Understanding What Data-Driven Decision Making is

Using data to make better decisions

Data is everywhere. It surrounds us, motivates us and ultimately drives our behaviours. 

We’re constantly coming into contact with it. Especially in organisations that deal with the ongoing influx of facts, figures and statistics. And It’s often our job or role as humans to make sense of and contextualise all of these symbols.

However, we now live in an age that is quickly changing that narrative. Where artificial intelligence now fills that role.

From data engineering through to data science, making use of AI is becoming more necessary than ever and it’s our responsibility to know how it works to benefit business, as well as ourselves.

Data-Driven Decision-Making Through The Use Of AI

Trying to make sense of the processes behind AI can be nerve-wrecking and difficult, to say the least. It’s clear that most people can’t seem to fathom its complex nature, or how it works in the context of business.

Daniel Hulme, CEO of Satalia, a company that implements AI innovations into some of the world’s largest organisations, helps us to understand some of the key factors that form the purpose of artificial intelligence in companies, as well as the use of data in and around business operations.

Finally, some clarity.

In a presentation that Hulme conducted, he talks about a concept called Data Driven Decision Making. He breaks it down into 5 key stages: Data, Information, Knowledge, Understanding, and Wisdom.

These points follow a process that offers a guide to making decisions around the operation of any organisation. Especially those that have loads of data. This allows for competitive advantage, more efficient systems and valuable insights into making use of the wealth of data.

Data analysts, data engineers and data scientists all work with these avalanches of information in order to understand, predict and utilise it. This is to extract and implement the best strategies and processes to run a successful organisation.

What Is The Difference Between Data, Information And Knowledge? 

First things first, we need to start right at the beginning, with: Data.

The dictionary definition for data is “facts and statistics collected together for reference or analysis”. At its most basic understanding, it can be considered: stuff, things, numbers, words, names – anything without true meaning or context.

“…it’s not until you give it context, does it become information,” says Hulme. 

With data, we’re able to identify and add context to make use of it. Take the example of a random number; let’s say, 30. The number 30 can be considered a piece of data. But without context, it doesn’t hold any weight. Once we add context, i.e. there are 30 people in an office – then we have information. 

Hulme also provides clarity around the buzzword big data. He describes it as huge amounts of data that can’t be handled by a single computer or device. Some data loads are far too big. Think about how much data is on Facebook, Instagram, or within any large business. Now imagine trying to process all of that on your laptop – it simply can’t happen. 


Information can be considered the various interpretations found in contextualised data. Once you’ contextualise the data, it can be interpreted in different ways and only then does it have meaning. 

The meaning that you extract from information can be used in a variety of ways. Depending on how you choose to interpret or implement it. Think about how you use information to create graphs, statistics and other means of mapping out these different interpretations. 

Making use of it means organising it in a way to help us know things. This means extracting patterns and finding correlations in order to create knowledge.

Using the example of 30 people in an office, we can introduce more information, such as their heights. By looking at the different heights in the office, we’re able to organise that information to find patterns or correlations and make assumptions. Assumptions stem from knowledge.

Knowledge is knowing. When we know information, then it’s of more use to us and opens the way to draw comparisons between that information. This then allows us to describe changes or notice key differences in the information that we have. 

Noticing that there may be similarities or differences in the information will allow us to also make predictions around those insights. And being able to describe and predict certain patterns through knowledge is important to create understanding.


Understanding is different to knowledge in that it creates clarity around why certain patterns exist in the first place.

The correlations that we find through information will give us enough descriptive and predictive insight which allows us to take action and implement changes or processes that can put that knowledge to use. Without understanding, we’re unable to get the best out of that information and create solutions that truly have an impact.

Knowledge alone goes a long way in making decisions that can be beneficial to an organisation, but far too much reliance falls on intuition, guessing games and running with gut-feelings when implementing that knowledge. That’s why it’s so important to create understanding.

Hulme identifies understanding as “interpreting knowledge – creating a narrative, a story around these patterns to explain the world.” 

He also refers to understanding as “interpreted knowledge”, or knowledge that has been interpreted in a way that gives us more accurate insight around why that knowledge has come to be. 

So we have the heights of 30 people in an office. There’s a variety of height data and we can find patterns with differences and similarities in all of those people. Creating understanding around why some are taller, or shorter, than others can help us formulate new facts, predictions or insights into that environment. 

What do we do with that understanding, those insights, predictions and facts? We utilise that understanding to accomplish a particular goal or objective within the organisation.


Wisdom is the utilisation of understanding. Applying what we understand to achieve a particular goal or objective is the final step in the decision-making process. Without application or implementation, there is no real point to understanding.   

Unfortunately for us as humans, we can’t process heaps of data, extract patterns efficiently and make valid decisions based on the information, knowledge and understanding that we receive through these steps.

We are complex in our own right and have biases, moods, as well as internal and external interferences that cloud our judgement. This often leads to poor decision-making. 

And that’s why AI is so crucial to extracting and implementing these vast amounts of data that businesses collect. 

The Role Of AI – The Data Experts

While taking us on a journey to understand Data Driven Decision Making, Hulme shares two definitions around what he believes AI is. 

He defines it in two ways: a weak definition, and a strong definition.

The weak definition is, “getting computers to do things as good, or better, than humans”. And explains that the strong definition is, “goal-directed adaptive behaviour; goal-directed in the sense that we’re trying to achieve an objective… and behaviour is how quickly I can move towards that goal”.

He emphasises the word ‘adaptive’. “If your system is not adapting itself in production, I would argue that it’s not AI”.

When companies (like teraflow.ai) build AI systems for businesses, they ensure that it also follows a similar course of action to that of Hulme’s – where the sequence of Data, Insight, and Action are prioritised to create the most effective results.

Data refers to a stage that ensures all of the data available to an organisation is in one place (e.g. a cloud server), in order to extract information. This is where data engineering comes in. Infrastructures are built in order to assimilate or understand the information so that it can be used in meaningful ways.

Insight is the stage where context is added. Theories, models and systems are extracted from the collected data so that different approaches or strategies can be planned around that information.

Action is the final stage where data scientists can make predictions, as well as prescribe and implement models and strategies to achieve a goal or objective for the company. This is where machine learning and optimisation comes in and the decision making becomes essential.

Our Role As The Data Engineer

The foundation and starting point of this process is what teraflow.ai provides for your business. It’s the fundamental stage where data is obtained, gathered and organised in a way that can be understood and analysed to gain valuable and precious insights.

It’s the data engineer’s responsibility to extract and discover trends through the massive amounts of data that a company collects. Remember ‘big data’? Well, that’s the stuff that these guys have to work with and it’s not an easy task.

With a deep understanding of programming languages, these engineers create algorithms and charts for an organisation to utilise. Usually to create or enhance products, understand competitive influences and make the right decisions around customers or clients. 

Their role is crucial to the data-driven decision-making process and without that first step, it becomes extremely difficult to take action and implement strategies that will work. 

While plenty of industries are still making use of their gut-feeling or calculated guesswork to build their plans upon, this new approach to business through data and AI integration makes the most sense. 

It’s a rapidly-growing technology that has some of the biggest organisations in the world adopting it. And with that, competition will become increasingly difficult as AI reaches more and more businesses.

Start your journey with AI today and bear witness to the value that it brings to your company. 

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