The comparison between data scientists and business analysts is not very common. But surprisingly, they share a lot of business goals. so, How they work towards these goals is what differentiates them. Data scientists work towards the goals using a statistical and mathematical lens, while business analysts approach the goals with an integrated outlook. We will look at the work done by both professionals and cover the similarities and differences in this article.
Data Scientist vs Business Analyst: Main Differences
For instance, Business Analysts help identify business problems and opportunities and provide solutions for their organizations. Suppose the business analyst (BA) identifies a problem of high customer churn. So, the BA will identify all the stakeholders involved, assess the organization’s current processes, set timelines, define measurable objectives, requirement elicitation, and documentation. They also work on getting the necessary approvals. Business analysts are domain experts who collaborate with data professionals in order to address business problems – solve the churn problem.
Once the data scientist provides insights – a process of identifying customers who will churn – business analysts use specific tools and business analyst skills to assess the insights from the business perspective, market trends, anomalies, and other key business interests. They then prepare business visualizations and reports on steps required to prevent churn. These insights are then communicated to the business stakeholders, and in collaboration, business analysts help in decision-making, planning, and implementing changes in the organization. They are also in charge of generating user manuals and final documentation.
Business Analyst – Goals
- Provide feasible and economical solutions to the business problems in the given timeframe
- They are change catalysts who work with all the stakeholders involved
- Define a roadmap and develop an actionable plan
- Determine KPIs
- Manage, implement changes and work on documentation
Business Analyst – Skills Required
- Excel, MS Office, SQL, visualization tools like Tableau, Power BI
- Jira, Trello, Visio, Pencil
- Problem-solving and analytical thinking
- Communication & Interaction
- Leadership & negotiation skills
Data scientists prepare enormous amounts of business data for analysis. Preliminary data exploration and mining gives insights into the data, which are helpful while creating models. So, Machine learning algorithms, statistics, artificial intelligence, and much more are used to solve the business problem. Data scientists may use regression analysis to predict and forecast.
So, data scientists may prepare and finetune a model to predict customers who will churn and suggest corrective actions before the customer leaves. Hence, Data scientists communicate the technical results with business analysts and other stakeholders.
Data Scientist – Goals
- Utilize data and machine learning algorithms to solve business problems.
- Make business more efficient by interpreting data into actionable insights.
- Create accurate solutions.
Data Scientist – Skills Required
- Statistics, Python, R, SQL, NoSQL, Hadoop, Spark, etc.
- Machine Learning algorithms
- Big Data Techniques
- Data Visualization (Tableau, Power BI)
- Problem-solving, Analytical thinking, Communication
- Business Knowledge
Both professionals work towards recommending solutions to business problems. They work with stakeholders on solutions, results, and impact. The role of a business analyst may change in different organizations.
IIBA has defined a new business data analyst role, who bridges the gap between business and actual mathematical-statistical data analysis. So, A Business Data Analyst understands the big picture and the business context is a visionary who sets a vision about the breadth and width of the analytics initiative and is a business domain expert with data analysis knowledge. Business data analysts work as advisors to data scientists by guiding their analysis with business expertise. They use data visualizations and data storytelling to engage decision-makers and drive a data-driven culture.
Check out more on how to begin your journey as a certified business data analyst (CBDA).