Returning to work via data analytics: map your old day to the job

Reconstruct one ordinary day from your last role

Take a pen and write down a normal Tuesday from your last job, hour by hour. The example below is a hypothetical returner, Meera, who was a sales coordinator at a distribution company for six years before a five-year break. Your day will differ; the method is the same.

9:30 — Pulling yesterday's orders. Meera exported the previous day's orders from the company's billing software into Excel and filtered out cancelled invoices. Analyst equivalent: extracting data from a source system and applying documented inclusion rules. Today that is a SQL query with a WHERE clause, and the documentation is the part people forget.

11:00 — Matching dealer statements. Two dealers disputed their outstanding balances, so she compared their statements with the company ledger line by line. Analyst equivalent: reconciliation — joining two sources on a key and investigating mismatches. This is one of the most valued analyst habits, and many new graduates have never done it.

14:00 — The weekly sales MIS. Every Tuesday she built the region-wise sales summary with a comparison to the previous week, and highlighted regions that fell. Analyst equivalent: a recurring report with period-over-period comparison. This is exactly the report worth rebuilding as your first project.

16:00 — "Why is the north region down?" Her manager asked, and she found that one large dealer had not ordered because of a pending credit limit. Analyst equivalent: root-cause analysis — decomposing a total into its parts until the change has an explanation. Being able to tell this story is worth more than any tool.

17:30 — Mailing the summary. A short email with three bullet points and the file attached. Analyst equivalent: communicating findings. Short, specific, with the caveat stated.

Almost every returner's day, whatever the department, contains two or three of these. Write yours out; those are the lines that go on your resume, described in analyst language.

Rebuild the Tuesday report in SQL

Here is Meera's weekly MIS rebuilt on a synthetic table, using a window function to compare each region with its previous week.

CREATE TABLE weekly_sales (
  week_start TEXT,
  region TEXT,
  net_sales REAL   -- synthetic, in ₹ lakh
);

INSERT INTO weekly_sales VALUES
('2026-08-31','North',18.4),('2026-08-31','South',22.1),('2026-08-31','West',15.0),
('2026-09-07','North',19.2),('2026-09-07','South',21.7),('2026-09-07','West',15.6),
('2026-09-14','North',14.9),('2026-09-14','South',22.8),('2026-09-14','West',16.1),
('2026-09-21','North',15.3),('2026-09-21','South',23.0),('2026-09-21','West',15.2);

SELECT
  week_start,
  region,
  net_sales,
  LAG(net_sales) OVER (PARTITION BY region ORDER BY week_start) AS prev_week,
  ROUND(100.0 * (net_sales - LAG(net_sales) OVER (PARTITION BY region ORDER BY week_start))
        / LAG(net_sales) OVER (PARTITION BY region ORDER BY week_start), 1) AS wow_change_pct
FROM weekly_sales
ORDER BY region, week_start;

The query flags North's drop in the week of 14 September. In the old job, Meera would have found the missing dealer by memory. In the project, add a second synthetic table of dealer-level orders and show the drop is explained by one dealer's absence — that turns a report into an analysis. Then build the same view as a one-page Power BI report, and write a short note on how the report handles a region with no previous week (the first row returns an empty prev_week, which is correct, not an error).

What has actually changed while you were away

Be specific about the refresh rather than anxious about everything:

• SQL is now expected for most analyst roles, not optional. It is the single biggest gap for returners from Excel-based jobs.
• BI tools such as Power BI are common for recurring reports that used to be emailed spreadsheets.
• Generative AI assistants are widely used to draft formulas, queries and summaries. The skill employers need is checking that output, not just producing it.
• Excel itself has newer functions such as XLOOKUP and dynamic arrays. Worth an afternoon, not a course.

The role to look for first

A reporting analyst, MIS analyst or junior data analyst role in the function you already know — sales, accounts, operations or HR — lets your earlier experience count. Some employers also advertise roles specifically open to returners; read each listing's actual requirements rather than assuming. The free skill check will show which analyst skills your resume evidences once you have added your new project.

Saying it in the interview

"I worked for six years as a sales coordinator and prepared our weekly regional MIS. I took a five-year break for family reasons, and this year I decided to come back into analytics because the report-building and reconciliation were always the parts of the job I did best. I've learned SQL and Power BI, and I rebuilt that weekly report on synthetic data with a week-on-week comparison and a dealer-level breakdown that explains a regional drop. I'd be happy to show you the query."

One sentence on the break, the rest on evidence. If you want a structured route with mentor review each week, the Data Analyst programme is built around exactly this kind of deliverable, and its sessions are recorded, which helps if your day still has fixed commitments.