Business analyst vs data analyst explained 

A business analyst should be trained in modelling; meanwhile, data analysts need to have excellent data mining skills.

Over the years, companies have used data analysts and business analysts conversely. Still, if you take a deeper look into their job descriptions and personal experiences, we can bring out their differences. However, people working with data are often lumped together in smaller companies. But, in bigger organisations, both data analysts and business analysts are hired for various critical tasks. 

Moving on, let us take a closer look at these two roles to understand their nuances and what they bring to the table.

Business Analyst

Ideally, the role of a business analyst is to calculate every single part of the business to make sure that it is on the right track. They play a crucial role in maintaining the growth of the company. Business analysts create, develop, and maintain important metrics commonly known as KPIs (Key Performance Indicators). It helps quantify the business and allows the company to rapidly make a sales process or a product improvement.

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Apart from working with metrics, business analysts investigate internal and external business sections to discover anomalies. During this process, the companies can plan with the help of the business analyst’s findings to make the business more efficient and successful. However, a successful business analyst needs proper tools to drill down on the key issues facing a business. They employ various tools like SQL, Tableau (visualisation tools), Excel, Salesforce and other CRM tools, KPI analysis and then present findings to stakeholders. As a business analyst, you will get a lot of “screen-time” because it is vastly customer-facing, even if the “customer” is internal.

Data Analyst 

Although it might be borderline confusing with the business analyst role, there are again key differences in skills, goals, and description. A data analyst must focus on the company data and need not be liable for the data’s impact on the business. Ultimately, they will be asked to perform both duties. Still, more importance is thrown on data warehousing, data tables, and SQL code developed to create business metrics and not present them to stakeholders.

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In an organisation, data analysts will be required to design and maintain data systems and databases and also troubleshoot potential issues which arise from time to time. Another critical role they assume is to mine and clean data and prepare it for analysis. To hit the nail on the head using data, there are a few things that data analysts must be familiar with, like SQL, R and Python programming, data architecture, and routine reporting. In addition, data analysts make sure that the data does not include any noise, and SQL queries are optimised for deploying the data efficiently and effectively. A data analyst focuses on the technical aspects of the data rather than business-oriented analysis.

Key Differences

Now that we have understood business and data analysts’ roles, let us see how different they are. To start with, business analysts use data to discover problems and solutions but do not get into their technicalities. Although, they work on a more conceptual level to define strategy and communicate with stakeholders and are mostly concerned with the business facet of data.

On the other hand, data analysts spend most of their time gathering raw data and intel from different sources, cleaning and moulding it, and using a range of specialised techniques to extract useful information and develop conclusions. Similarly, business analysts have more extensive industry knowledge and experience in e-commerce, manufacturing, or healthcare. This is why business analysts often do not sweat about the technical aspect of data and its procurement. However, they must have a working knowledge of statistical tools, common programming languages, networks, and databases.

Data analysts’ roles can be very linear due to their rigorous practices and the constant hunt for data. However, a business analyst should be trained in modelling and gathering requirements; meanwhile, data analysts need to have excellent data mining skills and be familiar with machine learning and AI (a must-have for industries today). Finally, business analysts with a strong foundation in business administration are an important asset. On the other hand, data analysts should ideally have an information technology background that will help deal with complex algorithms, databases, and statistics. 

Irrespective of which path you choose to follow, the boom of big data has created many opportunities for both business and data analysts. Important trends like explainable AI, augmented AI, blockchain, and data fabric have taken over the world, and the importance of key data and what to do with it has finally been realised. This can motivate more people to take up this challenge and kick-start their business or data analysts careers

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by Vijayalakshmi Anandan

The Deep Learning Curve is a technology-based podcast hosted by Vijayalakshmi Anandan - Video Presenter and Podcaster at Analytics India Magazine. This podcast is the narrator's journey of curiosity and discovery in the world of technology.

Akashdeep Arul
Akashdeep Arul is a technology journalist who seeks to analyze the advancements and developments in technology that affect our everyday lives. His articles primarily focus upon the business, cultural, social and entertainment side of the technology sector.

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