Data Analytics in Accounting: Where to Start If You Only Know Excel
You already do analytics every time you build a reconciliation. Here is the honest shortest path from Excel to pulling your own data, and why the government projections say this is the skill that matters.
If you can build a reconciliation, you already understand the logic of data analytics. The missing piece is usually getting the data yourself instead of waiting for an extract. The shortest useful path is SQL at a working level, which takes weeks rather than years, and it matters because clerical accounting work is projected to decline 6% while analytical work grows. This post covers the four starting projects that actually teach you something, what to skip, and how to describe the skill so it counts in an interview.
Accountants hear "data analytics" and picture machine learning, which is why most of them never start. The actual gap between where you are and where this skill pays off is much smaller than it looks.
If you can build a reconciliation, you already think like an analyst. You define a population, join it to a second source, isolate the differences, and explain why they exist. That is analytics. The part you are probably missing is getting the data yourself instead of emailing someone for an extract.
The goal is not to become a data scientist. It is to stop being dependent on someone else to see your own numbers. That single change is what moves you from reporting the past to being asked what it means.
Why this is the skill worth having
The projections are unusually clear about where accounting work is going.
Bookkeeping, accounting and auditing clerk roles are projected to decline 6% from 2025 to 2035, and BLS states the reason directly: "Software innovations have automated many of the tasks performed by bookkeeping, accounting, and auditing clerks." Median wage, $50,670.
Over the same decade, accountants and auditors grow 5% at a median of $83,680, financial analysts grow 7% at $103,570, and data scientists grow 35% at $120,230.
The work being automated is the work of moving data between places. The work growing is the work of deciding what the data means. Analytics skill is how you move from the first category to the second without leaving accounting.
Start with SQL, not Python
This is the most common wrong turn. People start with Python because it sounds more impressive, then spend three months on syntax they never use at work.
SQL is the right first step for an accountant for three reasons. It is the language your company's data actually lives in. It maps almost perfectly onto what you already do, since a JOIN is a lookup and a GROUP BY is a pivot table. And the working bar is low: if you can write a SELECT with a WHERE, join two tables, and aggregate with GROUP BY, you can answer most real accounting questions.
That is a few weeks of evenings, not a career change. Python is worth learning after, when you have a repetitive task that SQL cannot reach.
The fuller comparison is in Should Accountants Learn SQL or Python?.
Translating what you already know
The fastest way to learn this is to notice that you know it already under different names.
- A VLOOKUP or XLOOKUP between two tabs is a JOIN
- A pivot table is a GROUP BY with an aggregate
- A SUMIFS is a SUM with a WHERE clause
- Filtering a range is a WHERE
- Removing duplicates is SELECT DISTINCT
- Your reconciliation's unmatched items are a LEFT JOIN where the right side is null
That last one is the whole concept of a reconciliation expressed in SQL, and once it clicks, the language stops feeling foreign.
The four projects that actually teach you something
Tutorials teach syntax. Projects teach judgment. These four, in order, are what I recommend to accountants starting out.
1. Rebuild a reconciliation you already do by hand
Take a reconciliation you perform monthly and reproduce it in SQL or Power Query. You already know the right answer, which means you will catch your own mistakes. This is the single best first project because correctness is verifiable.
2. Profile a general ledger extract
Pull a period of GL data and answer basic questions: how many entries, posted by whom, which accounts have the most manual entries, which entries landed on a weekend or after the close. You will find something surprising. Accountants almost always do.
3. Build a variance explanation from source data
Instead of receiving a variance report, build one. Join actuals to budget, compute the difference, rank by absolute dollar impact, and write one sentence about the top three. This is the exact skill FP&A interviews test.
4. Automate something that takes you an hour a week
Find one recurring manual task and remove it. Not the biggest one, the most boring one. The point is to experience the full loop of noticing waste, building a fix, and getting the time back.
Work on your own company's data wherever policy allows, not on a tutorial dataset. Sample datasets are clean, and the entire skill of accounting analytics is handling data that is not. Duplicate keys, inconsistent entity codes and dates stored as text are the real job.
Get the free resume template
Join finance and accounting pros getting my weekly positioning, resume, and interview tactics. I will send my resume template as a welcome gift.
What to skip
Being honest about this saves months.
Skip machine learning. Skip statistics beyond the basics of averages, distributions and outliers. Skip building dashboards nobody asked for. Skip certifications as a first move, because a project you can describe beats a certificate every time in an accounting interview.
Also skip trying to learn this in a vacuum. The accountants who build this skill fastest always attach it to a task someone already wants done.
How to describe the skill so it counts
A resume line that says "proficient in SQL" is unverifiable and gets discounted. Describe the build instead, with scope and outcome.
Weak: Used SQL for data analysis.
Strong: Rebuilt the monthly intercompany reconciliation in SQL against a 1.2 million row ledger extract, cutting preparation from six hours to under thirty minutes and surfacing a recurring entity-coding error.
That version tells the reader the data size, the mechanics, the time saved and a finding. The finding is the part that signals judgment rather than tooling.
More on this in How to Quantify Your Accounting Experience.
Pick the reconciliation you most dislike doing, rebuild it in SQL this month, and write down what it found and how much time it saved. That is one project, one resume bullet, and one interview story, and it takes a few evenings.
Frequently asked questions
Do accountants need to know data analytics?
Increasingly, yes, though the practical bar is lower than the phrase implies. Getting your own data and explaining what it means is enough, and it maps directly onto the shift BLS describes toward advisory and analytical duties.
Should I learn SQL or Python first as an accountant?
SQL first, almost always. It is where your company's data lives, it maps onto lookups and pivot tables you already use, and a working level takes weeks. Python is the better second step once you have a repetitive task SQL cannot handle.
How long does it take to learn SQL for accounting work?
A few weeks of consistent evenings to reach a genuinely useful level, meaning SELECT, WHERE, JOIN and GROUP BY. Most accounting questions never need more than that, and depth comes from doing real work rather than more tutorials.
Is data analytics replacing accounting jobs?
It is replacing a specific kind: clerical roles are projected to decline 6% through 2035 because of software automation, while accountants grow 5% and analysts 7%. The skill is how you stay on the growing side. I cover the whole question in Will AI Replace Accountants?.
What tools should an accountant learn for analytics?
SQL, Power Query, and one BI tool such as Power BI or Tableau, in that order. Power Query is especially underrated because it handles most of what people used to write macros for and survives being handed to a colleague.
Can I do analytics work without leaving accounting?
Yes, and that is the point. Technical accounting, internal audit, finance systems and FP&A all now have analytics-heavy versions, and those roles tend to pay better and automate less.
I teach the judgment side of this in a free live session each week. Grab a seat at summitresume.com/resources.
Next read: What Excel Skills Do You Need for a Corporate Finance Job?.
Want to turn skills into offers?
I help finance and accounting pros package their strengths and land competitive roles at the Big 4 and corporate finance. Book a free Career Strategy Call to get started.
