With climate change and resource scarcity threatening our planet, companies worldwide are racing to transform their businesses to achieve improved sustainability. However, without the right tools, achieving meaningful progress is not easy. This is where building custom analytics software can help you make a difference.
Data analytics provide deep visibility that companies need to optimize energy use, reduce waste, and understand their societal impacts. It replaces guesswork with data-driven insights that identify the most impactful areas for improvement.
Specifically, analytics enable tracking key sustainability KPIs across the organization to pinpoint inefficiencies and excess consumption. This includes metrics like energy usage, carbon emissions, resource consumption, waste generation, and diversity stats. Advanced machine learning algorithms can even forecast future resource needs based on this historical data, allowing proactive planning to minimize consumption.
Data-driven sustainability initiatives powered by purpose-built building analytics software have proven results. Unilever reduced its greenhouse gas emissions by over 20% while increasing production volume by over 27% through analyzing and optimizing energy usage across its manufacturing plants. These impressive results demonstrate the power of data analytics in enabling ambitious sustainability targets.
However, entrenched organizational barriers often hinder transformation efforts. Let’s examine the four “hidden enemies” that can sabotage sustainability initiatives and see how the right analytics approaches can help overcome them:
- The Problem: Sustainability is siloed in a single department rather than integrated across the company. This limits scope and alignment.
- The Solution: Descriptive analytics gives end-to-end visibility across functions, enabling enterprise-wide alignment of sustainability efforts. It ensures initiatives in domains like operations, supply chain, and HR are synchronized under a common vision. A common example is Unilever, which successfully leveraged analytics in this way to reduce water usage company-wide by nearly 40% from 2008 to 2015.
- The Problem: Short-term profit goals end up clashing with sustainability objectives. This creates hesitation in approving green investments.
- The Solution: Adapted optimization models balance financial and sustainability KPIs to deliver win-win outcomes. For example, AI can generate scenarios for modernizing equipment that maximize ROI while minimizing emissions. This provides the confidence to approve investments that support both profit and the planet.
- The Problem: Leadership clings to old ways, lacking the mandate needed to drive sustainability top-down. This results in lip service rather than meaningful change.
- The Solution: Diagnostic analytics reveals cultural barriers that need to be addressed through education and coaching. Sentiment analysis identifies concerns or misconceptions among leadership to be overcome. Behavioral analytics tracks progress on culture change and sustainability mindset adoption.
- The Problem: Existing employees lack the data-driven decision-making skills needed to support sustainability transformation. This results in missed opportunities.
- The Solution: Workforce training in data mining, simulation, machine learning, statistical analysis, and other sustainability analytics competencies. This equips employees to leverage data insights in their daily roles. Learning programs also cultivate the mindset shift needed to become stewards of resources versus drivers of consumption.
Source: Author’s expertise and experience
Let’s see how leading companies are utilizing analytics to make progress on their ambitious sustainability visions:
Walmart, the retail giant, isn’t just about shelves stacked with products. They’re also stacking up data to optimize their supply chain. By digging deep into supplier data, they’ve figured out how to move goods around more efficiently, which means fewer emissions from transportation. It’s all part of their mission to go completely green with 100% renewable energy by 2035.
BMW isn’t just about crafting sleek and powerful cars; they’re also using analytics to make them greener and more sustainable. They’ve brought machine learning into the mix to predict the best way to take their cars apart when they reach the end of the road. Why? The reason is because this smart disassembly process lets them salvage and reuse parts and materials, reducing waste and boosting sustainability.
Unilever, the consumer goods company, is doing some heavy data lifting too. They’re not just churning out products; they’re also churning through energy, water, and material data in their factories. Why, you may ask? They are doing it to spot where resources are being wasted and to stop it in its tracks. This data-driven approach is crucial to their goal of making every single one of their products sustainable by 2030.
A company’s Environmental, Social, and Governance (ESG) score is crucial for attracting investors and top talent. Data analytics directly strengthens ESG performance across all three dimensions:
- Environmental: Enables reducing emissions, waste, resource usage, and supply chain impacts through data insights. This elevates the “E” component of ESG reporting.
- Social: Identifies diversity gaps, unsafe working conditions, human rights issues, and other ethical concerns through workforce analytics. Allows corrective actions to be taken that improve the “S” component.
- Governance: Supports ethical sourcing, anti-corruption, transparency, and accountability with analytics-driven auditing and compliance monitoring. Boosts stakeholder trust reflected in the “G” component.
In essence, data provides the evidence needed for more accurate and compelling ESG program development and reporting. This leads to tangible benefits like easier access to capital, lower financing costs, and a more motivated workforce.
- Data analytics provides the visibility to optimize operations for sustainability.
- It helps overcome organizational barriers that are hindering transformation.
- Real-world case studies showcase analytics enabling companies’ green goals.
- Strong ESG performance forged by analytics delivers competitive advantages.
Forward-thinking companies will embrace analytics to build resilience and secure a sustainable future. Will you lead others to such an impactful cause or be left behind? The choice is yours.
Descriptive analytics is like the magic tool that lets different teams within an organization see what everyone is up to. It’s kind of like having a window into each department’s efforts. When you have this kind of visibility, it becomes way easier to bring all those separate sustainability initiatives under one roof.
Short-term cost reduction can conflict with long-term environmental benefits. Analytics models are optimized for both profit and sustainability KPIs.
Focused training programs to build competencies in areas like data mining, simulation, machine learning, and statistical analysis for better sustainability.