The role of data within business has never been more important, giving management the insights required to make important decisions based on facts, trends and statistics.
The trouble is there’s just so much of it out there, and new technology is generating ever more sets of data that it can be confusing to sift through the noise and pick out the information you need to make those crucial decisions.
Data Insights

From human resources to marketing, data is now at the heart of every business function whether it’s a start up, SME or PLC.

The effective harnessing of data allows us to streamline all operations, whether that’s getting a product made in the first place through to the logistics of getting it to our customers. Crucially, it allows us to spot where things are not working as they should, so that we can fix that problem and boost overall efficiency, productivity and, ultimately, the bottom line.

So, let’s explore some vital forms of data and how they can be applied to help our businesses grow.

First, let’s look at data visualisation. This is in its literal form the graphic representation of the information and data we have collected. We show this through things like charts, graphs and maps. It’s not only a very immediate and accessible way to interpret data, it’s great for sharing with colleagues to demonstrate in a simple and effective manner any trends and patterns.

At the basic level, visualisation of data is effective because as a species we are naturally drawn to colours and images. In an instant we can process a visual, such as a graph, and pick out shapes, colours and sizes.

If you are in a situation where you need to “get people on board” in a hurry, such as when pitching to potential investors, a visualisation of data is a vital tool.

When talking about how data can be used in business we hear a lot about “quantitative” and “qualitative”, but what does that mean? What are the main differences between the two?

Data that we describe as quantitative is basically numbers, quantities and values. Quantitative is used to express data in black or white terms, it either is or isn’t. For instance, we can learn with absolute surety the number of customers who have bought a particular product, or how much money they have spent, or how many times your website has been visited.

This form of data is ideal for studying your business analytics as it clearly and objectively shows where you can make practical changes to your operations in order to capitalise on those patterns of behaviour.

Meanwhile qualitative data is descriptive and less about the hard and fast numbers, making it less measurable than quantitative and, to some, less reliable. Examples of qualitative data are those that could contain opinions, such as those on customer review sites. Even though it’s not as reliable as its quantitative counterpart, qualitative data is still valuable in that it tells us why people are behaving in a certain way.

In recent years AI and machine learning has made its way into practically every business sector on earth, from medicine to manufacturing. While the visualisation, quantitative and qualitative methods all provide a data snapshot of how your business is operating, AI is very much the engine room of that process, on at all times monitoring progress and performance.

AI not only allows you to automate business data processes across your operations, it does so in “real time” with zero chance of human error.

Today machine learning is used primarily to process huge amounts of data in an incredibly short amount of time. What’s also remarkable is that, over time, the AI will “learn” from this data and get even more efficient at capturing it. This is especially useful for processing enormous data sets and translating it into a format that business owners and managers can understand and act upon.

A good example would be within a manufacturing setting where typically your machinery is networked, meaning data about those processes is constantly generated and stored. We could never hope to wade through this much data and wouldn’t be able to extract the useful information even if we could. However, AI and machine learning software can interpret this tsunami of data as it’s generated and spot trends, such as a slowdown in productivity, instantly, allowing you to act on that problem using informed, data-backed insights.

This has just been a very swift overview of some of the types of data that can be captured and applied to your business. I’ve worked in data, analytics and insights for years and have seen time and time again the challenges businesses face in harnessing the data lead to inefficiencies, waste and heavy cost to companies.

Every time I moved to a new organisation I would see these issues repeating, forcing me to effectively start from scratch building the processes I needed to create a system that catered to analytics teams and was easy to use for business decision-makers.

There was a clear and obvious requirement for a tool to address these problems which is why I’ve made the decision to enter the world of entrepreneurship to provide the very tools businesses need to tackle these challenges. I aim to unlock the potential companies miss through lack of analytics integration, workflow transparency and ROI assessment.

I’m excited to share Brijj, a workflow tool for data, analytics and insights teams. We’re busy working behind the scenes to fine-tune the platform, over the coming months I’ll be sharing progress and asking for feedback!

Get in touch if you want to find out more.