Data & Artificial Intelligence Explained
Helping you understand how data analytics and artificial intelligence can revolutionise your business.
Data Explained
Why: The “why” you should look to do more with your data is easy. It will help you make better decisions. And if you make better decisions, your performance improves.
How: The “how” you do this is often made to sound very complicated. But the theory is simple, even if you may need our help with the execution.
With the power of forward-looking insight, use your data to predict what’s going to happen next
Data Science
Analyse your data so you understand what’s happening, why it’s happening, and can predict what’s going to happen next. Use these insights to inform your decisions and intervene effectively.
Data Analysis
Build dashboards that show all the KPIs and metrics that matter to you. Filter by time, location, product, employee, or whatever else might tickl your fancy.
Data
Visualisation
Connect everything together by pulling the data from all your different systems into one place. This is known as the “single source of truth”: one place to get the answers to all your questions about your business.
Data Integration
Data Quality
Make sure the data in your systems is accurate, complete, and in the right shape. Otherwise, sh*t in means sh*t out.
Know what you want to achieve, and how you can do it with clarity of purpose.
Data Strategy
But Is It All Worth It?
If you’re a company that doesn’t have a lot of data, or you’re not making decisions often, then honestly, probably not. Your money can likely be better spent elsewhere.
However, if you’re a company that has a lot of data, and you’re making decisions frequently, almost certainly. In our experience, even marginal improvements to your decision- making will compound over time, adding up to a meaningful competitive edge.
Artificial Intelligence Explained
AI Without The Noise — Practical Data Automation and AI for Leadership Teams
As we're sure you've noticed, everyone is talking about AI, Including a lot of people who definitely aren’t qualified to be talking about AI.
When people talk about AI, they are usually referring to one of three things:
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automation,
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machine learning,
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or generative AI (ChatGPT, Gemini, Claude, etc).
Understanding what each of these involves – and what they can and can’t be used for – is essential to understanding if, and how, you can use AI effectively within your business.
Automation Explained
1
Is it Actually AI?
In a word — no.
Automation follows predefined rules to carry out repetitive tasks. It does exactly what it is told, the same way, every time.
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What Does it Mean?
Using rules, workflows or triggers (if X happens, then do Y) to get systems to do things automatically, without any manual intervention.
3
Where Can I Use it?
Anywhere in your business where there are repetitive and time-intensive tasks that involve moving data between systems, checking that data has been entered correctly, or generating standard reports.
4
What Are its limitations?
Automation is incredibly useful, but because it relies on a set of predefined rules, it’s only as good as the rules you give it. It won’t understand context, learn, or handle cases the rules don’t cater for.
Machine Learning Explained
1. Is it Actually AI?
Yes, absolutely. Before generative AI, if someone mentioned AI in a commercial context, they almost always meant machine learning.
2. What Does it Mean?
Using historical data to find patterns and make predictions. Instead of using hard-coded rules (automation), machine learning involves training a model on past data so it can predict what’s likely to happen next.
3. Where Can I use it?
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Demand and sales forecasting
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Customer lifetime value (LTV) models
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Scoring and ranking (leads, campaigns, customers, etc)
4. What are its Limitations?
The quality of the predictions made by a machine learning model are dependent on selecting the right model and the reliability of the historical data it’s trained on.
Is it Actually AI?
Yes — and everyone is very excited about it.
What Does it Mean?
Generative AI – as the name suggests – generates new content. It works by learning patterns from massive datasets and predicting what comes next. You can think of it as autocorrect on steroids.
Where Can I Use it?
Anywhere where general intelligence is sufficient to complete the task. The potential use cases for generative AI are therefore incredibly diverse. If you want examples of specific business problems we’ve solved using AI, take a look at our AI Guide for Business Leaders.
What are its Limitations?
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If specialist knowledge is required, it’s unlikely to be suitable.
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AI “hallucinates”; it makes stuff up.
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It’s a language model, making it less suited to maths and logic tasks.
Generative AI Explained
Our AI Guide
Anywhere where general intelligence is sufficient to complete the task. The potential use cases for generative AI are therefore incredibly diverse. If you want examples of specific business problems we’ve solved using AI, take a look at our AI Guide for Business Leaders.
We’ve spent the last three years experimenting with AI models, and we’ve distilled our learnings into this guide.
We explain the full AI spectrum (from basic to advanced), give examples of specific business problems we’ve solved using AI (and you can too), and offer practical step-by-step checklists, frameworks and blueprints so you can start using AI in a meaningful way throughout your business.