A few years ago, I sat in a boardroom in Kuala Lumpur with the leadership team of a mid-sized logistics company. They had just invested over 200,000 ringgit in a business intelligence platform. Dashboards covered every wall screen. Real-time metrics blinked and updated. There were charts tracking everything from fuel costs to driver break times to warehouse temperature. The CEO turned to me proudly and said, “We are now a data-driven organisation.” I asked him a simple question: “Based on all this data, what is the single most important decision you need to make this quarter?” Silence. He looked at the dashboards, then back at me, and said, “I suppose we need to think about that.” That moment captures the core problem with how most SMEs approach data. They collect it. They display it. They report on it. But they do not actually use it to make better decisions.
I have seen this pattern across dozens of organisations in ASEAN. The enterprise technology vendors have sold a compelling story: buy our platform, connect your data, and you will unlock insights that will transform your business. And for large corporations with dedicated analytics teams, that story can be true. But for SMEs with 50 to 500 employees, the reality is different. You do not have a data science team. You do not have analysts who can interpret complex dashboards. Your managers are already stretched thin running operations. Adding a business intelligence tool without changing how decisions are made just adds a layer of complexity without adding a layer of value.
The truth is that being data-driven has nothing to do with how much data you collect or how fancy your dashboards are. It has everything to do with whether data actually changes the decisions you make. During my time serving on various boards and advising companies through ICG Asia, I have developed an approach to data-driven leadership that works for SMEs. It is simpler than what the technology vendors sell, and it is dramatically more effective because it starts with decisions rather than data.
The Leadership Trap: Collecting Data Without Making Decisions from It
This trap has three variations, and most SMEs fall into at least two of them. The first is data hoarding: collecting everything because you might need it someday. You track hundreds of metrics because the software makes it easy. Your team spends hours preparing reports that nobody reads. You have data on everything and insight from nothing. The cost is not just the software licence. It is the hours your team spends collecting, cleaning, and reporting data that never influences a single decision.
The second variation is analysis paralysis. You have the data, but instead of using it to make decisions, you use it to delay decisions. “We need more data before we can decide.” “Let us run another analysis.” “The numbers are not conclusive yet.” I worked with a retail chain in Bangkok that spent four months analysing whether to open a new location. They had foot traffic data, demographic analysis, competitor mapping, rent comparisons, revenue projections with three scenarios. By the time they finally decided to proceed, the landlord had leased the space to someone else. The competitor who took that space made their decision in two weeks using a fraction of the data.
The third variation is vanity metrics: tracking numbers that make you feel good but do not inform action. Website traffic that does not convert. Social media followers who do not buy. Employee satisfaction scores that do not correlate with retention. Revenue growth that masks declining profitability. Vanity metrics are dangerous because they create a false sense of progress. You see numbers going up and assume things are going well, until they suddenly are not, and by then you have lost months or years of opportunity to course-correct.
The Decision-First Data Approach: Start with the Question, Not the Dashboard
Step 1: Start with the Decision
Every data initiative in your organisation should begin with a specific decision that needs to be made. Not a vague area of interest. A concrete decision with a timeline. Examples: Should we hire three more warehouse staff this quarter or invest in automation? Should we renew our lease or relocate? Should we continue with Supplier A or switch to Supplier B? Should we expand into the Vietnam market in 2025 or consolidate our position in Malaysia? These are real decisions with real consequences. Each one can be informed by data. But you have to name the decision first.
I worked with a food manufacturing company in Johor that was struggling with rising costs. The CEO wanted to “analyse our cost structure.” That is not a decision; it is an activity. When I pushed him to name the actual decisions, he came up with three: Should we switch raw material suppliers? Should we automate the packaging line? Should we raise prices or absorb the cost increase? Now we had three specific decisions that data could inform. Instead of a general cost analysis that would take weeks and produce a 40-page report nobody would read, we had focused questions with clear action implications.
Step 2: Identify What Data Would Inform the Decision
Once you have the decision, ask: What would I need to know to make this decision with confidence? For the supplier-switching decision, the food manufacturer needed: current cost per unit from each potential supplier, quality rejection rates for each supplier, delivery reliability data, and switching costs. That is four data points. Not forty. Not four hundred. Four. For the automation decision, they needed: current labour cost for the packaging line, throughput rate, error rate, capital cost of automation equipment, expected throughput with automation, and payback period. Six data points.
