MIS executive and analyst interviews test the office and analytics tools businesses run on: advanced Excel, VBA, Google Sheets, Power BI, Tableau, Google Analytics, Jira and PowerPoint. Expect practical questions on formulas, pivot tables, dashboards and reports, then automation, data modelling and how you turn data into decisions.
30 questions · Updated
Fundamentals, definitions and simple scenarios. Good for freshers and warm-ups.
An MIS (management information system) executive collects data from different sources, cleans it and turns it into regular reports and dashboards for managers, such as daily sales, collections, attendance or stock reports. The job is mostly Excel and increasingly Power BI or Tableau, plus accuracy, meeting deadlines and explaining what the numbers mean.
VLOOKUP searches the first column of a range and returns a value from a column to the right, given by a number, and defaults to approximate match unless you pass FALSE.
XLOOKUP takes separate lookup and return ranges, so it can look left, defaults to an exact match, has a built-in "if not found" value, and does not break when columns are inserted.
INDEX with MATCH does the same in older versions of Excel.
A pivot table summarises a large table quickly: for example total sales by region and month, or count of tickets by status.
You drag fields into rows, columns, values and filters, and can change the summary to sum, count or average.
It needs clean data in a table with one header row and no blank rows.
Slicers and pivot charts make it interactive.
Styles for headings and body text, which give consistent formatting and enable an automatic table of contents, page and section breaks, headers and footers with page numbers, tables, captions and cross-references, track changes and comments for reviews, templates, mail merge for personalised letters and labels from Excel data, and exporting to PDF.
One message per slide with a clear headline, minimal text in large readable fonts, visuals and simple charts instead of dense tables, consistent design using the slide master and a template, a logical story from problem to recommendation, and a strong summary.
Detailed data goes into appendix slides or handouts.
Rehearsing the timing matters as much as the slides.
Google Sheets is browser-based with excellent real-time collaboration, easy sharing and version history, Google Forms integration and functions such as IMPORTRANGE and QUERY, which suits shared trackers and team inputs.
Excel is more powerful for large datasets, complex models, Power Query, Power Pivot, advanced charts and VBA, and is standard in finance and MIS work.
Many teams use both.
Correct formatting makes reports readable and prevents mistakes: thousand separators, the Indian lakh and crore format where expected, consistent decimals, currency symbols, percentages, and proper date formats. Formatting changes only how a value looks, not the value itself, so text that looks like numbers, a common problem in imported data, must be converted to real numbers before calculation.
Ctrl+Arrow keys to jump to data edges, Ctrl+Shift+Arrow to select ranges, Ctrl+T to create a table, Ctrl+Shift+L for filters, Alt+= for AutoSum, F4 to lock references, Ctrl+D and Ctrl+R to fill down and right, Ctrl+; for today's date, Ctrl+1 for format cells, Alt+N+V for a pivot table, Ctrl+Page Up and Page Down to switch sheets, and Ctrl+Z to undo.
Use clear folder structures and file names with dates or versions, such as Sales_MIS_2026-04.xlsx, store files on shared drives such as OneDrive, SharePoint or Google Drive rather than emailing copies, use version history instead of many duplicate files, set the right access permissions, and keep a single master data source that reports link to.
Applied problems, trade-offs and questions about your own projects.
I convert the range to a table, remove duplicates, and fix spacing and case with TRIM, CLEAN and PROPER.
I split columns with Text to Columns or TEXTSPLIT, convert text numbers and dates to real values, and use filters to find blanks and errors.
For repeated monthly cleaning I use Power Query, which records every step so next month I only click Refresh.
A relative reference such as A1 changes when the formula is copied, so it points to a different cell relative to its new position.
An absolute reference such as $A$1 stays fixed.
Mixed references like $A1 or A$1 lock only the column or row.
Absolute references are needed for constants such as a tax rate cell used in every row.
I start with the questions managers need answered and pick a few key metrics.
I prepare clean data (Power Query or a data model), then build visuals: KPI cards at the top, trends over time, and breakdowns by region or product, with slicers for filtering.
I keep it simple, label clearly, use consistent colours, and automate the refresh so the dashboard is never out of date.
The same principles apply in Excel, Power BI or Tableau.
Work is organised into projects containing issues such as epics, stories, tasks and bugs.
Each issue has an assignee, priority, status and estimate, and moves through a workflow like To Do, In Progress, In Review and Done.
Scrum teams plan sprints from the backlog and track progress on a board and burndown chart; Kanban teams limit work in progress.
JQL queries and dashboards produce reports for managers.
Users and new users, sessions, engagement rate (the opposite of bounce rate in GA4), average engagement time, traffic sources by channel, top landing pages, and conversions defined as key events such as purchases, sign-ups or form submissions. I compare periods, segment by device and channel, and use explorations such as funnels to find where users drop off.
