Indonesia has an interesting paradox regarding data. According to a survey by Katadata Insight Center 2024, around 72% of companies in Indonesia have been collecting operational data regularly. However, of that number, less than a third actually utilize it for strategic decision-making. The rest? Data just piles up in spreadsheets, reported monthly to management, and then stored who knows where.
This phenomenon is not new. Many professionals have been accustomed to working with Excel since the beginning of their careers, but their skills are stuck at the entry level: creating tables, calculating totals, making standard graphs. Meanwhile, the need for analysis is becoming increasingly complex, especially since AI-based tools have started to enter the spreadsheet ecosystem.
From Spreadsheet to Decision: A Distance Often Underestimated
Budi Setiawan, Research Director at McKinsey Indonesia, once stated in an industry forum that “Indonesian companies on average only utilize 15-20% of the data potential they possess.” Most analysts' time is spent cleaning data, not analyzing it. This cleaning process, known as data wrangling in the data science world, can consume 60-80% of the total time of an analysis project.
What makes the situation trickier is that many medium-sized companies in Indonesia still rely on Excel as their sole analysis tool. Not because there are no alternatives, but because Excel has been embedded in their operational DNA for decades. So instead of a total migration to another platform, a more realistic approach is to upgrade Excel skills themselves.
AI in Excel: More Than Just an Additional Feature
Microsoft has integrated Copilot into its 365 ecosystem, including Excel. But long before Copilot arrived, there were already many advanced analysis techniques that could be done directly in Excel: Power Query for automating data cleaning, Power Pivot for modeling data from various sources, and DAX for calculations that far exceed the capabilities of regular formulas.
The problem is, these features are relatively hidden. They are not in the main menu, not taught in standard Excel training, and the documentation is more suited for developers than business analysts. As a result, professionals who actually need them are the least likely to touch them.
A survey from the Central Statistics Agency regarding the adoption of information technology in the business sector shows that the use of data analysis software in medium-sized companies is still dominated by Microsoft Excel (89%), followed by Google Sheets (34%), and then specialized tools like Tableau or Power BI (below 12%). This figure confirms one thing: improving data literacy in Indonesia must start from Excel, not jump to more advanced tools.
Dashboard: The Bridge Between Data and Decision
One of the most direct outputs of analysis that impacts decision-making is a dashboard. Not just any dashboard that displays static numbers, but an interactive dashboard that allows stakeholders to explore data from macro to micro levels without needing to understand the formulas behind it.
This is where the combination of Excel and AI becomes powerful. With the right techniques, an analyst can build a dashboard that automatically pulls data from various sheets, cleans it via Power Query, and then displays real-time updated visualizations. Add AI prompts to help write complex formulas or create automatic interpretations, and productivity can increase significantly.
“What we see in the field is that companies that successfully build a data-driven culture usually start with small things: one dashboard that is truly used, not dozens of reports that are never read,” said Rinaldi, a data analytics practitioner who has assisted various Indonesian companies in their digital transformation.
Investing in Skills, Not Just Tools
The Ministry of Manpower in the 2025-2030 Human Resource Development Roadmap explicitly mentions data literacy as one of the priority competencies that need to be improved in the Indonesian workforce. The context is clear: the AI era does not replace humans who can analyze data, but replaces those who cannot.
The good news is that this skill gap is not something that requires years to close. With the right approach and a focus on practical applications, professionals who are already familiar with Excel can leap to a much higher level of analysis in a relatively short time. The key is not to learn complicated statistical theories, but to understand how to use the tools that are already in front of them more intelligently.
In the midst of increasingly data-driven business competition, data processing skills are no longer a nice-to-have. They have become a basic necessity, as important as communication or time management skills. And for those who are already accustomed to Excel, the next step may be closer than they think.
References:
- Katadata Insight Center – Survey on Data Analytics Adoption in Indonesian Companies 2024 → katadata.co.id
- Central Statistics Agency – Information and Communication Technology Statistics → bps.go.id
- Ministry of Manpower of the Republic of Indonesia – Human Resource Development Roadmap 2025-2030 → kemnaker.go.id