Most companies today already have plenty of data. Sales, purchasing, inventory, customers and finance are usually covered by an ERP or several other business systems. Yet when management needs an answer to a fairly simple question, someone still ends up exporting data to Excel.
There is nothing particularly wrong with that. Excel is flexible, familiar and often the fastest way to get something done. The problem starts when those exports become part of the regular way a company operates.
A sales manager has one spreadsheet, finance maintains another, purchasing has its own calculations and management receives a report that someone manually prepares every week or month. Over time, these files become increasingly important, even though most of the information they contain already exists somewhere in the company’s systems.
So the issue is often not that the company doesn’t have the right data. It is that getting from that data to a useful answer still requires too much manual work.
An ERP usually contains a large part of what a company needs, but rarely everything.
Customer information may be in a CRM, warehouse movements in a WMS, online orders on an e-commerce platform and forecasts or budgets in Excel. Even within the ERP itself, information that belongs to different parts of the business isn’t always presented in a way that makes analysis straightforward.
This becomes noticeable when someone asks a question that requires looking at several things at once.
Which customers generate the most revenue is relatively easy to answer. Which customers are actually the most profitable, after considering discounts, product margins and other costs, may be much harder.
The same applies to inventory. Knowing how much stock is currently available is one thing. Understanding which products are moving too slowly, which ones are at risk of running out and how current sales should influence purchasing is something else.
In many companies, people solve these questions by exporting the necessary data and putting it together manually. It works, but every new question creates another calculation, another spreadsheet or another version of an existing report.
When we see companies relying heavily on Excel, the obvious conclusion is that they should stop using it. In practice, that is rarely necessary.
Excel is useful and there are plenty of situations where it is the right tool. What becomes problematic is using it to manually connect systems that could be connected automatically.
If every Monday someone exports sales from the ERP, combines them with another file, cleans several columns, updates formulas and sends the result to management, the problem isn’t the spreadsheet itself. The problem is that the same process needs to be repeated every Monday.
The same applies when several departments maintain their own versions of the same numbers. Sales may calculate revenue one way, finance another, while management has a third report. None of them necessarily has to be wrong, but people eventually spend more time explaining why the numbers differ than discussing what those numbers are telling them.
As the company grows, these small manual processes tend to grow with it. They become more complicated, more dependent on particular employees and harder to change.
Improving this situation doesn’t necessarily mean replacing the ERP or introducing a large new system.
Often the first step is simply connecting the systems and data the company already uses.
Data from the ERP, CRM, warehouse or other relevant sources can be brought into a common data model and used for reporting. Instead of preparing the same report manually every week, the underlying data can refresh automatically.
This also means that everyone can work with the same definition of revenue, margin, inventory turnover or any other important KPI.
Once that foundation exists, reporting can go a step further. Not every piece of information needs to wait for someone to open a dashboard.
If inventory falls below a certain level, the responsible person can receive an alert. If the margin of an important product changes significantly, someone can be notified. If a customer’s purchasing pattern suddenly changes, sales can see it without manually searching through reports.
At that point, data stops being something that is prepared periodically and starts becoming part of everyday operations.
One mistake companies often make is starting with the solution.
They decide that they need Power BI, automation, a new data platform or, increasingly, AI. Only afterwards do they start looking for something to do with it.
It usually makes more sense to start with a much simpler question: what is unnecessarily difficult to know or do today?
Maybe management waits several days for the monthly report. Perhaps purchasing doesn’t have a clear view of what needs to be reordered. Sales and finance might regularly disagree about certain figures. Someone may be spending hours every week combining data from different systems.
Those are good starting points because they are concrete.
Once the problem is clear, it is relatively easy to trace where the required information comes from, which systems are involved and which parts of the process are currently manual. Only then does it make sense to decide whether the answer is better reporting, system integration, automation or something else.
Over the years, companies naturally accumulate systems. A new ERP is introduced, another department starts using a CRM, an e-commerce platform is added and various spreadsheets fill the gaps between them.
Individually, all of those tools may work perfectly well. The difficulty is usually in what happens between them.
Before introducing another tool, it is worth looking at those gaps. Which information is still being moved manually? Which reports depend on exports? Where is the same data entered more than once? Which questions take much longer to answer than they should?
Fixing those parts is rarely as exciting as introducing the latest technology, but it creates the foundation that makes everything else more useful.
Once data moves reliably between systems and people can trust the information they are looking at, dashboards become more valuable, automation becomes easier to implement and AI has much better data to work with.
And in many cases, the company already has most of what it needs. The missing part is simply connecting it properly.
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