Until the mid-2020s, Excel remained the primary workplace tool for analysts in many offices in Indonesia. Pivot tables, VLOOKUP, a bit of VBA macros. Enough for monthly reports, management dashboards, or ad-hoc analysis from the sales team. Recently, the situation has shifted rapidly.
Data no longer comes in thousands of rows. A medium-sized e-commerce company can generate hundreds of thousands of transactions per day from various channels. Analysts who stick to Excel are starting to encounter formula crashes, 80 MB files that open slowly, and fragile formulas as soon as one column is added.
Shifts Happening in the Office
According to data from Sakernas February 2025 released by BPS, the information and communication sector has become one of the sectors with a consistently increasing demand for labor. Along with this, job openings for data analyst and business intelligence positions on job portals like LinkedIn and JobStreet increasingly require SQL and Python as basic skills, no longer just nice-to-have.
This shift is not about Excel becoming obsolete. Excel still has a place for quick exploration and presentation. What has changed is how analysts treat data: it is no longer downloaded to a spreadsheet and then processed. Now, data is viewed in its original place. The database. And SQL becomes the language of conversation.
SQL: An Often Overlooked Entry Point
SQL is not a complicated language. Many analysts who are used to thinking in tables in Excel quickly become familiar with it. The WHERE logic is similar to filters, GROUP BY is similar to pivot, JOIN is like a cleaner VLOOKUP. What makes it powerful: SQL runs on the data source, not on the laptop. Want to filter two million rows? The database completes it in seconds, while Excel might crash before it finishes.
More importantly, SQL opens the door to other roles. Junior data engineers often start from strong SQL analysts. The interview stack for data roles in many Indonesian startups begins with SQL tests, not Python.
Python: When Analysis Starts Talking to Systems
After SQL, Python is the next logical step. Not because Python replaces SQL, but because it complements it. Pull data from the database with SQL, process it further with Pandas, visualize it with Matplotlib or Seaborn. The output can be automated reports that are scheduled, self-refreshing dashboards, or reproducible notebooks.
For analysts, Python also paves the way into basic machine learning domains. There is no need to immediately become a data scientist. But you can explain correlations, perform simple forecasting, or cluster customers with k-means clustering. Skills that were previously monopolized by engineering are now accessible to analysts.
A Career Path That Is Not a Dead End
The transition from Excel to SQL and Python does not require a new degree. What is needed is a re-mapping of how to think about data: from “how do I arrange it in a sheet” to “how do I query the database, then process it with reproducible code.”
These skills also have longevity. Tools come and go: Power BI rises, Tableau shifts, JavaScript frameworks change. But SQL has been around for over four decades and remains relevant. The Python ecosystem continues to grow for data science and analytics, with a community that is hard to surpass by other languages.
In the increasingly competitive job market in Indonesia, mastering SQL and Python is no longer a plus. For analysts serious about a long-term career, this foundation has become the standard.
References:
- Badan Pusat Statistik – Official Statistics News Sakernas February 2025 → bps.go.id
- LinkedIn Economic Graph – Skills Insights for Southeast Asia → economicgraph.linkedin.com
- Kementerian Komunikasi dan Digital – Roadmap for Indonesia's Digital Talent → komdigi.go.id