Data science

Sustainability from a data mindset (part 1)

April 2, 2024 - 5 minutes reading time
Article by Frank De Nijs

The increasing pressure on environmental responsibility due to CSR, ESG, and emerging CSRD guidelines with Scope 1, 2, and 3 reporting obligations is well known. Many organizations still see sustainability as a reporting exercise rather than a real business transformation. But sustainability goes beyond reporting; integrating it into business processes leads to both better results and financial benefits, which is crucial for sustainable investments. Let's be honest, business benefits are essential to anchor sustainability investments.

'Organizations that embed sustainability broadly in their practices exhibit a form of sustainability that leads to better financial results'

Integral sustainability means taking sustainability out of the functional silo and integrating it into every business unit. Both in the core functions and workflows of their own organization, as well as with adjacent players within the ecosystem in which the business operates.

Organizations that follow the money, rather than treating sustainability as a stand-alone phenomenon, will be able to profitably embed sustainability into their operations. Take the order-to-cash workflow as an example. This includes various functions and processes, such as sales, distribution, inventory management, supplier and customer engagement, where sustainability can have a distinguished impact.

For organizations that approach it integrally, sustainability within their own organization is also about long-term added value for customers and society.

Sustainability in a broader perspective is therefore about understanding the drivers of each player and the practical consequences of any change in the ecosystem.  Consider reduction of waste from transportation, inventory, movement, waiting times, overprocessing, overproduction, defects and skills of employees involved.

Each coin has its flipside, such as reducing transportation movements that can lead to larger inventories in spaces that must be maintained at temperature. The trade-offs require a permanent integration of sustainability into the existing corporate governance framework that aligns standardization with ecosystem-wide collaboration. This allows chain perspectives to be leveraged on reducing waste, on the one hand, and opportunities for far-reaching energy transition, both in the business units and the ecosystem, on the other. A nice aspiration, but how feasible is it? What can organizations do about it?

Applications & data

The importance of data in achieving sustainability goals is also seen as logical by senior executives. If we look at the data needs around energy transition and waste reduction, we see a hugely fragmented landscape. Many aspects of business operations that affect sustainability appear to be represented in a multitude of business applications. Each of those business applications has its own origin and framed set of data, a database as a silo.

At the same time, there is agreement that in order to succeed in intended changes, high-quality data and transparency are necessary. Despite the recognition of the link between data and sustainability success, automatically retrieving sustainability data from core systems is proving to be quite a challenge.

Another important consideration is that not only large amounts of data are useful. First, it is important to be able to establish that collected data is useful by demonstrating that it is being applied appropriately. Of course, the adage "if you can't measure it, you can't manage it" applies here, but managing data does not come for free. So being selective with data pays off quickly. On the other hand, if one does not have access to the data that paints the picture of sustainability throughout the organization, the likelihood of effective action without adverse consequences elsewhere in the chain is significantly reduced.

The challenge is therefore to first obtain insights into sustainability data in a balanced way from the 'jungle' of business applications and the tangle of data silos, and to be able to combine them. This is not a one-off exercise. Only by integrating data from operations based on continuous monitoring can a lasting positive difference be made to business value.

'Initially, it is important to be able to determine that collected data is useful.'

Off the beaten track

Partly for this reason, Centric believes it is important to be able to access data derived from its business applications efficiently and conveniently. The digitalization mindset in business operations, and consequently the IT landscape, is broadening for the same reason. Traditionally, business applications and underlying data have their origins in process-oriented thinking. This is characterized by, among other things:

  • focus on standardization and efficiency of business processes
  • centrally controlled decision-making and management of the whole
  • focus on best practices (because: today's success offers more certainty for tomorrow)

Applications are purchased and enhanced for each process or part of it. Linking those process parts is the obvious thing to do. The result is a huge tangle of APIs that interconnect applications, mostly transactional in nature and focused on one or more actions within a specific business process.

However, the digital perspective on the sustainability issue also requires a different mindset: the data mindset. This is now just as important as the process mindset. The data perspective emphasizes issues such as:

  • focus on the intelligent acquisition and use of process and situational data
  • dependence within a network of autonomously deciding and acting parties
  • focus on agility (fail fast, and above all learn from it)

By setting up data only from the process perspective, choices are made that are simply not workable from the data perspective. For example, there are examples of applications that store completion forms as unstructured data, for example as an image file in PDF format, because an employee only needs to view it briefly to then approve it. The data in the form is otherwise virtually unusable for analysis unless someone again applies additional techniques to convert the image back into tabular form, for example with OCR. In short, an expensive and error-prone detour to analysis, due to a missing data mindset.

Data coming from other parties in the ecosystem, whether public or not, are also crucial for a solidly based understanding of sustainability measures. Instead of linking applications process-oriented, or transaction-based, there is an increasing need to be able to link data sources as well. With those links, data queries can be realized that are completely independent of what a business application can or wants to release in terms of data at any given time.

'By setting up data only from the process perspective, choices are made that are not workable from the data perspective'

Chapter 1

The end

The pursuit of sustainability therefore goes beyond optimizing one's own business processes. We also conclude that understanding sustainability is hampered by data silos behind business applications. A data mindset alongside the longstanding process mindset lies at the heart of comprehending sustainability choices. 

Related articles
Structured work on data quality
Data science
Few organizations still work on data quality in a structured way. Natan van der Knaap, graduate student a ...
Data science should increase efficiency above all
Data science
That data science is a promising field of research is beyond dispute. But what exactly is its purpose? Th ...
Data-oriented working in nine steps
Data science Retail Finance Public Logistic
The rise of data science offers an enormous amount of opportunities to map and solve problems within your ...