How can AI help with data analysis in my business?
AI helps with data analysis in three ways: you ask questions about your own figures in plain language, reports prepare themselves and deviations are flagged before anyone notices them. None of the three works when your data is scattered across mailboxes and loose spreadsheets. Below: what does and does not work, what has to be in order first and how you keep personal data out of the analysis.
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AI helps with data analysis by translating plain-language questions into an analysis of your own figures, by preparing reports automatically and by flagging deviations. It does not replace your accounting or your data source; it extracts what is already in there. The first form is the best known: you export your accounts, webshop or CRM and ask which customers ordered less this quarter than last year. The second form is dull and pays the most: fetch the same figures every morning and put them in your mailbox as an overview, so nobody does that by hand on Monday. The third is flagging: an invoice amount far outside the usual, a supplier that suddenly appears twice, a stock level dropping below the minimum. In all three, AI reads and interprets while ordinary software counts and compares. That second layer has to be right, otherwise a wrong answer sounds just as convincing as a good one.
What data analysis can you do with AI without a data analyst?
Without a data analyst you can do three things with AI: ask questions about an export, have a fixed report produced and have exceptions reported. Building a model that predicts or segments does need someone who knows what the outcome is worth. The line runs along whether you can check the result. A revenue overview per month you check yourself in a minute. A forecast of your revenue six months out you cannot, and then you do not know whether the model saw something or made something up. So start with questions whose answer you roughly know already. If that holds, take one step further. And always have the assistant show which rows or columns an answer is based on, so you can trace what it did.
How do I analyse an Excel or CSV file with AI without sharing personal data?
By anonymising the file first and only then letting AI analyse it. Datawonder built a tool for this that reads Excel and CSV files and anonymises them first; only the anonymised file then goes to an AI model in the cloud. The tool is own work, not commissioned. For most analyses you do not need the personal data. Whether customer 4711 ordered more or less is the same question as whether Mrs Jansen did. Replace names, addresses, email addresses and phone numbers with a code and keep the key yourself. Then you can use a cloud model without personal data going to a party you have no data processing agreement with. Never paste a raw customer export into a free chat tool: you do not know where that data ends up or whether it is used for training.
Which three figures do you want to see every week? Describe your bottleneck →
Do I need an expensive BI licence to get insight into my figures?
No. Datawonder built a business intelligence tool in which you drop a CSV file or export and immediately get charts and insights back, without a licence. That tool is own work, not commissioned. For your organisation such a dashboard can be connected to the systems where your figures already live. A BI package is only worth it if you look at it every week and someone maintains the connections. For an office of three to thirty people, a dashboard that refreshes itself every morning from the accounts and the webshop is usually enough. That is a small integration, not a project. The question to answer first is which three figures you want to see every week. If you cannot name them, no dashboard will help.
What has to be in order before AI can do anything with your figures?
Three things: the data sits somewhere software can reach it, there is one version of the truth per figure and someone owns keeping it up to date. If one of the three is missing, your project becomes about unlocking data instead of about AI. This is the silent project killer. If the information sits in a system you cannot access, in a spreadsheet on someone's laptop or only in the head of the longest-serving employee, it takes three times as long. So first pick a question for which the data is already tidy, for example in your accounting package or a webshop with a good API. Clean up duplicate sources before you start: two revenue lists that are almost equal give two answers and the model picks one without saying so.
Where does AI data analysis go wrong?
It usually goes wrong at three points: the model gets the wrong or incomplete data, the answer sounds certain while it is made up and nobody tests the outcome against something you already know. A language model is built to answer fluently, not to say it does not know. Ask for an average over a column that is half empty and you get a number. That is why every analysis should show two things: which data was used and which calculation was performed. Record thirty questions whose answers you know and run every change through them again. And instruct the system explicitly to say when something is not in the data; an assistant that never says I do not know is making things up.
What does it cost to look at your figures with AI?
A small tool or integration, such as a dashboard that refreshes itself from your accounts, starts around €2,000. A complete application with several sources and an assistant on your figures sits at the top of the custom software range up to €25,000. After the discovery call you get a fixed price. If you first want to know whether it is worth it, an AI audit is the cheapest start: I visit and you get a ranking of processes with what each costs and saves. If you then build with me, that amount is deducted from the first invoice. On your side, count the hours that now go into compiling overviews by hand; that number decides whether an integration pays for itself.
The short version
Three forms: asking questions, automatic reports, flagging deviations. First data in one place, one version, one owner. Anonymise before a cloud model looks in. Have every answer show which data and which calculation were used and test against thirty questions whose answers you know.
Which three figures do you want to see every week?
Describe your bottleneck →