Spot the difference: understanding key tech terms 

Data (and big data), AI, GenAI, NLP, datasets, predictive modelling...the world of data analytics is overflowing with specialist terminology and acronyms.  If you have no experience in the analytics sector – or even if you’re generally tech savvy – it can be difficult to make sense of some of the language and terminology.  Some terms are also commonly confused or mixed up, which can lead to misunderstandings when you’re looking at analytics or AI solutions.

At Datamine, we’ve been deep in the data analytics world for decades, and we’re happy to be tech translators.  We’re here to define some key terms you’ll hear when you’re working on an analytics project or scoping out solutions for your organisation.  We’ll also give you questions to ask your tech team or data expert so you can really understand what’s happening underneath the hood.  

What are the key terms used in digital analytics and analysis?

There’s no way to cover every single term used in data analytics, as thousands of products, technologies and methodologies are on the market.

Instead, we’ve focused on the terms that come up most and those that cause the most confusion with our clients, grouped by area.

Here’s what you need to know:

AI and machine learning

AI is an umbrella term for a range of technologies that use computing to solve problems and perform tasks in a human-like way, analysing vast amounts of data to ‘learn’ and improve over time.  While there are several types and uses for AI technology, ChatGPT and other generative AI tools tend to dominate the conversation.

This can mean that people group GenAI, machine learning and other forms of AI into one – in reality, they’re very different.

Generative AI (GenAI)

GenAI is artificial intelligence that can generate new content rather than simply performing preset tasks.  This type of AI can be used to create incredibly sophisticated articles, emails, videos, static images and more.  GenAI requires huge amounts of data and resources, and is best used for tasks that require creating new, complex or unstructured outputs.

Machine learning

Machine learning is a form of AI in which algorithms and statistical models extract insights from large datasets and use them to improve – or ‘learn’ – over time.  Unlike generative AI, machine learning doesn’t create new content.  Machine learning is better suited to specific tasks and typically requires less data and power compared to GenAI.  It’s used for tasks like image recognition, spam filtering and fraud prevention.

Also see predictive modelling in analytics.

Natural language processing (NLP)

NLP is an AI technology focused specifically on language-related tasks.  It sounds very high-tech, but you’ve probably used a tool with NLP processing already – think Alexa, Siri or a chatbot answering questions on a company’s website.  This type of AI model can analyse and interpret human language in spoken or text form and generate human-like responses.  It’s a way to create engaging, conversational experiences for your customers – without having a person on the end of the line.

Ask your tech team:

• What problem are we trying to solve and is AI the right tool?  
• If it is the right tool, do we have the necessary, high-quality data?
• Do we have the capability, capacity and infrastructure required to support embedding AI into our business?

Data

‘Data’ is a catch-all term for information used to help you understand your business, but it’s not as simple as it once was.  If you’re unclear on the difference between data and big data, we can help.

Data vs big data

Data, at its most basic, is raw information that can be used to measure, define or understand something.  Data doesn’t have to be purely numbers-based – transaction records from your CRM, browsing information from your website and feedback from customers are all examples of data sources.

You may have heard the term ‘big data’ – but what makes data ‘big’?  Big data is defined by its volume, variety and velocity – in other words, a very large collection of information from a range of sources that grows rapidly. Generally, data becomes ‘big data’ when a dataset goes over a million lines, but that’s more of a guideline than a strict definition.

Because it’s so large and complex, big data can’t be managed or analysed with manual methods or traditional tools – you’ll probably need specialist support if you want to use it in your business.

Data cleaning

Data cleaning is the process of checking and reformatting data to make it usable in analytics.  Depending on the type of data, this might involve removing duplicate information, matching and merging data from different sources, and validating data by checking it against third-party sources.  For example, address information may be verified against public address data.  Datamine can do this for you – Data Cleaning is often one of our first steps in a data analytics project.

Dataset

A dataset is information that has been collected, cleaned and organised in a specific way to make it easy to use.  In analytics, datasets are used to train predictive models and AI algorithms or test the impact of a particular strategy.

Data visualisation

Data visualisation is the practice of presenting data visually using graphs, charts, diagrams, heat maps or other formats.  It’s an invaluable tool for presenting outcomes or insights in stakeholder meetings.

Ask your tech team:

• What is our data strategy?
• Do we have the right data and is it reliable?

