Once we've defined which ones are relevant and not, we can look at each goal's targets and indicators. Those are the actual quantitative measures of our progress towards achieving this goal. Now step number two is to define a key metric. Often, the SDG indicator will not be directly applicable to your company so we need to define a metric that we believe will directly and positively affect that indicator. So as an example, let's say we decide to focus on goal number three, which is to ensure healthy lives and promote well-being for all at all ages. We can go through the different targets and indicators and pick those indicators that are relevant to our company, so for example if your company has a factory in a particular community we can identify that the indicator 3.9.1 "mortality rate attributed to a household in ambient air pollution" could be impacted by our company's actions.
So a key metric we can define is what are the concrete PM2.5 or harmful gas emissions attributable to our factory? We can make a commitment to drive that number down ideally to zero and we can measure the impact of that which should directly translate to an improvement in the indicator we are interested in.
Having defined a metric, you need to decide how to measure it. Depending on the type of metric, this data might be available in the form of existing corporate data, recorded in documents or spreadsheets. It might also be measured by sensors deployed in your physical facilities, or might need to be estimated following standard accounting procedures.
It may seem that a metric for which you already have the data would be an easier target to measure. However, organizations often have trouble with information silos, and it might be inefficient to collect all the data from different scattered documents in your organization. That's where natural language processing tools could be of help.
Recently, we've seen the development of several AI-based search tools which seek to unify the information silos in a company and aggregate all the information into a data lake, or a knowledge base. Such knowledge bases contain aggregate information on the company and might make it much easier to measure this data. If you have such a digital transformation initiative in your company underway already, these metrics might be much easier to measure than otherwise. Similarly, if you already have sensors in your physical locations, data aggregation might be easier, considering your organization already has protocols for data aggregation, statistics, and summarization.
If not, the cost of deploying a network of sensors in physical locations, and the software development required to aggregate and process this data must be considered when accounting for the costs of such a sustainability project.
Some of the targets cannot be practically directly measured, and must be estimated by scientifically based accounting methods. For example, to estimate a company's carbon footprint, the GHG protocol
is a good source of a consistent set of standards. As they are being calculated by indirect means, improvement in these targets will require a more detailed plan as there might be many contributing factors that indirectly affect the outcome. As such, it might not be immediately clear where action is required. Thorough evaluation of the accounting protocol to determine all the inputs that factor into it will help you map those contributions back into tangible measurable business metrics.