Data fuels the modern business world and that’s as true for sustainability as it is for any other aspect of an organisation, whether it be transactional data used by the finance department or customer data used by the marketing or sales department. With the advent of increasingly sophisticated tracking, monitoring and sensory devices connected to the Internet of Things, organisations of all sizes are now producing huge volumes of data relating to their core operations on a daily basis.
In this article I want to look at why this data is so important to corporate social responsibility (CSR) policy and how to go about harnessing it. Let’s begin by looking at why CSR is so important to a 21st century business.
Why is Corporate Social Responsibility Important?
Corporate social responsibility is often seen as shorthand for a company’s approach to compliance and statutory regulation. Whilst emissions reporting and an ever changing regulatory landscape predicate the need for a robust policy in this area, this really only represents the first pillar in a strategy that has short, medium and long term commercial benefits for the entire business.
I’ve discussed the four pillars of corporate social responsibility policy before but let’s remind ourselves of them again here:
- Compliance: This is an essential for any organisation that has statutory reporting requirements placed upon it; especially those in the manufacturing sector.
- Driving Efficiency: The first real commercial gain from CSR policy will be through driving more efficient processes at an operational level, whether it be through less waste or more joined up supply chain management.
- Innovation: A developed CSR policy means using data collected across the organisation and leveraging it into your core business model and corporate strategy. This can usually involve business-wide change management, which many businesses may choose to bring under the remit of a Chief Sustainability Officer (CSO).
- Storytelling: The final pillar of CSR policy is all about brand CSR storytelling and how you translate the hard and often esoteric world of non-financial data and operational efficiencies into a brand narrative that you can present to your stakeholders and customers.
These four pillars tend to evolve over time, but when working together they represent the power a business-wide approach to sustainability has in driving profitability and brand image, as well as future-proofing all your compliance and reporting requirements.
Mapping Data Topology
Putting the data your business creates to good use involves mapping it and this is a more complex and involved task than it may first appear. It’s important to know all the data the organisation is generating and this means asking people across departments to give you access to it. You might not appreciate the need for all this data but at some point in the future it may become very important so it’s crucial you get a handle on all the data.
Collecting isolated datasets is only one half of the exercise. You need to also understand exactly where the data is coming from and by definition how it is potentially connected to other datasets you may have. This will allow you to create a topological data map, which will form the basis of your analysis.
Ensuring Data Quality
Establishing data quality is also essential if you are going to rely on your datasets for analysis and interpretation. There are several dimensions to data quality but the two most important to sustainability policy have to be data accuracy and timeliness.
Accuracy refers to the degree to which the data is a representation of the physical system or world it is measuring. This can be improved by use of an automated process of verification; ensuring data is within tolerances or meets validation rules, as well as the use of a structured data validation checking process where data is reviewed and signed off by other people.
Timeliness refers to the time it takes to get from data capture and entry to reporting of verified data. For some companies this is far too long to produce quarterly board reports for example.
Managing Sustainability Data
Collecting, analysing and drawing meaningful conclusions from all these disparate data sources is a complex and hugely coordinated task that is more often than not beyond the capabilities of an Excel spreadsheet (as useful as these are for individual analysis and some reporting). Many companies will therefore inevitably opt to bring all this data under the remit of a single sustainability management system. Choosing the right sustainability data management tool is a serious exercise in itself, with profound implications for the organisation well into the future.
It’s important therefore that a proper business case be put together, you get buy in from senior management and IT are involved from the start. You will also need to map out all the things you will need your sustainability software to do, both now and several years into the future, as this won’t be a tool you can simply replace for another one at the drop of a hat.
About the Author: Nicola Ainger is an Account Manager at Bristol based global sustainability, EHS and risk data consultancy, SustainIt. She is an expert on implementing data driven systems and strategies to drive cultural change and engagement throughout the corporate world. You can connect with Nicola or SustainIt on Twitter, LinkedIn or Facebook.