Self-hosted analytics: the Plausible and Umami architecture we ship by default.
Why we stopped recommending Google Analytics in 2024, the two-hour migration we now standardise on, and the privacy posture it gives every client by default.
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Up to 90 percent of the data a business holds is never looked at, yet analytics projects routinely pay for themselves within a year. Here is what a data analysis engagement actually involves, and why Kenyan SMEs start closer to the finish line than they think.
By Hanova Editorial
Every business in Nairobi is already running a data operation, whether it admits it or not. The till prints a record of every sale. The M-PESA statement logs every payment, timestamped to the second. WhatsApp holds every customer question you have ever been asked. The delivery book knows which routes run late. The problem is almost never a shortage of data. The problem is that nobody is looking at it.
Researchers have a name for this: dark data. IBM defines it as the information a company collects in the normal course of business and then never uses for anything, and the estimates are brutal. Somewhere between 80 and 90 percent of the data a typical organisation holds is dark. IDC estimates that 90 percent of unstructured data, the messages, documents, photos and free-text notes that make up most of what a business generates, is never analysed at all. Gartner-adjacent studies put the share of data most companies actually analyse at around one percent.
Those numbers come from studies of large enterprises, but the pattern is worse in a small business, not better, because nothing is instrumented on purpose. The sales history sits inside a till nobody exports. The payment record sits in a statement PDF. Customer complaints sit in a personal phone. Each system is a silo, and no silo talks to another, so the questions that matter most stay unanswered: which product actually made money last month, which customers came back, which day of the week deserves more stock.
The case for fixing this is not sentimental. Industry studies year after year find the same shape: analytics projects routinely pay for themselves within the first year, with returns in the low hundreds of percent over three. Firms that run on evidence rather than recollection report revenue gains in the 15 to 20 percent range from better pricing, stock and marketing decisions, and meaningful reductions in operating cost from cutting what the numbers show is not working. Organisations that make decisions from data are also simply faster, several times faster by most measures, because a question that used to trigger a week of arguing becomes a thirty-second glance at a dashboard.
That speed is the part we see most clearly in our own engagements. The first dashboard rarely tells a client something nobody suspected. It confirms one suspicion, kills another, and ends three standing arguments. That alone changes how the next quarter is planned.
Kenya is an unusually good place to be a small business with data ambitions, because the payment layer is already digital. With mobile money penetration at 98 percent, the average Kenyan SME has a machine-readable record of nearly every shilling in and out, which is a starting position European corner shops would envy. The raw material exists. What is missing is the pipeline: getting the till, the statements, the spreadsheet and the WhatsApp orders into one place where they can be compared.
That is also why the excuses travel badly here. A business that takes M-PESA does not need a data collection project. It needs a data consolidation one, and consolidation is cheaper, faster and much less risky.
The phrase sounds grander than the work. A good engagement is not "big data", it does not start with hiring a data scientist, and it should not start with buying software. Ours follow the same four steps every time.
We wrote before about the self-hosted analytics stack we ship by default for website data. The same philosophy applies to business data: own the pipeline, keep it simple, and measure only what you will act on.
You do not need a strategy document. You need honest answers to three questions. What decision do you repeat every week? Where does the data for that decision currently live? And how long would it take you, right now, to say which product made you the most money last month? If the last answer is more than a minute, the gap between you and the answer is the project, and it is usually a small one.
Start with one decision, one consolidated dataset and one dashboard, and let the results argue for the rest. If you want help building that first pipeline, our data analytics team does exactly this, and the first conversation about what your numbers could tell you costs nothing.
Why we stopped recommending Google Analytics in 2024, the two-hour migration we now standardise on, and the privacy posture it gives every client by default.