OPPORTUNITIES PRESENTED BY USE OF BIG DATA IN SUSTAINABILITY Big Data provides us with information to prioritise action on sustainability For large businesses that have a significant environmental impact and access to large volumes of information, Big Data allows them to take action on sustainability. Interestingly, their requirement for data collection for sustainability reporting can also incentivise their smaller suppliers to be more responsible in their own operations. SCORED 7/7 VERY IMPORTANT Collecting all sustainability data into one system allows insightful analysis and the setting of targets and measurement against these targets. This allows sustainability teams to focus on action rather than on time intensive data management. For one workshop attendee, by grouping together with other linked organisations allowed the collective management of energy, water and waste across the group. Big Data would help us to prioritise which list of potential projects will have the greatest ROI and will reduce environmental impact e.g. should we focus on waste versus energy. One table noted that this prioritisation of issues is also impacted by differing compliance requirements by industry. Compliance is where it all starts. However, some attendees felt that compliance hasn t yet embedded sustainability into organisation one organisation noted that now it was no longer under the CRC, the board demonstrated a belief that carbon was no longer important. Financial business cases and use of appropriate language depending on one s audience are paramount to the prioritisation of sustainability initiatives. If we are confined to using spreadsheets, having more data won t help it will just make things more confusing, taking more time. A barrier to using Big Data to prioritise action on sustainability is that most organisations lack the skill set internally to develop the algorithms and do required analysis. The software required to handle big data requires a large investment. These sustainability initiatives must be embedded into the organisation and achieve engagement across organisational silos, however, often it is difficult to explain the value of collating this vast amount of data to individuals, particularly in HR. It is difficult to understand how to prioritise action on scope 3 emissions, particularly for mandatory GHG reporting. How reliable is the information provided by suppliers? Team considered that main driver was cost reduction and that sustainability needs to be directly linked to bottom line activity to be high on the executive agenda. It is also important to understand what your industry specific low hanging fruits are this helps prioritise action
Big Data allows companies to understand carbon hotspots BT examined the carbon footprint of the business and found that emissions out of its direct control accounted for 92% of total emissions. Understanding this vast amount of data meant that BT was able to highlight carbon hotspots in their own operations and that of their suppliers where it could focus its efforts to create opportunities for carbon and cost reduction. SCORED 7/7 VERY IMPORTANT It was noted that for some industries embodied carbon is important and this is not covered by legislation. Individuals expressed a desire for more guidance on how to manage their supply chain this is often a significant area of environmental impact. Big Data allows benchmarking across buildings and benchmarking sustainability performance against your industry Data analytics allows you to benchmark across your portfolio to understand where performance improvement opportunities may exist in particular buildings. Companies reporting to voluntary sustainability reporting initiates such as the CRC, CDP & GRI are benchmarked against their peers in sustainability performance. Companies that perform well enjoy the benefits of improved brand reputation and investor confidence. SCORED 5/7 - IMPORTANT SCORED 5/7 - IMPORTANT It is difficult to compare whether a difference in performance across organisations is something that is natural due to differences in the estate. It is important to ensure what is in scope and what is out of scope when benchmarking. Internal benchmarking is easier - the outliers help understand what may be wrong. MER provides an opportunity to look at scope 3 emissions While the CRC league table was not great, individuals reported that their board wanted to improve their scores year on year and saw it as a reputational risk. CDP benchmarking is much better and has improved year on year.
Big Data volume smooths out data inaccuracies (more trumps better) It has become economical to capture, store and analyse all the data from operations, so why settle for a sample of it. Moreover, uncertainties and inaccuracies can be smoothed over by the sheer quantity of data and so are less harmful than they once were. SCORED 3/7 QUITE IMPORTANT Where there is no automisation, data collection requires human input and it is inevitable that errors will occur. Is the best way to deal with this just accepting all data or to accept that which falls within a reasonable range? Which best reflects actual performance? If we accept all data then we can debate and investigate anomalies Big Data opens up the opportunity of new product market research If organisations are more transparent with their sustainability data and share performance metrics, they open up the opportunity to tap into new potential for innovation through people who use their data to help co-create their products. SCORED 5/7 IMPORTANT Big Data allows us to create whole life costing models Whole life costing refers to the total cost of ownership over the life of an asset and considers financial, environmental and social costs. These models allow us to understand and to reduce the environmental impact of a product/service throughout the entire supply chain. To understand the entire environmental impact of a product or service in this way requires Big Data tools. Researchers at Columbia Engineering have developed Big Data tools that will allow the calculation of thousands of products and materials almost instantly. This will help standardise product carbon footprinting and labelling. SCORED 5/7 IMPORTANT Individuals working in sustainability rarely come from financial backgrounds but have to understand concepts such as the ROI and the NPV of assets. It is complicated to build the cost of carbon into investments, but this is becoming more of a necessity High hazard industries do often consider things such as the cost of carbon as part of their risk mitigation strategy, but in mainstream industry only where there is a compliance issue.
