New Ways for Simplified Data Science

The last few years have cemented the importance of data in any business. Owing to the phenomenal contribution data makes to any business, it is hardly a surprise that data scientists are the new superheroes. Glassdoor ranks the job role of the Data Scientist as the ‘Hottest job of the 21^st Century’ three years running.  The Deloitte Access Economics report highlights that 76% of companies plan to increase their spending on analytics capabilities over 2019 and 2020. They also forecast that by 2022, the data science professionals with a post-graduate degree will be earning an average $130,176 p.a.

The role of the data scientist has shot to prominence owing to the growing importance of data. Data is now becoming part of organizational DNA becoming not only a part of the decision-making process but also of product development, identifying business value and new business propositions, and evaluating risks amongst other things. Data science prepares organizations to find more value in technology. And it is the data scientists who so far have been the enablers of the same.

However, as we delve deeper into the tech and the data economy, one thing becomes clear – data science is not a ‘back office’ thing. It cannot exist in the form of horizontal diversity with the organization. As data becomes critical for every organizational function, the traditional role of the data scientist has to evolve. And while organizations will have to have their set of data scientists, they will also need their band of Citizen Data Scientists – the ones who will be using data to empower business decisions and contribute to the bottom line.

Data Science – then and now

Data Science is no longer a niche role. It does not belong to the hallowed portals of a select few organizations alone. From healthcare to eCommerce, every business and every industry has the need for data science and consequently data scientists. These data scientists have advanced training in math, statistics, and computer science. They have to have in-depth technical knowledge of languages such as R and Python to create robust data models. And what good is this knowledge if they do not have great domain expertise? It is the domain expertise that helps these data scientists manipulate the vast sea of data at hand to glean intelligent business intelligence and insights.

The role of the data scientist

The data scientist models and rearranges the data with a visual front end. This is traditionally operated in the scripting interface. The current technology being favored here is R3. Along with this, data scientists also have to manipulate different tools built for specific purposes to get the desired answers from the data at hand. They have to work with Knowledge Discovery Datasets, conduct data exploration as well as data visualization. R and Python have been the favorable programming languages to fulfill the programming needs amongst the data scientists. This has been mostly because these languages are user-friendly and have a big support network amongst other benefits. Clearly, to develop robust data models and fulfill data exploration, visualization, analytics demands etc., the data scientist has to have deep programming and scripting knowledge to use the available tools. This also establishes how niche the role of the data scientist is.

The growing need for ‘citizen data scientists’

Given the growing role of data in every organizational aspect, can the role of the data scientist remain as niche as it is presently? In my opinion, organizations now need the non-data scientists, basically, the business users to assume the capabilities of the data scientist. They are the actual users. They are the domain experts. They have the right questions. They know the business problems they need to solve.

What they need is the capability to exploit the data at hand to drive their decision making. If empowered, any business user can become a citizen data scientist and apply the right data models to the right data to predict business outcomes. They need the flexibility and the bandwidth to connect with the database and create compelling visualizations, reports, and dashboards. They need the capability to play with the data they have and explore the myriad possibilities associated with that data to answer their own questions. No dependencies involved.

Data science has to come to the ground level to create a data-driven organization and develop a data culture. All that organizations need to enable the same is a platform that allows Open Source, algorithms, computation, and business users work harmoniously. The trick lies in ensuring that organizations don’t have to change their processes. The integrated platform should come with high interoperability – one which has a host of pre-built functions, employs popular algorithms, provides toolsets to analyze structured and unstructured data and social data, provides static models for changing data sources, and can handle changing data sources and volume.

Rubics.io is such a platform that empowers everyone to become a data scientist.

So yes, with such a tool, your business head could be your new data scientist. Or your manufacturing head or your programmer…anyone in the organization can become a citizen data scientist. And this brings data science to the ground level creating an organization that is ‘data-driven’ in the truest and the broadest sense of the term.