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VentaroAI Readiness Check

Know Your Numbers · Data Analysis

AI data analysis that turns scattered numbers into answers

We join up the data spread across your spreadsheets and systems, give you reporting your team actually reads, and let you ask questions of it in plain English.

Take the AI Readiness CheckSee examples

Example

Sales, ads, stock and finance data
Cleaned and joined in one place
One dashboard, refreshed daily
Ask: which products lost margin this month?

AI + Experts + Your Data

Any one of these on its own falls short. Together they give you the speed of AI with answers you can trust.

AI

Does the heavy lifting

Joins sources, spots patterns and answers questions in plain English.

EXPERT

Checks it's right

Knows which numbers matter and what to do about them.

YOUR DATA

Keeps it grounded

Your real sales, costs and customers, not industry averages.

HOW IT WORKS

How it works, step by step

Here is a typical job, from the moment it arrives to the moment it is done. Watch it move through.

  1. 01Sales, ads, stock and finance data
  2. 02Cleaned and joined in one place
  3. 03One dashboard, refreshed daily
  4. 04Ask: which products lost margin this month?

Where it pays off

Real examples of where Data Analysis pays off. Filter by the part of the business you care about.

OPERATIONS

One view of the business

Sales, marketing, operations and finance on one page instead of five spreadsheets.

All sectors
OPERATIONS

Ask your data in plain English

Type a question, get the answer and the chart, without building a report first.

All sectors
SALES

Margin and pricing checks

See which products, jobs or clients actually make money once every cost is in.

EcommerceTrades and B2B
SALES

Pipeline and forecasting

Know what's coming in next month, and which deals need a push.

Professional servicesTrades and B2B
MARKETING

Marketing spend that pays

Ads, SEO and email tied to real revenue, not just clicks.

Ecommerce
OPERATIONS

Early warnings

Alerts when stock, cash, response times or reviews move the wrong way.

All sectors

Start small. Prove it works. Then scale.

No big transformation projects. We find the one thing worth doing first, prove it with a pilot, then keep building month by month.

01 · 3 MINUTES

AI Readiness Check

Seven questions and a straight answer on whether AI fits your business yet.

02 · 1 DAY

1 Day Business Analysis

A day with your team to find the quickest wins. Free for a great fit.

03 · 2 TO 4 WEEKS

Pilot Build

One workflow built, tested and handed over, so you see it working first.

04 · MONTHLY

Monthly Support

Monitoring, training and the next build. Leave at the end of any month.

IN DEPTH

The detail, in 6 short parts

01 / 06

What Is AI Data Analysis?

AI data analysis means using artificial intelligence to help collect, clean, join and explain your business data. Instead of someone spending a morning copying figures between spreadsheets, AI tools do the heavy lifting and a person checks what matters.

Put simply, AI data analytics refers to three things working together. Machine learning algorithms spot patterns and trends in raw data and individual data points. Natural language processing lets you ask a question in plain English and get an answer back. Automated reporting keeps your interactive dashboards up to date without anyone pressing refresh.

For a small or medium business, the point of AI for data analysis isn't fancy charts. It's getting data insights and data driven decisions faster: which products make money, which customers are slipping away and where the cash is going. AI plus a specialist plus your own data gets you there. AI on its own guesses. A specialist on their own is slow. Your data on its own just sits there.

How AI analytics works, step by step

First comes data collection: pulling business data from the systems you already use, such as your accounting software, CRM, ecommerce platform, ad accounts and the odd Excel file. Next is data preparation and data cleaning: fixing missing values, matching customer names that are spelt three ways and removing duplicates. Then the AI models get to work on the prepared data, finding patterns, flagging anomalies and building predictive models where they help. Finally, the results are shown in a dashboard or answered in a chat, with links back to the underlying data so you can check them.

Data analysis vs data analytics

People use the two terms loosely. Data analysis usually means answering a specific question, such as why margin dropped last month. Data analytics is the wider set of tools and habits that let you keep answering questions over time. We help with both: the first answer, and the setup that keeps the answers coming.

02 / 06

Natural Language Querying: Ask Your Data in Plain English

Natural language querying is the part most business users notice first. You type a question like "which products lost margin this month?" and the AI data analyst writes the query, runs it and shows the answer with a chart. Nobody needs to write SQL queries or build a report first.

It works best when the data model underneath is set up properly. If sales, costs and stock are joined correctly, the AI can answer questions about complex data reliably. If they aren't, it will give confident answers to the wrong question. That's why we spend time on the model before we switch on the chat. You don't need complex data models or data science workflows built for a large data team, just a clean, well named model that fits your business.