Analytics

Analytics is the process of sifting through and interpreting data to assess performance or identify trends.  While analytics is used as a general term, there are several approaches including straightforward performance analysis and complex predictive modelling.  Other terms, like demographics and segmentation, are also misunderstood – here’s what you need to know:

Analytics

Analytics is the practice of using statistics, machine learning and AI to sort through large volumes of data, find information, and identify meaningful patterns.  Often called data analytics or digital analytics, it’s used to help businesses and organisations better understand their customers, sales and marketing techniques.  

Predictive analytics

Predictive analytics (also known as predictive modelling) is an advanced analytics method that uses data to predict future trends or the likely outcome of an action.  With this method, data analysts create predictive models to explore different scenarios and outcomes so businesses can make better decisions around pricing, marketing or inventory.  Predictive modelling also falls under machine learning – these processes use your historical data to accurately predict future outcomes, including stock management, staff scheduling and more.

Ask your tech team:

• What analytics do we use within our business?
• Are we supporting and improving the customer experience through analytics?
• Can we use predictive analytics with our current data?  Data Cleaning or integration of siloed data may be needed first

Demographics

Demographic data puts people into groups based on shared characteristics, which can help you understand and target them in your marketing and product development work.  Often, people confuse demographic details with other customer details.  Demographics are measurable characteristics like age, geographic location, sex, income, occupation and family status.  Preferences and shopping habits are not demographics – instead, they fall under customer behaviour.

Both demographic and behavioural information can be gathered from your data and used to create a comprehensive customer profile.  

Insight

An insight is a new piece of information uncovered during the analytics process.  This could include a sales trend, a correlation between customer demographics and sales numbers, or groups of products that are commonly purchased together.    

Ask your tech team:

• How can I surface the insights gathered from this data in my reporting?  Datamine’s Visualisation tools may be valuable here

Segmentation

Segmentation is a method of analytics that sorts a dataset into groups based on shared characteristics.  Customer segmentation, the most commonly used form, groups customers based on demographics or behavioural patterns so marketing teams can create effective targeted strategies.  Segmentation can also be used to group other aspects such as product ranges and store locations.

Storage and access

Cloud or on-premise, architecture and infrastructure, storage and access – there’s a lot of complexity around securing and using your data.  The way you store and manage your data can have a real impact on security and accessibility for your team, so it’s important to get your head around the differences.

The cloud vs on-premise

The cloud is a worldwide network of servers used to store and provide access to data and software applications.  Businesses and individuals use cloud hosting to save and access their data and work tools, including analytics programs.  Anything stored ‘in the cloud’ is actually in a data centre – huge physical storage facilities managed by cloud providers – and can be accessed via an internet connection.

On-premise storage involves storing business data and systems on physical servers housed in your office or work facility.  This approach can give you more control over your IT resources but tends to be more expensive to maintain.  

Ask your tech team:

• What are the security and privacy risks of our current storage solution?
• Is our current storage aligned with our data analytics goals?  Cloud storage could give you more flexibility if you plan to leverage big data or launch a major analytics project
  
  

Architecture

Data architecture is a term that describes how data is collected, stored, managed, and used within your organisation.  Think of it as a blueprint that helps you ensure safe handling and efficient access to data in your business.  

Infrastructure

Infrastructure is the collection of hardware, software, networks and services that allow an organisation to store, manage, access, and analyse data efficiently.  Your data infrastructure may include cloud storage or physical on-premise servers, software subscriptions, apps and networking equipment.

Ask your tech team:

• What will it cost to upgrade and future-proof our data infrastructure?
• What is our current data strategy, and does our infrastructure support it?

Decipher the jargon with Datamine

We’re the first to admit that data and analytics terminology isn’t as accessible as it could be.  Even clients with years of experience face confusion about terms and methodologies.  Misalignment often stems from stakeholders holding different definitions of the same term.  It doesn’t help that the world of data is constantly changing, with new products, solutions and concepts hitting the market all the time.

That’s where Datamine comes in.  As certified data experts with decades in the business, we can help your organisation navigate the complexities of data analytics, from terminology to solution design.  Our team knows how to break down the barriers to understanding and – more importantly – we can turn analytics into meaningful change in your organisation.

Get in touch to talk data, definitions and the best options for your organisation.

Fill in our contact form, and one of our team members will be in touch.