Can business transformation and ESG goals be attained together?

Welcome aboard the Data Express! We're embarking on an exciting journey to delve into the intriguing world of Environmental, Social, and Governance (ESG) goals and business transformation.

28 Tem 2023

7 dk okuma süresi

Welcome aboard the Data Express! We're embarking on an exciting journey to delve into the intriguing world of Environmental, Social, and Governance (ESG) goals and business transformation.

As we traverse this intricate landscape, we'll uncover how data intelligence acts as our compass, steering us towards actionable insights and aiding transformation.

To kick things off, let's ponder this amusing one-liner: 'Data is like people – interrogate it hard enough, and it will tell you whatever you want to hear.' Now, isn't that a fun way to think about data? But let's not lose ourselves in the interrogation. Instead, our focus will be on strategically deploying data intelligence to attain ESG goals.

So, buckle up, and let's plunge into the exhilarating realm of ESG-driven transformation and the strategic deployment of data intelligence

Symbiosis of ESG goals and business growth

An assessment of the 100,000+ companies suggested that ESG activities are associated with encouraging revenue growth and EBITDA margins. While this is good news, the demands of business and society won't allow for a break to celebrate.

Most organizations have achieved positive results by tackling the low-hanging fruit. Fully operationalizing the first rounds of improvements will benefit corporations, investors, and society. However, operational excellence is still a far-off goal.

Achieving long-term goals will require even more innovation, and data intelligence will play a pivotal role in every organization’s ability to evolve quickly and effectively. As a leading thinker, Peter Drucker once said, 'The best way to predict the future is to create it.

Crucial Role of data intelligence and why you should care

What sets ESG apart from sustainability is its corporate governance aspect. As such, the voices of investors are woven into the fabric of ESG. This is confirmed by Deloitte’s 2023 CXO Sustainability Report, with two-thirds of the 2000+ executives surveyed saying they feel pressure from investors to act on climate change.

A PwC Global Investor Survey (covering all investment, not just ESG) states that “Sustainability outcomes have become too important to investors for companies to treat them as mere add-ons. Instead, sustainability should be embedded into business strategy and decision-making processes about capital allocation, investment, and other strategic execution activities.”

Data intelligence is being used in various ways to advance ESG and sustainability. For instance, companies use data analytics to track their carbon footprint and reduce energy consumption. They also leverage machine learning algorithms to predict future sustainability trends and make informed decisions. However, businesses face several obstacles when using data intelligence for ESG and sustainability issues. These include the lack of standardized ESG data, difficulties in integrating ESG factors into existing business models, and the challenge of translating data into actionable insights.

This should answer the question of why data intelligence will play a pivotal role. The stakes are simply too high for us to rely on scattered spreadsheets. Information is not enough. We need reliable and actionable insights delivered at a speed that would impress Wall Street.

But before we rush to insights, we should better understand what sustainability and ESG are transforming. Just because new compliance rules only cover investor-grade information doesn’t mean your business can limit itself to reporting. We need data to fill out reports and actionable data intelligence to tell us where to focus our transformation efforts.

How data intelligence is being used to advance ESG and sustainability

Supply chain transparency: The environmental and social effects of a company's supply chain can be monitored with the help of data intelligence. Companies can use this to ensure they deal with sustainable vendors and detect and reduce potential hazards.
Energy efficiency: Smart data analysis may be utilized to keep tabs on energy use and spot leaks in the system that can be sealed off. As a result, businesses may cut their emissions and their expenses.
Waste reduction: Waste production may be monitored, and reduction strategies can be discovered using data intelligence. In addition to saving money, this can help businesses have a smaller environmental impact.
Sustainable product design: Data intelligence can be used to collect and analyze data about product use and environmental impact. This can help companies to design products that are more sustainable and efficient.

What does an ESG-driven transformation look like?

According to 2023 ESG & Climate Survey, a significant majority of executives (62%) view product stewardship as crucial to their business, closely followed by supply chain sustainability (58%). This focus on product and supply chain is not only logical but also a double-edged sword. Product stewardship, being at the heart of every product-oriented company, presents both immense business opportunities and risks. The disruption spans from supply chain design and sourcing to manufacturing operations, and even extends to evolving customer relationships to incorporate reverse logistics and circularity.

Leaders have already ventured down one or more of these paths to adapt their business models. New companies will rise as victors, while older enterprises that fail to transform may already be on borrowed time.

ESG and sustainability data intelligence poses several challenges for businesses

Data availability: Not all businesses have access to the data they need to make informed ESG and sustainability decisions. This is a particular challenge for small businesses and businesses in developing countries.
Data quality: The quality of data can vary significantly. This can make it difficult to draw accurate conclusions from data analysis.
Data interpretation: It can be difficult to interpret data in a way that is relevant to ESG and sustainability. This requires specialized knowledge and skills.
Data security: Businesses must ensure their data is secure and protected from unauthorized access. This is a challenge for businesses that collect sensitive customer or supplier data.

How can you strategically deploy data intelligence?

None of us want to be on a sinking ship. The same NASDAQ survey reveals that as the pressure to report and meet ESG goals intensifies, organizations are increasingly turning to digital tools and technologies to bridge the gap in knowledge, capabilities, and resources. This indicates that you and your competitors recognize the need for software and IoT-enabled hardware. However, NASDAQ doesn't go as far as to provide recommendations.

If your company is involved in the design, production, or distribution of products (be it raw, component, or finished), you likely have a multitude of systems trying to manage your data: CRM, ERP, LCA, MES and MOM, PLM, QMS, SCP, SRM, TMS, WMS, uncontrolled SQL and Excel repositories, and even paper.

Your data will continue to swim in an alphabet soup, but sticking with the same approach won't yield new intelligence. Achieving long-term, healthy ESG will necessitate alignment in every part of your business. To achieve this, ESG practices need to be operationalized into everyday practices, and your everyday business systems must contribute to data-driven ESG improvements and reporting.

Here are five ways you can do this:

Platform your paper, including: SQL and Excel. If you're not a statistics nerd, that means you have offline "systems," whether or not you're aware of them. No/low-code platforms can be win-win alternatives that allow IT to maintain governance, engineers to be engineers, and your company to have visibility into data (a natural precursor to intelligence).
Shift to the cloud: Embracing cloud software provides two advantages. First, SaaS providers are improving how their products support ESG. Second, progress against your environmental and governance goals can be accelerated by partnering with providers that excel in energy efficiency and security.
Demand faster, cheaper upgrades: This means your current software may not help you. However, the latest versions might pleasantly surprise you. Challenge your vendors to provide an efficient upgrade, and if they can't, consider changing your vendor.
Be outcome-driven: ESG progress requires business progress. Sustained ESG will require buy-in from Operations, and to hit their existing targets and new ESG outcomes, they will demand data intelligence native to their systems.
Use practical AI: ML (machine learning), and AI are finally benefiting industrial enterprises through solutions that focus on business problems rather than technological capabilities. This type of intelligence is imperative to making continuous progress once the low-hanging fruit has all been picked.

To wrap it up, let's borrow a line from data scientist and engineer Josh Wills: "A data scientist is someone who knows more statistics than any software engineer and more software engineering than any statistician." That says it all about how important data intelligence is and how crucial it is to have these skills to succeed in the business world, doesn't it?"

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