In today’s digital economy, data – and the concept of big data – is revered. Every business chief wants it to better understand their business, stakeholders and assets, and ultimately drive revenue.
But you can have too much, and it’s crucial to recognise data for not only its value, but its cost to operations and environment – big data is a challenge that intersects technology and sustainability.
Rising Investment in Big Data
There are no signs of that challenge slowing down as investment heats up. Worldwide, companies spent US$215 billion on big data and business analytics (BDA) software in 2021 according to IDC, with the analyst firm expecting a 31 per cent increase in data science by 2030. In Asia-Pacific US$42.2 billion went towards BDA in 2023, up 19.6 per cent from the year prior.
Data is a double-edged sword. On one side, it promises enhanced understanding and improved decision-making. On the other, large volumes of data collected can be non-essential, and creates inefficiencies and adds complexity.
There’s the issue of data storage and the complex infrastructure required to support it, as well as the energy needed to power and cool those systems; data centres globally are estimated to consume about three per cent of the world’s total electricity, contributing to two per cent of total greenhouse gas emissions – figures that rival the airline industry. Then there are the expenses to train or hire people to look after it all.
While possessing large volumes of data can offer unprecedented insights and strategic advantages, Australian businesses must evaluate at what cost given the challenges of managing vast data sets, alongside the financial and environmental impacts associated with them.
Complications from IoT Technologies
The proliferation of Internet of Things (IoT) technologies is only making matters more complicated. As higher volumes and new types of internet-connected devices mature from aspirational pilot projects to large-scale deployments, organisations are being flooded with new information from multitudes of sources, much of it non-essential.
If we look at the logistics sector as an example, there are countless cases where transport companies are being bombarded with new data from thousands of IoT tracking devices fixed to their trucks, containers and cargo. While it is crucial to understand where an asset is located, that information doesn’t need to be relayed every few seconds; it just creates noise.
This begs the question: what constitutes essential data? The definition varies greatly across industries, between organisations in the same sectors, and even different departments within the same enterprise.
Analyzing Business Challenges
Fundamentally, understanding essential data requires an analysis of existing or anticipated business challenges, or the short and long-term goals a company is targeting. This process generates a baseline to determine the type of data needed, and how frequently it should be captured – and it’s exactly this type of foundation that’s needed to begin considering how technology and IoT investments can generate returns to the business.
Coles is a use case that demonstrates this approach. The supermarket digitalised its supply chain with IoT devices so it could have full visibility over its Protein Cold Chain by receiving the right information at the right time.
The company’s approach was to fix internet-connected trackers to its smart food bins, with those devices transmitting data only at pre-determined trigger points, including when units aren’t where they should be, when there is a physical impact incident due to mishandling, and when refrigeration goes outside optimal conditions.
It’s a move that helped reduce the cost of the total asset pool required by 25 per cent, tripling the amount of empty bins returned, and subsequently reducing cost and waste.
The benefits of filtering out non-essential data and only seeing what’s really important from the IoT is not just about the immediate impact, but the outcomes it generates downstream.
Data’s Role in Sustainability and ESG Strategies
This is particularly true in context of sustainability, and the ability to accelerate environmental, social and governance (ESG) strategies.
This isn’t just confined to environmental impact – such as reducing trucks on the road in the case of the logistics example, or reducing waste by improving asset recovery. It’s about enabling businesses to fulfil ESG reporting requirements mandated by governments, and increasingly expected by stakeholders and customers; companies need to both act ethically and sustainably, but also demonstrate how these practices are occurring to generate results.
Excessive and poorly managed data is detrimental to achieving this. Inaccuracies in data collection, processing and analysis hinder ESG reporting, leading to difficulties in complying with regulatory pressures, and opening the door to greenwashing.
According to EY, “Stakeholders and investors demand baseline, standardised data to support relevance, objectivity and comparability. However, at present, they are receiving fragmented data from multiple sources, such as company reports, news articles, vendors and rating agencies.
In order to put an end to greenwashing and enable investors to make informed, precise and transparent decisions, ESG data integration must evolve quickly.”
Data used for ESG reporting can’t be inconsistent or questionable; extensive volumes of non-essential data create this risk, prevents the ability to work from a single source of truth, and creates insurmountable complexity during verification.
The key for Australian businesses lies in a more strategic approach to data. This means not merely collecting data for the sake of having it but being selective about what data is retained, how it is stored, and how it is used.
By re-evaluating data practices and prioritising accuracy in critical areas that tie directly to business challenges and objectives, organisations can transform their data from a cumbersome liability into a strategic asset that resolves both technology and sustainability headaches.
