Big Data LDN filled Olympia London on 23 and 24 September with more than 400 speakers, 16 theatres and over 200 exhibitors. Walk the floor for ten minutes and you notice something: almost every stand promises the same thing. "AI-powered." "One platform for all your data." "Insights in seconds."
If you run a small business, that is confusing. How do you choose between products that describe themselves in identical words? That question, more than any single announcement, is what this post is about.
What you'll get from this post
A simple way to understand what different data tools actually do, a side-by-side look at the best-known platforms, and five questions you can ask any vendor to cut through the marketing. No technical background needed.
Follow one small business's data
Let's make this concrete. Oak & Ember (a made-up name) is a six-person online homeware shop in London. It sells through Shopify, advertises on Instagram and Facebook through Meta Ads, does its accounts in Xero and keeps everything else in Google Sheets. Like most small businesses, it has five data problems, and each one is solved by a different type of data tool.
Problem 1: "Our numbers are spread across five different exports."
The solution: one place where all of it lives together. Instead of opening five files and copying numbers between them, Oak & Ember loads every export into a single store once, and from then on can ask one question across all of it: "which Instagram campaign brought customers who ordered twice?" Questions are asked in SQL, a simple language for talking to databases, or through an AI assistant that writes the SQL for you. Platforms that do this: MotherDuck is built for small and medium amounts of data, simple to set up and free to start. BigQuery is Google's version, convenient if you already use Google's tools. Snowflake and Databricks are built for large organisations with data teams, and Databricks adds heavy machine learning work on top. Oak & Ember has a few gigabytes of data, so MotherDuck or BigQuery fits; the enterprise options would be overkill.
Problem 2: "Someone spends every Monday morning downloading exports."
The solution: the exports download themselves. A connector logs into Shopify, Xero and Meta Ads on a schedule, pulls the new data and drops it into the store from Problem 1. Monday morning becomes "open the dashboard" instead of "download five files". Nothing is analysed at this stage; the job is only moving data reliably. Platforms that do this: Fivetran offers hundreds of ready-made connectors, including Shopify and Xero, that work without code and has a free plan. Airbyte does the same job as an open-source alternative that technical teams can run themselves. For Oak & Ember, Fivetran's free plan covers it.
Problem 3: "Our checkout broke and we only found out from an angry customer."
The solution: something watching the website around the clock and raising an alarm before a customer does. Monitoring tools check that pages load, payments go through and servers respond, and send an alert the moment something slows down or breaks. Platforms that do this: Datadog is the best-known, built for engineering teams. But Oak & Ember's website runs on Shopify, and Shopify monitors its own servers. For this business, Datadog would be money spent on a problem someone else already handles. If Oak & Ember built its own website or app, it would become relevant.
Problem 4: "I want yesterday's sales and ad spend on one screen every morning."
The solution: a dashboard that reads from the store in Problem 1 and refreshes itself. The owner opens one page and sees sales, ad spend, best-selling products and repeat-customer rate for yesterday, with no spreadsheet work. Dashboard tools don't hold data; they display what the store already has. Platforms that do this: Google Data Studio (formerly Looker Studio) is free and connects to Google Sheets, Analytics and Ads. Power BI suits teams already living in Excel and Microsoft 365. Hex is aimed at analysts who want code and AI in one place, and Looker is Google's enterprise reporting tool. For Oak & Ember, Google Data Studio is enough.
Problem 5: "Shopify says we made £42,000 last month. Xero says £38,500."
The solution: agree once on what each number means and write it down. The gap is usually refunds, VAT or shipping being counted differently in each system. Once "revenue" has one definition, every report uses it and the two numbers match. For a six-person business, this is a single shared page; as a company grows, it becomes software. Platforms that do this: Atlan is a data catalogue that records what each number means, where it comes from and who owns it. DQLabs monitors data quality and flags missing or broken data before it reaches a report. For Oak & Ember, one page defining "revenue", "customer" and "repeat customer" solves most of the problem; Atlan and DQLabs can wait.
This is why names that sound alike on a conference stand are often completely different purchases. Put the best-known ones side by side:
- Datadog solves Problem 3. It watches your website and servers and alerts engineers when something breaks. Priced by servers monitored and log volume. Oak & Ember doesn't need it; a company running its own software does.
- MotherDuck solves Problem 1 for small and medium data. It holds your orders, invoices, ad spend and customers, and lets you ask questions of them. Built on DuckDB, a free open-source engine; charges for storage and computing used. Fits Oak & Ember.
- Snowflake solves the same Problem 1 for large organisations: hundreds of users, terabytes of data, strict governance. Priced on computing time, which is where bills grow fast. Overkill for a six-person shop.
- Databricks also solves Problem 1, but its real purpose is training and running machine learning models on top of the data. Built for teams with data scientists. Not a fit for Oak & Ember today.
- BigQuery is Google's answer to Problem 1. Convenient if the business already lives in Google Ads, Analytics and Sheets. Priced per query, which is cheap at small scale.
- Fivetran solves Problem 2 only. It moves data, stores nothing and analyses nothing. Often mistaken for a database because it sits on every data diagram.
- Power BI, Google Data Studio, Hex solve Problem 4. They draw charts from data that lives elsewhere. None of them is a place to keep your data.
- Atlan, DQLabs solve Problem 5. They document what numbers mean and catch bad data. Valuable once there are several teams; a shared page does the job before that.
Five of these say "AI-powered" on their website. Only the job they do tells you whether you need them.
Five questions to ask before you choose a data product
This is the part to keep. Whether you are at a stand, on a sales call or reading a pricing page, these questions separate products that sound identical.
1. What job does it actually do?
Ask the vendor to describe it in one sentence without using the words "AI", "platform" or "insights". If they can't, be cautious. Then place it in one of the jobs above: storage, moving data, monitoring, dashboards or quality.
2. Is it built for your size?
Ask how much data their typical customer has. If your whole business lives in a few spreadsheets and exports, a tool designed for petabytes will be expensive and overcomplicated. Lighter tools like DuckDB (free) and MotherDuck (with a free starter tier) exist exactly for this.
3. How does the price grow?
Products charge in very different ways: per user, per hour of computing, per server, per gigabyte. Ask what your bill looks like if your data doubles or your team grows. The cheapest starting price is not always the cheapest tool a year later.
4. Can you leave easily?
Ask whether your data is stored in an open format and how you export it. Tools built on open-source foundations, like MotherDuck on DuckDB, make it easier to move later. Being locked in is a cost you only notice when you try to leave.
5. Will it work with what you already use, and can they prove it?
Check it connects to your real systems: Shopify, Xero, Google Sheets, your CRM. Then ask to see a demo using data like yours, not a perfect sample. Useful follow-ups: how much setup sits behind this demo, and where does a person still need to step in?
What to try next
Write down your three most important questions about your business (for example: which marketing actually brings repeat customers?). Then list where the data to answer them lives today. That one page tells you which job you need a tool for, and protects you from paying for tools built for companies a hundred times your size.
At KMCP Solutions, this is how we approach every project: understand the job first, pick tools that fit the business rather than the brochure, and add AI only where it clearly earns its place. If your data lives in five spreadsheets and a few exports today, that is not a problem. It is a starting point.
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