Highlight values that need attention, such as targets missed in red and achieved in green, overdue items, top or bottom performers, and duplicates.
Data bars and colour scales show size at a glance, and icon sets show trends.
Formula-based rules can highlight entire rows.
Too many colours reduce clarity, so formatting should focus on exceptions.
They summarise raw data by several conditions, such as total sales by region and month with SUMIFS, or number of orders by status and salesperson with COUNTIFS, and AVERAGEIFS for averages. Combined with cell references for criteria, a single summary table updates automatically when the month or region is changed, forming the core of many MIS reports.
Power Query connects to source files, folders, databases or ERP exports and records each cleaning step, such as removing columns, fixing dates, merging lookup tables and appending monthly files from a folder. Each month you drop the new file into the folder and click Refresh All, and the whole report updates in seconds without manual copy-paste, which also reduces errors.
Common KPIs include sales versus target, growth versus last year, collections and outstanding receivables by age, inventory levels and stock days, attendance and productivity, order fulfilment time, customer complaints and resolution time, and cost per unit. Each KPI needs a clear definition, data source, owner and frequency, agreed with the business.
Use data validation with dropdown lists, input messages and error alerts, lock formula cells and protect the sheet, use tables so formulas extend automatically, add clear instructions and examples, colour-code input cells, and include checks that flag missing or wrong entries. Standard templates make consolidating data from many people much easier.
Column or bar charts for comparisons across regions or products, line charts for monthly trends, combo charts for values with a percentage (such as sales and growth), waterfall charts for explaining changes in profit, and sparklines for compact trends in tables. Keep charts simple, label them clearly and keep colours consistent across reports.
System design, deep internals, leadership and tough follow-ups.
I map the steps first.
Data import and cleaning go into Power Query, so it refreshes in one click.
Calculations move into formulas or a data model instead of manual copy-paste.
Remaining repetitive steps, such as formatting, saving PDFs or emailing, can be automated with a VBA macro or Power Automate.
Better still, if the data source allows it, I build a Power BI report with scheduled refresh so nobody has to run it.
Both build interactive dashboards from many data sources.
Power BI is cheaper, integrates tightly with Excel, Microsoft 365 and Azure, and uses Power Query and DAX for data modelling.
Tableau is known for flexible, polished visual exploration and is strong with very large datasets.
The choice usually depends on the company's existing tools and budget; the skills of data modelling and good dashboard design transfer between them.
I reconcile totals with the source system, for example matching report sales with the accounting figure.
I check row counts after each transformation, look for duplicates and blanks, and test formulas against a few rows calculated by hand.
I keep raw data separate from calculations, document the logic, and add checks such as a cell that flags a difference.
When something looks surprising, I investigate before sending it.
I lead with the conclusion and the action I recommend, then show the few charts that support it.
Each slide has one message in its title, for example "North region sales fell 12% after the price change", rather than just "Sales by region".
I remove clutter, highlight the important number, keep detailed tables in the appendix, and prepare for likely questions.
Meet the users to understand the decisions the report supports and the questions they ask, agree KPIs and definitions, identify data sources and owners, sketch the layout with a summary at the top and details below, build it with clean data, tables, Power Query and pivot tables or Power BI, add checks and reconciliations, automate the refresh, and collect feedback after the first few cycles.
Compare totals in the export with the system's own reports, such as total sales or ledger balances for the period, check record counts, look for missing or duplicate entries, date range issues, cancelled vouchers, and differences in how amounts include or exclude tax. Document the reconciliation and investigate differences until the numbers match before publishing reports.
Much business data lives in databases behind ERP, CRM and e-commerce systems.
SQL lets you pull exactly the data you need, filter, join tables, group and summarise large datasets directly, instead of exporting huge files into Excel.
It makes reports faster, more reliable and automatable, and is often required for data analyst roles.
Share reports only with people who need them, using access permissions on shared drives or row-level security in Power BI rather than emailing files, remove or mask personal and sensitive data that is not needed, avoid putting salary or customer data in widely shared sheets, protect files with passwords when emailing is unavoidable, and follow the company's data policies and the DPDP Act.
Check the definitions first, such as gross versus net sales, order date versus invoice date, or inclusion of returns and taxes.
Then compare data sources, date ranges, filters and refresh times, and trace a sample of records through both reports.
Once the cause is found, agree a single definition and source with stakeholders, document it and fix the report that is wrong.
Start with one high-value report, build a clean data model from the same sources, recreate key views as interactive dashboards with scheduled refresh, validate numbers against the existing Excel reports, train users, and run both in parallel briefly. Then expand report by report, set up workspaces, apps and permissions, and retire manual reports once users trust the new ones.