The discipline here is restraint. Your instinct will be to collect everything. Resist it. Every additional data point you collect has a cost: time to gather it, time to verify it, time to analyse it. More data does not mean better decisions. The right data means better decisions. I have seen SME leaders make excellent decisions with five well-chosen data points and terrible decisions with fifty poorly chosen ones. The key question is: If I had this data, would it actually change my decision? If the answer is no, do not collect it.
Step 3: Collect Only That, Decide, and Act
This is where the Decision-First approach diverges most sharply from traditional business intelligence. Instead of building a comprehensive data infrastructure and hoping insights emerge, you collect the specific data you need for the specific decision you are making. You set a decision deadline. You collect the data. You analyse it. You decide. You act. Then you measure the outcome and learn for next time.
The food manufacturer collected the four data points for the supplier decision in one week. The data showed that Supplier B was 12 percent cheaper with comparable quality but had a 15 percent higher delivery delay rate. The decision was clear: switch to Supplier B for non-time-critical ingredients and keep Supplier A for time-critical ones. Total decision time from question to action: two weeks. If they had followed the traditional approach of building dashboards and running comprehensive analyses, it would have taken two months and the cost savings would have been delayed by six weeks. For the automation decision, the payback period analysis showed a 22-month payback at current volumes but an 11-month payback if they hit their growth targets. They decided to automate but only after securing two new contracts that would ensure the higher volume. Decision made, action taken, result measured.
Case Study: The Construction Company in Jakarta
I consulted with a mid-sized construction company in Jakarta that was experiencing thin margins on its projects despite growing revenue. The finance director had created elaborate monthly reports with dozens of charts: revenue by project, cost breakdowns, labour utilisation, equipment usage, and more. The management team would review these reports for two hours every month and then go back to work without making any changes. The data was comprehensive. The impact was zero.
We applied the Decision-First approach. I asked the leadership team: What are the three most important decisions you need to make in the next 90 days? After discussion, they identified: Which of our current project categories should we pursue more aggressively and which should we scale back? Should we buy or lease our heavy equipment? Should we hire a dedicated procurement manager or continue having project managers handle their own procurement? For the first decision, we needed only two data points per project category: average margin and trend direction over the past 12 months. The data was in their existing reports but had never been presented this way. When we extracted it, the picture was stark: residential projects averaged 8 percent margin and were trending down. Infrastructure projects averaged 14 percent margin and were trending up. Commercial projects averaged 11 percent margin and were stable. The decision was obvious: shift business development resources toward infrastructure projects. For the procurement decision, we calculated the total cost of procurement errors and missed early-payment discounts across all projects in the past year. The number was 340 million rupiah. A dedicated procurement manager would cost 180 million rupiah per year. The decision was straightforward.
Within six months of adopting the Decision-First approach, the company’s average project margin had improved from 9 percent to 12.5 percent. Not because they had better data, but because they were using data to make specific decisions rather than just reporting it.
Data does not create value when it sits in dashboards. Data creates value when it changes a decision you were about to make.
Self-Assessment: Is Your Data Actually Driving Decisions?
1. Name the last three business decisions you made that were directly informed by data. If you struggle to name three from the past quarter, your data is not driving decisions.
2. How many reports does your team produce each month that do not result in any action? Each one represents wasted hours.
3. For your most important current decision, can you identify the five or fewer data points that would most inform it? If not, you may be drowning in data.
4. When was the last time data told you something that surprised you and caused you to change course? If data only ever confirms what you already believe, you may be experiencing confirmation bias.
5. What percentage of your analytics budget is spent on collecting and displaying data versus on building the skills to interpret and act on it? Most SMEs over-invest in tools and under-invest in capability.
Key Takeaway
Data-driven leadership for SMEs is not about having the most sophisticated analytics platform or the most comprehensive dashboards. It is about starting with the decisions you need to make, identifying the specific data that would inform those decisions, collecting only that data, and then actually using it to decide and act. The Decision-First approach is simpler, cheaper, and dramatically more effective than the traditional approach of collecting everything and hoping insights emerge. Start with the decision. Work backwards to the data. Decide. Act. Learn.
Ready to Become Truly Data-Driven?
Our Data-Driven Success workshop teaches SME leadership teams how to implement the Decision-First Data approach across their organisation. In a focused, practical session, we work with your real decisions and your real data to build a system that actually drives better outcomes. No jargon. No unnecessary technology. Just better decisions, faster. Visit being-specific.com/contact to learn more.