  • Ask about sales, margin, stock, marketing spend or customer feedback without waiting for a report.
  • Explore data from your phone or laptop, then save the useful answers as dashboard pages.
  • Keep the definitions agreed, so "revenue" means the same thing to everyone.
  • See the numbers behind every answer, so you can check them before acting.
03 / 06

AI Data Analysis Tools for SMEs

There are a lot of AI data analysis tools, and most SMEs don't need a separate BI tool or a data warehouse on day one. We usually start with what your team already opens.

Power BI and the Microsoft ecosystem

If you run on Microsoft 365, Power BI with Copilot is often the natural choice. It connects to Excel, SharePoint and many common business systems, and its AI features can summarise a report page or answer questions about it. Copilot even creates report pages from a short description, which helps business teams get started quickly.

Google Sheets and Looker Studio

If you're on Google Workspace, Google Sheets with Gemini and Looker Studio dashboards can go a long way for small data volumes. They're familiar, low cost and easy to share.

ChatGPT, Claude and other AI assistants

Business plans of ChatGPT, Claude, Copilot and Gemini can read an Excel file or CSV and other common file formats, visualise data as charts and explain what they find. They're good for one off questions. For regular reporting, we connect these AI systems to a proper data source so the answers are repeatable and fit into your analytics workflows.

When you need more

As data volumes grow, or you want real time data analysis across large datasets and multiple data sources, a small data warehouse and embedded analytics inside your own tools or Ventaro Hub may make sense. We'll tell you if and when you reach that point, rather than selling it up front.

04 / 06

Data Analysis for Small Business: Where It Pays Off

Data analysis for small business is about a handful of questions asked every week. Here's where AI powered analytics tends to help first.

  • Margin and pricing: see true profit by product, job or client once every cost is in.
  • Sales pipeline: predictive analytics to spot which deals are likely to close and which need a push.
  • Forecasting demand: use past sales and seasonality to plan stock and staffing.
  • Marketing spend: tie ads, SEO and email to real revenue rather than clicks.
  • Customer feedback: sentiment analysis on reviews, surveys and support emails, including unstructured data like free text.
  • Early warnings: anomaly detection that alerts you when stock, cash or response times move the wrong way.
  • Operational data: job times, delivery times and utilisation in one place.
05 / 06

Choosing a Business Intelligence Consultancy

A business intelligence consultancy should make your numbers easier to use, not harder. Before you pick one, ask a few plain questions.

  • Will they work with the analytics tools you already have, or push a new analytics vendor?
  • Who owns the dashboards, data model and logins at the end? It should be you.
  • How do they handle data quality and show you the gaps?
  • Can your team change a report without calling them?
  • How do they keep sensitive data safe, and where is it stored?
  • What does ongoing support cost? We'd put this as from £750 a month until it's agreed.

What a good engagement looks like

It starts small. One dashboard or one question answered well, built on clean, joined data, with your team trained to use it. Then the next question, and the next. Over time you build a single view of the business without a big upfront project or data science teams on the payroll.

06 / 06

Data Quality, Privacy and Human Oversight

AI is only as good as the data you give it. Poor data quality leads to confident but wrong answers, so we check sources, fix gaps and agree definitions before anyone relies on the output.

On privacy, your own data stays in accounts you control. We use business plans of AI tools that don't train on your data, limit who can see sensitive data, and keep a record of what each AI system can access. That keeps you on the right side of UK GDPR.

And people stay in charge. AI can identify patterns and generate insights at speed, but a person who knows your business decides what they mean and what to do next. That's how you get actionable answers you can trust, with less manual effort.

FAQ

Common questions

Does our data need to be clean first?

No. Cleaning and joining it is part of the work, and we'll show you where the gaps are.

Which tools do you report in?

Usually Looker Studio, Power BI or Google Sheets, whichever your team already opens. We can also report inside Ventaro Hub.

Is our data shared with AI providers?

We set things up so your data stays in accounts you control, and we use business plans that don't train on your data. The details are in our AI use and data statement.

How often does it update?

Daily for most sources, and more often where the system allows it.

Can ChatGPT do data analysis?

Yes, for many tasks. Business plans of ChatGPT can read a spreadsheet, run calculations and draw charts. It works best for one off questions. For regular reporting, connect it to a clean, joined data source so the answers are consistent.

Will AI replace data analysts?

Not for SMEs, in our view. AI takes on the repetitive parts like cleaning, joining and first drafts of charts. You still need someone who understands the business to ask the right questions and check the answers.

What do business intelligence consultants do?

They help you collect and join your data, build reports and dashboards, and set up the habits that turn numbers into decisions. A good one leaves you owning everything and able to run it yourselves.

What are the main types of data analysis?

A common split is descriptive (what happened), diagnostic (why it happened), predictive (what's likely next) and prescriptive (what to do about it). Most SMEs start with the first two and add prediction once the data is in good shape.

Which AI is used for data analysis?

Common choices are Copilot in Excel and Power BI, Gemini in Google Sheets, and ChatGPT or Claude for files and quick questions. The right one is usually whichever fits the tools your team already uses.

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