A new report, based on a survey of small and medium-sized enterprises (SMEs) conducted by the Singapore Institute of Technology and the Institute of Singapore Chartered Accountants, has come as an eye-opener.
The report stated that approximately 70% of the 575 SMEs surveyed had not yet adopted data analytics solutions and services, with many being familiar only with spreadsheets and databases, indicating a lack of awareness and understanding of advanced data analytics.
But the positive news was that these SMEs were open to learning about how data analytics solutions and services could benefit them.
While earlier global studies showed that more and more enterprises, including SMBs, were turning to data analytics services, this new research puts a different spin on the trend.
While there can be no doubt that a percentage of organizations around the world today, as compared to say about five years ago, have moved on from conventional methods of analytics to algorithm-based ones, what’s equally true is that many of them have also moved on from the simple collection of (big) data to the analysis of this data.
A few others have even started using commercial machine learning (ML) in data analytics for small businesses.
Yet the new report by the Singapore-based agencies indicates that some small businesses continue to operate traditionally, without data to support their decisions. Some were even intimidated by the very idea.
We at Express Analytics have often said this — data and its analysis are the modern, scientific way to do business and stay ahead of the game.
Data alone does not drive your business. Decisions do. Speak to Our Experts to get a lowdown on how data analytics can help your small business.
In fact, some small businesses that had previously adopted data analytics solutions were now in the next phase of the process — well on their way to transforming their organizations into data-centric ones.
What are the 5 P's of Data Analytics?
The 5 P’s of data analytics are a simple way to think about how data turns into real business value. Here’s a clear, practical breakdown:
Purpose
Everything starts with why. What problem are you trying to solve or decision are you trying to make? Without a clear purpose, analytics becomes reporting for its own sake.
People
Data doesn’t work on its own. You need the right mix of people who understand the business, know the data, and can translate insights into action. This includes analysts, domain experts, and decision-makers.
Process
This is how data flows from source to insight. It covers data collection, cleaning, analysis, validation, and reporting. A defined process keeps results consistent and trustworthy.
Platform
These are the tools and technologies used to store, analyze, and visualize data. Think databases, BI tools, analytics platforms, and automation. The goal is usability, not just sophistication.
Performance
The final and most important P. Are insights actually improving outcomes? Performance measures whether analytics is helping you make better decisions, reduce costs, increase revenue, or improve customer experience.
What are the 5 C's of Data Analytics?
The 5 C’s of data analytics are a simple way to think about how data becomes useful for decision-making. They’re not a strict framework, but they help keep analytics practical and grounded.
Collect
This is about gathering the correct data from the right sources. That could be customer data, sales data, web analytics, or operational data. The key is relevance. More data isn’t always better if it doesn’t answer a fundamental question.
Clean
Raw data is messy. Cleaning means fixing errors, removing duplicates, handling missing values, and ensuring consistent formatting. This step often takes the most time, but it’s critical. Poor data quality leads to poor insights.
Connect
Data usually lives in silos. Connecting data means bringing different sources together to see the whole picture. For example, linking marketing data with sales or customer support data helps you understand cause and effect, not just isolated metrics.
Compute
This is where analysis happens. You apply calculations, models, or statistical methods to find patterns, trends, and relationships. It can be as simple as basic metrics or as advanced as predictive modeling, depending on the problem you’re solving.
Communicate
Insights only matter if people understand them. Communicating means presenting findings clearly through dashboards, reports, or stories that highlight what actions to take. Good analytics always ends with a decision, not just a chart.
What does Data Analytics for Small Businesses Mean?
What do data analytics consulting services and solutions for small businesses mean? Here’s a simple explanation of data-centricity: In the way of doing business in the modern world, data is considered an asset as tangible as a company’s hardware or its headquarters building.
It is at the heart of the enterprise’s operations; in fact, the entire IT and business architecture is built with the understanding that data is a prime and permanent asset.
Data-centricity is the enterprise's shift from an application-centric to a data-centric approach to doing business. In such a data-driven organization, data analysis flows from the top down.
In a data-centric design, however, data analysis flows in the exact opposite direction, feeding into operations.
Business teams should be able to go beyond merely collecting information and monitoring; they need to analyze data in real-time to extract value from it and pass down the analyses to key decision-makers as quickly as possible.
Here are some of the key metrics that can be monitored for analysis:
- Multi-channel traffic, such as website visits and social media interactions.
- Transactions record
- Cart abandonment
- Marketing metrics
- Inventory management
- Customer service
But what is the great advantage to be derived from becoming data-centric?
- First and foremost, a data-centric model gives a massive financial lead to enterprises adopting this approach, starting with savings on infrastructure and a reduction in other recurring costs
- Makes the entire enterprise “data-smart”, and not just a few team leaders
- Can be used to digitally disrupt the market a business is in
- No more data silos within the organization
However, this is only true for companies that strive to be data-centric. What about those, for example, the SMEs, who have not even begun their data analytics journey?
To this lot, we say, you need to embrace change. Businesses today need not only to work smarter but also faster in order to gain from the valuable insights derived from their data. To reiterate, all businesses collect data, and to not analyze this treasure-trove for better business decisioning and to keep your company ahead of the competition means wasting precious resource.
Time is of the essence today for many businesses to translate data-related value into results.
Whether in logistics or retail, with access to analytics, your company can leverage advanced end-to-end delivery, benefiting both the front and back ends of the process.
The First Time
If your enterprise has eventually come around to the view that it does want to be a data-centric one, here are some decisions you need to take first:
Ensure your business goals align with those of your service, solution, or product.
For example, if your business goal is to increase revenue, consider how data will help you achieve it. You probably will have to ask these questions:
- Which of my services, solutions, or products are revenue drivers?
- What data are they collecting or not collecting?
- Who will have access to this data and the analytics?
Understand your data’s baseline metrics and how to measure them
Data quality means different things to different people. All your teams and their members must be aligned to “the single source of truth”.
For teams to leverage operational data effectively, they must rely on a single source of truth, i.e., a consistent data set to inform their decisions.
To do this, they need to establish a baseline data accuracy metric that everyone in the company must follow. It has often been found that enterprises face data-related hurdles, including multiple data sources, limited cross-team collaboration, and low data accuracy.
There are many other questions that you need to answer before embarking on the data analytics journey; we’ve barely listed the two most important ones.
