Relooking at the Evolving Role of Data Scientists in Enterprises

A decade back, "Data scientist" was termed the "sexiest job of the 21st century." As companies began to generate large data volumes, a data scientist was  defined  as "a high-ranking professional with the training and curiosity to make discoveries in the world of big data." 

It's no surprise that data expertise continues to dominate job positions in the world of technology to this date. But has the role of the data scientist remained the same, or has it evolved in recent years? Are technical skills all that a data scientist needs to acquire, or do they also need a business viewpoint? 

In the following sections, let's discuss how the role of data scientists has evolved in business enterprises. 

How Has the Role of the Data Scientist Evolved? 

In the traditional mode, a data scientist is expected to create the data models and architectures needed for the data science initiative. Effective data models could derive valuable insights from massive data generated by the business. 

The role of the data scientist has now evolved towards being industry-specific and business-linked. Business enterprises now want data to be accessible and usable by business users. Besides their technical strength, data scientists must also think from a business viewpoint. For example, for a mobile-driven eCommerce company, data scientists may have to demonstrate to their stakeholders the financial benefit of improving a product recommendation system for individual shoppers. 

Similarly, a business-oriented data scientist can proactively suggest "what more can be done" to transform the business. Additionally, data scientists can collaborate with business users to execute  data science  projects. Through close collaboration, business users can know where to begin to gain better data insights, while data scientists can gain a profound understanding of the business problem. 

Next, let's discuss the benefits of a business-oriented data scientist for any enterprise. 

5 Benefits of a Business-Oriented Data Scientist 

Data scientists skilled in business practices can offer multiple organizational benefits like improved decision-making capabilities and productivity. 

Here are five key benefits offered by a business-oriented data scientist: 

1. Quantifying the Impact of a Product Feature 

Organizations spend a considerable amount of time and effort in developing a new feature or new product line. Data scientists can quantify the financial and business impact of a new product feature. They can provide accurate "ballpark" numbers of what the company can gain by implementing the recommended change. 

For instance, a data scientist can recommend a new system that increases the average time a consumer interacts with a business by 1%. With this quantified number, business stakeholders can determine if it is cost- and time-worthy to implement this system. 

2. Improving Business Decisions 

Effectively, data scientists extract and "make business sense" of data using mathematical and analytical skills. Using their knowledge of the business domain, business-oriented data scientists can extract deeper insights from complex scenarios to benefit enterprises.  

For example, data scientists in a competitive retail industry can present accurate sales forecasting solutions to decision-makers — for instance, year-on-year growth or sales hikes during festive seasons. Using objective evidence, data scientists can assist business stakeholders in making difficult decisions about the future course of action. 

3. Identifying Business Opportunities 

Using data science and analytical tools, data scientists can help organizations identify new business and revenue opportunities. Based on existing data patterns and industry trends, they can forecast market conditions and how they will impact the organization. This can allow enterprises to change their business model (if required) and prepare for future adversities. 

To achieve this, data scientists must possess a deep knowledge of business practices and how they work. For example, they can analyze the market price among competitive products and identify opportunities where they can gain a competitive advantage. 

4. Creating Better Products 

With their data-driven approach, data scientists provide ample evidence for organizations to: 

• Identify their target audience 

• Understand what their consumers want 

• Design products that can meet their requirements 

With data and consumer analytics, product managers can now work with data scientists to undertake continuous product improvements. Using data metrics and insights from data scientists, product teams can make informed decisions about adding product functionalities or capabilities. Without  analytics, product development is restricted to developing a product "idea" that may (or may not) meet consumer expectations.  

5. Reducing Business Risks 

Using data-driven business decisions, data scientists can help organizations identify potential risks and reduce business failure. In recent years, data science and analytics have played a crucial role in risk modeling, particularly in the banking and financial industry. For example, data-backed machine learning algorithms can detect fraud or suspicious user behavior. 

Studies have previously shown how only  36% of organizations  have devised a formal risk management plan, while 69% have not been confident of their risk management policies. With their technical and business expertise, data scientists can extract data-driven insights from historical data and generate predictive analytics to minimize risks. 

Conclusion 

The growing volume and complexity of business data have elevated the criticality of data scientists within any organization. Starting from technical experts, data scientists are now playing a more "business user-like" role across organizations. 

Novigo Solutions  is an accomplished IT consulting partner offering high-quality services in  data science and analytics. Our service offerings include providing our clients with capabilities like extracting valuable insights, tracking key performance indicators (KPIs), and leveraging predictive analytics. Here is our  suggestion  on how to make data initiatives more accessible to business users. 

Are you looking for an experienced partner for your next data science project? Get in touch  with us today!