CHALLENGES PRESENTED BY USE OF BIG DATA IN SUSTAINABILITY There is a challenge in understanding where in the organisation to focus Big Data investments Although organizations are investing in big data, budget limitations are considered the biggest challenge. How should your organisation focus Big Data investments? SCORED 7/7 VERY IMPORTANT This is key but it is difficult to understand how best to get buy-in to a Data Programme. Often, implementing Big Data software requires on-going external expertise which is an added investment. It was suggested that metering and sub-metering is a good initial focus as this provides granular data on inefficiencies The challenge of getting top management in the company to approve investment in Big Data It can be difficult to demonstrate the return on investment of Big Data tools and related investments such as training. SCORED 6/7 IMPORTANT This challenge varies between the public and private sector, and between data projects targeted towards compliance and those targeted towards strategy. Strong business cases, using financial language are fundamental to approve investment in big data. How can we get business units to share information across organisational silos Organisations rarely have a centralised database with all HR, energy, water, waste, financial data. Often individuals do not know what data exists within the organisation and when they do, it can be difficult to collate this data from across the various organisational silos. SCORED 7/7 VERY IMPORTANT This is difficult when information is sensitive or comes in strange formats with lots of technical jargon. It is often difficult to understand what information is relevant. The difficulties of keeping track of continual changes within an organisation can leave people feeling confused and disconnected. Culture change that originates from the Board level with a high-profile Champion is therefore the most effective.
The challenge of gathering external data from suppliers and other sources 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. SCORED 4/7 QUITE IMPORTANT Most organisations do not have a platform to collect all this data. These needs to be a focus, such as concentrating on biggest 100 suppliers. In requests for tenders, most organisations simply ask them to discuss what they are doing to demonstrate their commitment to the environmental issues and decarbonisation. Very rarely to organisations require evidence or ask for the specific information. The procurement team often have no idea what to do with all the information that they receive from clients. The data needs to be standardised and made comparable. Procurement teams need to understand conversion factors and knowing what has and what has not been reported. This is complex. The sheer volume of data is a challenge as it means we have to interpret thousands/millions of data points 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 spread sheets. SCORED 4/7 QUITE IMPORTANT If we had the right software and analytics it would not be a problem Once we have analysed the data, there is a challenge in understanding what sustainability looks like for an organisation. This is not difficult with financial data and kwh this information is close at hand. It is difficult when it comes to things like tonnage of waste. Companies receive an unmanageable amount of requests for information from different stakeholders. Ownership of data reporting should be given back to individual silos. The silo nature of organisations is a challenge sustainability is cross-functional. There is a risk of duplication with different silos spending time analysis the same thing or using data differently. There is no collaboration between silos within organisations. NHS Supply chain is focusing on top 5 purchases. Even this is a big challenge and it is difficult getting this information from different hospitals in the right format.
It is hard to get the right technology that can control the large volume, velocity and variety of Big Data SCORED 7/7 VERY IMPORTANT We can no longer rely on spreadsheets for collating, storing and analysis of large volumes of data. Companies are faced with a decision on what software they require to handle their Big Data requirements. How can we make this as automised as possible? People are spending so long on inputting the data and then have no time to act on it Many companies have previous systems which their board want to extend the environmental reporting they just are not flexible enough Engaging those who need to input data requires a massive culture shift particularly HR as they do not see it as part of their job and therefore are not invested in the process Systems aren t connected Creating the questions which lead to insight It is best to start by defining the insights and questions we want to answer and then collecting the necessary data. This avoids the resource-hungry process of gathering and managing all available data. But whose responsibility is it to work out the questions? How do we establish the optimal way to organise Big Data activates within the organisation? SCORED 5/7 IMPORTANT Compliance defined questions Using data to develop strategy Variety different sources. How to connect different sets. How focus output when large number of users Benchmarking against peers