is enabled by augmented analytics. It is designed for business users instead of a technical audience. Advancements, like natural language processing (NLP), is one of the most important factors for a non-technical user. Instead of writing SQL queries to extract the data, NLP uses a natural language query (NLQ) to ask a query in plain text and generates the results in a natural language.

Embracing augmented analytics in an organization as a part of digital transformation strategy helps in building trust and delivering advanced insights to a broad range of users including citizen data scientists and, ultimately, operational workers without expanding the use of data scientists. And by incorporating the necessary tools & solutions and extending resources & efforts, enterprises can empower citizen data scientists!

Are you leveraging Citizen Data Scientists within your organization? If yes, who are they according to you, what are their titles, and what do they do? I’d like to hear your stories in the comment section.

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