Data is the asset everyone is frantically looking to leverage. Worldwide, businesses have access to data that is continuously piling up in their storehouses, waiting to be drawn into useful insights. Through their experience, businesses are coming to realize that gathering data is no more a challenge but doing something with it is.

Leveraging the right data at the right place and utilizing it for business growth is the key to success. Therefore, companies are looking for data science experts to help them make sense from their data. As the traditional decision-making process is evolving, the fields of business analysis and data science are expanding as frontiers in harnessing data expansion.

For small businesses, these roles might seem overlapping. However, for companies who have more comprehensive data storage, processing, and analytics avenues, it makes sense to differentiate the responsibilities of a business analyst from those of a data scientist.

Opportunities and the Skill Division Between the Two

The role of the business analyst is to liaison between IT and business stakeholders. They need to be involved in finding answers to the demanding questions to optimize value for money.

Whereas, on a broad level, the responsibilities of a data scientist span finding out new insights, solving complex data problems, and revealing data that can help maximize the potential of a business. Arriving at conclusions through data-driven strategies and eliminating guesswork is the job of a data scientist.

Data science holds a lot of promise for businesses to transform holistically in the digital age. But according to a study by McKinsey, the results of many data science initiatives have been disappointing. In spite of investments in analytical tools, available capabilities, and bringing in data science skills, organizations have only been able to derive a fraction of the true potential.

The biggest hurdle in extracting value has been attracting top talent to work on organizational goals. Most companies need data scientists who are well-versed with domain and business knowledge alongside data science expertise.

While a data scientist needs in-depth know-how of all the latest tools, statistics, programming, and coding, they could do with a surface-level understanding of the latest algorithms in artificial intelligence and machine learning.

On the other hand, a business analyst needs to be comfortable in assessing organization-level changes, defining new requirements, and developing business cases. Domain and business understanding are critical for succeeding in the business analyst role.

This is precisely why it is desirable for business analysts to cross over into data science – a field with more promising career prospects that could use a business analyst’s experience.

Crossing the Divide from Business Analyst to Data Scientist

In switching over to a data scientist role, a business analyst can continue to leverage skills such as organizational and business knowledge, an analytical perspective to problem-solving, and secure communication. But now they need to acquire technical competencies in five core areas:

• Business
• Statistics
• Machine Learning
• Programming
• Mathematics

Part of the job of a data scientist is to follow a pattern. It includes –

• Understanding business needs by decomposing a problem and looking closely at its various use cases.
• Bringing out the perspective of multiple stakeholders depending on the problem and structuring issues into sub-tasks to build solutions using data mining techniques.
• Understanding the various data assets and assessing their value to the given problem. Then, using techniques such as descriptive analytics and visualization, measure the quality and usability of data. Strategize additional investments into gathering the right kind of data to facilitate organizational growth.
• Data preparation, which includes data enriching techniques, to improve the overall leverage from data. Data cleaning and organizing can be considered sub-tasks for this step.
• Data modeling is where all the previous pieces come together with data mining jobs. The parameters of models are calibrated to bring about optimal business solutions. Here, the in-depth understanding of a data scientist plays a crucial role in achieving positive business outcomes. It also includes evaluating the effectiveness of the solution by validating it against business objectives and reviewing the process for future iterations and deploying the results of data mining efforts to the user along with A/B tests.
• Comprehending these work patterns and putting all the pieces together helps a BA cross over into their role as a data scientist.

It’s the Right Time to Move from Business Analysts to Data Science Role

Since we are witnessing a proliferation in demand for data scientists, it only makes sense for business analysts to want to shift into the new and trending career prospect. As computing power gets feasible and accessible, companies might look for a business analyst who has technical proficiency in dealing with their data.

This is a chance for business analysts to ride the way they are so close to and stay relevant to the market. Since data science is a lucrative and ever-expanding domain, business analysts can use their experience and expertise to gain the upper hand in securing the ideal position in a top organization.

A Business Analyst only needs to build upon their existing skillset, and so the transition to data science can be a natural move for them in advancing their careers.

If a business analyst can get good at crunching numbers, they can look like the perfect employee to the most esteemed organization. A data scientist’s skills are invariably useful to organizations of all shapes and sizes across industries.

We are always looking for skilled data scientists to be a part of our growing team and help us solve complex business challenges for our global clientele. If you think you fit the bill, we would love to hear from you.

Author: webhero