What a data and analytics team does
The team collects data from the ERP, CRM, HRMS, website and spreadsheets, cleans it and stores it in a database or warehouse where it can be joined up. On top of that it builds recurring MIS reports and dashboards, answers one-off questions from leaders, fixes the definitions of key numbers such as active customer or monthly revenue so every department uses the same meaning, and runs deeper analysis on pricing, demand, churn, credit risk or store performance. Data scientists build statistical and machine learning models where the volume of data justifies them. The team also controls who can see sensitive data such as salaries and customer phone numbers.
- MIS and reporting: daily, weekly and monthly reports for each department
- Business intelligence: dashboards, KPI definitions and self-service reports
- Data engineering: pipelines, the data warehouse and data quality checks
- Data science: forecasting, segmentation, scoring and experiments
- Data governance: access rules, masking and one definition for each metric
How the team is organized at 50, 500 and 5,000 employees
At 50 employees there is rarely a separate team. An MIS executive in finance or operations pulls data from Tally or the billing system into Excel and circulates it every Monday, and the founder reads it. At 500 employees a small analytics team appears, often inside finance, sales operations or the CEO's office: an analytics manager, two or three business analysts, a BI developer who builds dashboards, and perhaps one data engineer who automates the data feeds.
At 5,000 employees, and much earlier in e-commerce, fintech or lending companies, data becomes a department under a head of data or chief data officer. It splits into data engineering, BI and reporting, analytics teams embedded with business units such as marketing, risk or supply chain, and a data science group. Some companies keep a central platform team and place analysts inside each department with a dotted line to the head of data.
Working with business teams and IT
Analysts are only useful if business teams ask good questions, so the head of data agrees a request process with department heads: what decision is being made, which numbers are needed, and by when. Finance and the data team must reconcile revenue figures before they reach the board, or two versions of the truth will circulate. IT owns the source systems and servers, while the data team owns the warehouse and the reports built on it. HR asks for headcount, attrition and overtime trends, and marketing for campaign attribution. Every report that shows personal or salary data needs a named owner who approves access.
Reporting lines and data access approvals
Analysts and BI developers report to an analytics manager, data engineers to a data engineering lead, and data scientists to a data science lead, all under the head of data, who reports to the CEO, CFO or CTO depending on where the function started. Embedded analysts may take daily direction from the business unit head. Access to raw data follows a simple chain: the requester's manager approves the business need, the data owner in the business approves the scope, and the data team grants it, masking fields such as salary, bank account and phone number unless there is a clear reason to show them.
Typical data and analytics team structure
Data and Analytics designation hierarchy
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Book a free demoSee pricingFrequently asked questions
What does a data and analytics team do?
A data and analytics team gathers data from the company's systems, cleans and stores it, and turns it into reports, dashboards and analysis that help managers decide. It defines key metrics so everyone uses the same numbers, answers business questions, builds forecasts and, in larger companies, statistical or machine learning models. It also controls access to sensitive data.
What is the difference between a data analyst and a data scientist?
A data analyst works with existing data to describe what happened and why, using SQL, Excel and dashboard tools, and presents findings to business teams. A data scientist builds predictive models, such as demand forecasts or credit scores, using statistics and programming in Python or R. Analysts are needed first; data scientists add value once data volume and quality are high enough.
Where should the analytics team sit in a company?
Early on, analytics usually sits inside finance or the CEO's office, because those teams need consolidated numbers first. As demand grows it becomes its own department under a head of data, often with analysts embedded in business units. Placing it under IT works for data engineering but can slow business analysis when priorities are set by system projects.
Who does an MIS executive report to?
An MIS executive usually reports to the head of the department whose reports they prepare, such as the finance manager, sales operations manager or operations head. In companies with a central data team, the MIS executive reports to the BI or reporting manager and serves several departments on an agreed schedule of daily, weekly and monthly reports.