By Harry
30 July 2025
It’s fairly common knowledge that data is in abundance; In fact humanity produces 2.5 quintillion bytes of data every day. In theory with such a mass of data, computing power, software and even AI available it should be easy for organisations to extract actionable insights and generate value from data for example; how customers are behaving, why you’re facing increasing rates of churn or identifying new opportunities. In practice, leveraging data to create insights, value and ultimately sustainability is challenging; qualifying, collecting, analysing and visualising data is intensive and costly if not done correctly. In this article we’re going to take you through why data-driven insights are vital, the challenges you may face and how you can overcome them to transform your organisation with data driven insights.
In 2008 the world was in economic turmoil, the majority of the world was in a recession. Hundreds of thousands of people lost jobs, companies were fighting to survive and rapidly reducing overheads. The international coffee chain, Starbucks was faced with the decision to close hundreds of stores, going forward they decided to take a data driven approach to decision making which would turn their fortune in their favour. They partnered with a location analytics company who use GIS data to help Starbucks pinpoint better locations using traffic and demographic insights. With 90 million transactions a week Starbucks diversified their data streams and developed the Starbucks app which allowed the coffee chain to understand how users are behaving. Do users order differently on workdays to weekends? Are they more likely to order food with a hot drink or cold drink?. Starbucks invested in data services, bespoke tools and the resources needed to change their mindset to making decisions and it has paid dividends.
It is not just internal decision making that benefits from data insights but your users. Embedding systems into your products so they can make better decisions can increase revenue, customer satisfaction and overall reduce frictions. Amazon uses data to decide which product they should recommend to you based on your prior purchases and patterns in search behaviour. This system uses vast amounts of data and machine learning to drive what is commonly known as a recommendation engine. 35% of Amazon’s consumer purchases can be traced back to the recommendation engine according to a study by McKinsey.
They’re are a myriad of examples that evidence how important it is, to not just collect data, but making sure tools, products and services are in place, that make the data useful and understandable to your organisation internally and externally.
Creating an effective strategy to move your organisation forward often proves difficult however the more arduous and vital step is making sure the whole team is aligned with the strategy and vision. Data is logical and can provide a script that helps your team to understand why a certain direction has been decided, where they fit in and how their actions can make an impact.
A key challenge is knowing what data to collect and how to collect it. At Axamattic we use our Data Value Creation Playbook to work backwards from the core business objectives through to the necessary data collection required to make an impact. Reverse engineering from an objective to the data source will make sure your organisation is flexible and agile as and when strategies change. An example for a logistics company may be that they want better overall driving from their drivers. We can break this down like so;
The goal is better driving, the data source is from vehicles & driving processes. Data collection is happening through onboard sensors and telematics, the output data is vehicle condition and driving data and through analysing the data we can understand driving patterns. We can then frame the data so it feels relevant to drivers so we’ll show information on fuel consumption, driving behaviour, consumables etc. Finally we need to visualise the data which we will do through an onboard display and finally this will help the organisation achieve the goal of better driving from their drivers.
Starting with objectives and working backwards will allow you to clearly understand what data to collect and where to source it. This approach enables flexible execution of existing objectives however, like most organisations ‘moments of magic’ that make huge impact can be unexpected, irrational and defy conventional logic, data can still have a valuable role in this which we will be covering in an article coming soon so make sure to follow our socials.
Part 2 of this article will be coming soon where I’ll cover how to take action and make the data useful.
See you soon :)