Data is Power

Data Strategy in the Waste and Recycling Sector

Data is the most under-utilised resource we have as businesses. We are creating more of it than ever before, with apps, systems and telematics. But volume does not mean value, especially when the quality is poor. Data is the fuel of the fourth industrial revolution; like any fuel, the power is unleashed when it is supplied to the right engine at the right moment.

The importance of data within the waste and recycling sector is world leading, the transition to a circular economy relies on it, and many sectors know the strategic value of waste data. Recent advancements have been around visual data, vision analysis for material segregation, contamination, sorting line technology and general operational health and safety analysis. Whilst this technology is being driven forward, the back office is being left behind: Customers and sales, competitors and the market, logistics and the supply chain. These are data rich areas with hidden trends and insights, left to be explored.

If a customer has missed collections, service disputes or a lower frequency of work ordered, this can be lost as isolated operational data. When connected and visible, this is early warning signs for customer churn, meaning there’s time to act and rectify at a customer level rather than seeing overall revenue figures in a sales report.

Having data available from a weighbridge, telematics and ERP system should combine into overall operational truth of a business. This leads to reporting profitability by Customer, Contract, Supplier, Route, Material – any possible variable. But how do we reach this?

Building Outcome-Based Data Frameworks

Too often, we start with “What can we show” instead of “What can we achieve”. We ask “What reports do we do already” instead of “What insights would be valuable to this business unit”.

To build a framework, we can instead work backwards from the end result of powerful information driving business objectives and trace it back to the starting point: the data.

We’ve experienced this challenge at Tegos. There’s data that is valuable to some companies but irrelevant to others. Flexibility should be part of any framework, in order to balance between data quality, volume and performance – whilst ensuring insights remain meaningful and provide value.

Modernising Analytics with Microsoft Fabric

The technical aspects of analytics have historically been the barrier to entry, Power BI has been an industry standard, but under the hood has either been done incorrectly, expensively or without insight.
Modelling data to meet functionality is part of the process, data coming in from ERP, other systems, plant machinery, logistics all needs to be aligned when used within reporting and turned into a valuable resource. This is the OneLake approach as part of Fabric, where everything flows together in a unified location, that isn’t just a big spreadsheet and reflects reality live as it happens

Source: https://learn.microsoft.com/en-us/fabric/onelake/onelake-overview

Raw data ingested in this way needs to transform: Field and values required or not, translated into a common language, compressed by dates and reporting tags – even turning from conventional row based to column-based formats like Delta Parquet for Fabric.

AI in Analytics: The Dual Role

AI becomes an accelerator for accessible analytics: It can identify bad data at the front end, trends and insights not even considered, as well as backing decision-making with data reasoning.
Two Gartner predictions show the duality of AI development, and the importance of staying at the sharp end:
By 2027, organisations incorporating intent detection and data loss prevention will realise a 33% reduction in risk. (e.g. catching fraud)

By 2027, AI agents will further automate credential stealing and compromise authentication channels, reducing time to exploit account exposures by half the time. (e.g. creating fraud)

This shows the fast-moving direction the world is taking, it will help identify fraud, intent and even data loss potential, but can also be used to apply the very same risks. We must ensure our data landscape is robust enough to defend against technology, competitors and the market – whilst using the same technology to compete.

This is a sector rich in data, the challenges have changed from entrance to frameworks and quality. Businesses that can align data strategy to their own strategy and take advantage of modern platforms and technology – even if it’s just awareness, will respond more effectively and have a better chance of extracting power and value from their data.