Data for Sustainability: a familiar enterprise journey

Today, with so much focus on sustainability and ESG reporting, how should sustainability data be treated? The answer may be simple, yet enlightening: the same as all other enterprise data. There are lessons learned there to consider, based on the steps towards data mastery that organizations have gone through in the past decades. So, follow our journey from carbon costing, to planning, analytics and sharing.

Taking one step back to reflect on the evolution of enterprise data in the last 20 years we see some clear patterns. We have been through different eras on data platforms. At first, we had the era of the Enterprise Data Warehouses and Business Intelligence (BI), structured and centralized often focused on reporting and dashboarding. Then came the era of Big Data and Data Lakes, a little bit less structured and bringing in more data science and AI for forecasting, planning and more.

And now we seem to be in the era of the Data Lakehouse – combining the two. The latter enables handling both structured, semi-structured and unstructured data, loaded in batch or real time, to serve both analytical and machine learning use cases. There is also an increasing emphasis on sharing and collaborating on data, both within the organizations and between different organization.

Looking at data, we separate actuals versus plan data, whereby the plan data is normally reflected in budgets or forecasts with financial and non-financial measures. The planning applications differ from BI/ analytics applications in a couple of ways. First, it is supporting ‘writeback,’ whereas a BI/analytics application is read-only. Second, the planning applications support top-down allocation and bottom-up aggregation. A BI/analytics application mainly supports bottom-up aggregation. Scenario modelling and what-if analysis are normal features here, alongside with process support and approval/ rejection procedures.

Top-down and bottom-up

Most large enterprises create a high-level strategic plan as part of their target setting process. In planning applications this is mainly handled top-down. Then the business creates their business plan bottom-up to - in the ideal situation - meet the targets set in the overall strategic plan. If not, the bottom-up plan by the business is often rejected and required to be revised. With all initiatives on sustainability going on we anticipate the rise of Sustainability - or Carbon planning - in these applications.?

Actual data, to be compared with the plan/budget/forecast, is ingested, curated, and stored in the data platform side by side with the plan which have been imported from the planning applications. There are different types of analytical processing on the actual (and plan-) data, i.e., reactive, and proactive. Reactive means reporting on history, looking at trends over time and creating a sense of where we are today and why we are here. Proactive means predictive analytics, prescriptive and cognitive analytics. Here we enter the domains of AI and machine learning. We have already seen Sustainability Cockpits and Dashboards emerge.?

Fitting it all together

Within an enterprise selling products or providing services, it is common to do product costing to know which price to ask in the market and which margins it will bring. The same goes for sustainability and carbon neutral scenarios – enterprise should do Carbon Costing for their products and services and make them transparent and available throughout the product lifecycle.

Certainly, the enterprise should also do a Carbon Plan – with normal top down-, bottom up- and write back capabilities mentioned before. Without a plan the enterprises will not know if their sustainability achievements are good or bad, ahead or behind targets. Carbon costing alone will not do the trick since it calculates carbon footprint product by product. But with planning applications being able to use allocation keys, the drivers behind carbon footprint can be spread across and allocated to all levels of the enterprise. This ensures scalability and speed and ability to handle complexity.?

On the accounting side, taking place in ERP’s or expert applications, we see Carbon Accounting now evolving. In most of the cases in conjunction with the Green Gas protocol. Separate ledgers are being set up, side by side with the regular general ledger. CO2 is then the currency, instead of EUR or USD.?

For Carbon Analytics everything needs to be combined, carbon costing, carbon planning, carbon accounting and actual data from various data sources. When all that data is collected in the Data Lakehouse, analytics and reporting can take place. Cloud data platforms with agility to integrate to new data sources and formats, and its elasticity and scalability will be enabling this. Most enterprise already have one or several of these platforms in place. Do they need to buy another one just for sustainability data? No, leverage the exiting investment to also harness sustainability data is the goal! To steer things right, even before they occur, predictive and prescriptive analytics is needed on top of the data.

Data sharing and Data Ecosystems will be key requirements since the ESG-data need to be shared not only within the own organization, but outside the enterprise, too. We already now see expert companies providing ESG-data as benchmark or as ruling data on whom to do sustainable business with. This data should be integrated as external market data into the company’s own calculations and ambitions.

Sustainability Data Hub?

In the Data for Net Zero service from Capgemini we see main objectives such as Anticipate, Measure, and Improve to be addressed by our clients. ‘Anticipate’ goes back to our reasoning about Carbon Costing and Carbon Planning above. ‘Measure’ goes back to Carbon Accounting and Carbon ana-lytics on top of the consolidated data in the Cloud data platform. And by anticipating and measuring we will see ‘Improvement’ and change.

To conclude, we have learned a lot from the journey towards becoming truly data-powered. And in the same fashion that we have been using data in increasingly advanced ways to support – for example – finance and accounting, we can now do the same for the race towards Net Zero. Only much faster. It’s a matter of not reinventing the wheel, but standing on the shoulders of (data) giants.

This article is extracted from the last Data-Powered Innovation Review | Wave 4. You can discover more article on #dataforsustainability on the magazine available for free here