Data exists everywhere. Every purchase order, video streaming, money transaction, and booking of cab services generates information that the organizations can leverage. However, data in itself doesn't take any decision. Organizations require professionals who can derive meaningful insights from these pieces of data. This is precisely the reason why Data Analyst job roles in India have been continuing to lure freshers, professional experts, and career changers. From IT firms and management consultancies to fintech, e-commerce, and product organizations, these organizations require analysts who can find answers to the most basic business query – What is happening? Why is it happening? And what must be done next? What makes it interesting is the fact that becoming a part of an organization is not just about learning Excel and creating a fancy dashboard. Which Companies Hire Data Analysts in India? All the industries require Data Analyst. Some of the renowned companies wherein positions in relation to Analytics could be available are mentioned below: • Accenture - Analytics in Consulting, Technology and Enterprise • Deloitte - Business Intelligence, Consulting and Strategy • TCS - Enterprise Reporting and Analytics Projects • Infosys - Data, Digital Transformation and BI jobs • Wipro - Enterprise Analytics and Technology Solutions • Amazon - Operations, Customer, Product and Business Analytics • Flipkart - E-commerce, Customer Behavior and Commercial Analytics • Capgemini - Consulting and Enterprise Data Solutions • Cognizant - Technology, Reporting and Business Analytics • Genpact - Analytics, Operations and Business Transformation It should be noted that the position may not necessarily have the name "Data Analyst". The candidates must look out for positions such as Business Data Analyst, BI Analyst, Reporting Analyst, MIS Analyst, Product Analyst, Operations Analyst and Junior Business Analyst. What Are Companies Actually Looking For? This is where most newbies make their mistakes. They know ten tools but do not know how to solve a single business problem. In consulting scenarios, consultancy enterprise needs can make an analyst deal with big data sets along with knowing what exactly the client is trying to accomplish. A well-designed dashboard is useless if the question being asked is wrong. As for the modern-day commercial analytic needs, those are much more than just reporting the previous month’s results. Companies want their analysts to recognize customer behavior, revenue models, issues and opportunities. And that’s why the business understanding becomes just as vital as technical skills. The Data Analyst Skill Stack You Need For individuals aspiring to work as Data Analysts in India, it is important to construct a solid stack of skills rather than getting a plethora of certifications. 1. Excel – Is Still Very Important Do not underplay Excel; get acquainted with: • Pivot tables and charts • XLOOKUP and other lookup functions • Conditional functions • Data cleansing • Power Query • Simple Dashboarding 2. SQL – Your Most Important Skill There can be millions of entries in company databases. SQL will help you extract information that is important. Be well-versed with JOINs, GROUP BY, subqueries, CTE, aggregate functions, and window functions. 3. Power BI or Tableau Being able to make a chart is not difficult. But making one which effectively conveys the insight is the hard part. Hone your skills in Power BI Dashboarding, Tableau Visualization, KPI tracking, data storytelling, and dashboards. 4. Python At this stage Python comes in the game. Data cleaning, exploration and automation via Pandas, NumPy and Matplotlib is possible. Also, different companies have their own modeling stack. That is why learning how SQL, Python, Excel and Business Intelligence (BI) tools work together will make you more flexible. The Forgotten Skill: Communication Think of being told that the customer retention rate fell by 18%. You present the board with 25 slides full of charts. The board asks: "And what do we do now?" This is where the difference between reporting and actual analytics comes in. Great analysts are aware of different ways of presenting an executive summary. Top-level executives are not always interested in hearing every detail. What they actually want to hear is the insight, its business implications and what the next steps are. Not "There are negative variances in the dataset among different cohorts." But "We have seen that repeat purchases decreased mostly for new sign-ups. It means that we need to think of onboarding and retention marketing campaigns." Same data. But great communication. Projects Can Become Your Proof It is not necessary for freshers to start off with a full-time job in order to get some experience. Create project ideas on the basis of real-life scenarios such as: • E-commerce sales dashboard • Customer churn analytics • HR attrition dashboard • Marketing campaign analytics • Banking transactions analytics • Retail inventory analytics The reason behind this practical approach is that students are now trying their hands at industry-based environments like Sparktech Pro Agile, which is one of the leading data analytics training organizations in Pune. The main point here is: Don't sell them what you know; sell them what you can do! The Future Belongs to Analysts Who Think Formulas, SQL, dashboards—all of these can be created using AI much faster than ever before. However, that does not mean that there will be no need for analysts anymore—they will just change their functions. Organizations will now need more analysts who know how to ask right questions, confirm the work done by AI, comprehend the business logic and make suggestions based on the data available. Instead of asking “Will AI replace Data Analysts?” try asking yourself “Am I going to be the one who knows how to utilize AI better than everybody else?”