When it comes to sustainability, Big Data is proving to be a big opportunity — businesses are now able to collect, store and process mind-boggling amounts of data. IBM estimates that 2.5 quintillion bytes of data are created every day, meaning that 90% of the data in the world was created over the last two years. Analysing, evaluating and understanding this level of information is providing insights that are transforming the way business is done.
When it comes to environmental issues, for most businesses the overwhelming majority of their impacts happen outside of their direct control. The need to work outside operational boundaries has made it difficult for organisations to focus on sustainability only within their own businesses, and in any case many of the most straightforward measures, such as simple energy efficiency with immediate cost savings, have already been implemented.
If a business wants to manage its real environmental impact, it first needs to measure it. Big Data has been the key to helping companies unlock an understanding of their value chains. This includes understanding the footprints of the supply chain, employee travel, customer use of products, waste management, investments and the myriad other activities that occur upstream and downstream from an organisation.
For example, UK-based communications group BT looked at the entire carbon footprint of its business, finding that emissions outside its own direct control counted for 92% of the total. And 64% of that impact was just in BT’s upstream supply chain. When that supply chain involves 17,000 suppliers around the world and products and services worth £9.7 billion, this inevitably leads to data complexity. But by understanding this, BT was able to highlight carbon hotspot areas, revealing business opportunities for reducing costs and carbon.
The supply chain is often the best place to start in using this data to realise savings. Taylor Wimpey is working on an energy and carbon strategy across its supply chain that is estimated to reduce annual material costs by £29 million by 2020. And Whitbread is aiming for a 10% carbon reduction across supply chain activities by 2017.
But BT also was able to use its understanding to set ambitious targets to do more good than harm as a business (a concept known as Net Positive). One of these targets is to help its customers reduce their carbon emissions by at least three times the end-to-end carbon impact of its own business, through carbon savings enabled by its products and services. And the company recently worked with the Carbon Trust and the Climate Group to develop a 3:1 methodology to measure this.
So where should companies start when it comes to taking advantage of the sustainability opportunities in Big Data?
Getting to grips with the value chain
Trying to get a meaningful figure for end-to-end environmental impact produces enormous data challenges and issues with consistency and comparability in business reporting. This is a nascent area and one where standards are only now starting to be set.
One of the best places to start is with calculating carbon, where there is now an internationally recognised methodology for the measurement and reporting of indirect (or Scope 3) emissions. In 2010 the Value Chain (Scope 3) Accounting and Reporting Standard was launched. The Carbon Trust also has been working with the World Resources Institute to create a Scope 3 business guidance framework, covering the 15 categories of possible Scope 3 emissions.
But when trying to get to grips with any sort of Big Data, there are four major challenges:
1. What data to collect?
When trying to measure something enormous and amorphous it is difficult to know where to start and which data sets will be most valuable, especially because this can vary hugely in both complexity and the time taken to obtain.
2. Volume of data
The sheer volume of data required to calculate a value chain footprint can be difficult to manage. For example, a complex business with a large range of products and services might have hundreds of thousands of rows of procurement data across multiple spreadsheets.
3. Data structure and version control
In many organisations data is stored in multiple formats and structures, often held by different people within separate departments or business units. Inconsistencies can occur in data updates, and for multinational businesses there is a need to convert units and currencies for consistency.
4. Handling external sources of data
Capturing and organising unstructured external sources of data, especially when engaging suppliers, can be one of the biggest challenges and uses of time. Supplier engagement involves requesting information, managing responses, sending reminders, calculating results and analysing data.
Solutions to the data challenge
The first step is breaking the task into manageable chunks and understanding where to focus. Before spending significant time on data collection and measurement, it is best to start by estimating which areas of the value chain are likely to be the biggest contributors. Focusing on these areas avoids wasting time and effort on immaterial activities and helps identify the biggest reduction opportunities.
The data challenges of volume, structure and version control are interrelated. To address these it is important to find an all-encompassing solution to centralise and optimise all sources of data.
When data is centralised then regardless of where it comes from, it can be stored in one location. This is especially important as different departments within a single organisation will have access to different data, or different views of the same data, all of which need to be maintained.
For example, procurement will have information on all purchased items, finance on business travel, and human resources on employee commuting. The key is collecting this data into one system, and processing it efficiently. The more this process can be automated the easier it becomes.
When it comes to the data challenges of a large-scale supplier engagement program then it is best to set up an automated system that can generate surveys, notify responses, and effectively capture and organise large volumes of unstructured data in real time. This involves giving context to data that is received and relating it to the environmental indicators in which the user is interested.
When that data is optimised, it can be presented in easy-to-understand charts and tables to analyse and interpret, and a transparent trail of version changes will be available and multiple users can update data without causing conflict. Data also can be stored securely in the cloud and properly backed up.
As part of the Carbon Trust’s mission to accelerate the move to a more sustainable, low-carbon economy, we decided to put these principles into practice and create a better way for organisations to centralise and optimise their sustainability data. We worked with sustainability software innovators credit360 to develop Value Chain Manager, a bespoke platform to analyse and manage value chain environmental impacts, including carbon, water and waste.
Taking hold of Big Data is the first step to measuring the complex web of interdependencies between the economy and the environment. If you can’t measure it effectively, it is certainly true that you can’t manage it effectively. Leading businesses are starting to blaze a trail, dragging their value chains behind them, and where these pioneers lead others will follow. This will help drive the transformations needed for businesses to survive and thrive in the coming decades.