Climate change represents not just the greatest threat facing humanity, but also one of our greatest economic opportunities. Research suggests embracing sustainability could unlock $26 trillion in benefits through 2030 – driven by resource efficiencies, energy savings, health impacts and more. Digital transformation sits at the nexus for capturing this potential.
In this piece I detail key technologies powering the sustainability revolution from an analytics lens. I elaborate data science and IoT innovations enabling real-time efficiency gains. I overview AI techniques optimizing prediction and decision-making across the value chain. And I discuss blockchain‘s role cementing trust in shared sustainability data exchanges.
My aim is demonstrating how information and technology can transform emissions from business cost to competitive advantage. That properly harnessed, analytics unlocks a radially favorable sustainability ROI – where what‘s good for the planet fuels good business growth.
The Role of Data in Digital Sustainability
Virtually every business function today relies on data inputs and analytical models to inform planning and operations. As strategies shift from compliance to value creation around sustainability issues, advanced analytics unlocks commensurate opportunities. Specifically big data sources and data science methods enable:
Carbon footprint clarity: Granular emissions data from IoT sensors, meters and operational systems feeds complex statistical models quantifying usage, waste and impacts across the value chain. These analyses shine light on the biggest leverage points for reductions – where to most effectively focus abatement efforts and investments for maximum ROI.
Smarter business intelligence: Building holistic data pictures related to inputs, production and distribution informs dynamic simulations using AI and specialized tools. For example creating digital twin replicas of factories or fleets to stress test sustainability KPIs under various scenarios before reconfiguring real-world systems.
Ongoing optimization: Analytics informs actions but also enables continuous measurement of outcomes for further tuning. This allows adjusting variables like supply chain routes, predictive building temperatures or manufacturing cycles as new data emerges. Closed feedback loop systems running autonomous optimization algorithms can incrementally improve over time.
Below we detail leading technology categories powering the data-driven sustainability transformation and showcase bleeding edge examples of analytics in action across sectors.
Top 5 Sustainability Tech Categories
Cloud Computing
Measuring and reporting sustainability KPIs involves synthesizing disparate data sources like IoT sensor readings, ERP records, logistics transactions, weather feeds and more. Cloud platforms provide the elastic data integration, storage and analytics backbone tying together hybrid value chain Insights.
Public clouds also enable sharing sustainability intelligence – via APIs opening insights to partners or sustainability dashboards communicating performance to stakeholders. Representative solutions include:
AWS Sustainability Data Lake – A purpose-built lake ingesting ecommerce, supply chain, HR and other data streams to centralize ESG reporting and identify hotspots for carbon reductions.
Microsoft Cloud for Sustainability – Combining Azure data and AI tools for modeling environmental footprints and recommending steps to maximize reductions.
Case Study: Unilever
CPG giant Unilever utilizes AWS analytics services for daily aggregation of production metrics across 400+ factories. Their Manufacturing Data Cloud melds with sales, logistics and sustainability data powering complex forecasting and scenario modeling integral to strategic decisions and investor relations.
Internet of Things
Networks of sensors and smart devices provide the data lifeblood nourishing modern analytics. IoT deployments such as equipment monitors, building controllers and inventory trackers capture millions of environmental, operational and spatial data points ripe for algorithms to mine sustainability insights.
Smart factories connect machines, assets and infrastructure via embedded controls, meters and tags. Contextual data enables real-time understanding of machine performance, asset health, yield rates and maintenance needs. Systems dynamically schedule activities balancing production targets with energy usage through dashboards tracking key eco-impact metrics.
Likewise smart buildings monitor occupancy patterns and environmental conditions (e.g light, temperature, humidity levels). Hybrid cloud IoT platforms ingest and analyze sensor streams triggering automatic adjustment of HVAC, lighting and other systems. Optimizing for human comfort minimizes power consumption from inactive zones or outside business hours.
IoT enables similar visibility driven automation across smart grids, fleets, agriculture and most any industry. The key is converting physical world variables into computable data theaters where AI drives efficiencies at the edge.
Industry Example: Oil & Gas
Operators like Shell, BP and Equinor run extensive networks of IoT sensors monitoring integrity of remote assets like pipelines or offshore platforms. Streaming corrosion, leak detection and geospatial analytics data to cloud agents optimizes maintenance scheduling, averting energy losses and catastrophic failures.
AI and Machine Learning
Artificial intelligence (AI) broadly refers to software algorithms capable of sensing environments, learning patterns, inferring insights from data, or recommending actions based on goals. AI adoption to improve process efficiencies has exploded across sectors. Key techniques applied to sustainability challenges include:
Predictive analytics: Regression models forecast electricity demand enabling utilities to dynamically manage distributed power sources. Neural networks inform expected warehouse product levels minimizing spoilage losses.
Prescriptive analytics: Optimization algorithms route delivery fleets maximizing capacity utilization and minimizing miles. Control systems modulate building temperatures hour-by-hour balancing live occupancy data against ideal heating/cooling setpoints.
Computer vision (CV): AI video analytics identify waste sorting errors along recycling lines to minimize contamination. Object detection guides agricultural robots to autonomously prune vines or harvest fruits and vegetables at optimal ripeness.
Effectiveness depends heavily on training data quality, manual feature engineering and model explainability. Teams must vigilantly track outcomes – ensuring algorithms behave as expected while perpetually seeking incremental improvements.
Customer Example: Optimizing Energy Demand Response
UK grid operator National Grid built AI models combining weather forecasts with natural gas demand patterns over 10 years. The neural networks now reliably predict hourly demand spikes allowing for perfect synchronization of supplemental power sources across Britain‘s national energy portfolio.
Blockchain
Blockchain provides the immutable record-keeper and trust broker cementing data transparency across fragmented yet interdependent value chains. The decentralized ledger‘s cryptographic assurances enable confident record sharing and verification of activities occurring outside one‘s purview:
Supplier audits: Retailers trace inputs assurance of responsible sourcing. Miners certify conflict-free minerals. Manufacturers verify ethical recycling streams.
Automated reporting: Tokens embed sustainability attributes like emissions or water usage supporting instant, incorruptible ESG disclosures across supply networks.
Incentivization: Tokenization further allows applying sustainability credentials to financing tools. Bonds or loans might link interest rates to eco-Key performance indicators – ensuring climate considerations remain tied to economic decisions.
Where ERPs track first-party data within organizational walls, blockchain provides common layer for exchanging verified shared data across entire ecosystems. This fuels visibility enabling partners to assess collective impacts towards industry sustainability transformation.
Consortium Example: Circulor
Circulor connects companies across EV, electronics and packaging sectors delivering end-to-end transparency into carbon tracing, responsible sourcing decisions and waste footprints. The ethical supply chain network combines IoT tagged parts, machine learning transparency and blockchain immutable records revealing full production circularity insights.
Overcoming Analytics Adoption Barriers
Many organizations struggle transforming digital sustainability experiments into enterprise endeavors. Common data-related challenges include:
Data quality – Can teams access reliable, consistent and complete emissions records across operating units? What certification processes confirm supplier figures?
Stakeholder alignment – Are leaders committed to transparency on current impacts? Will they endorse evidence-based changes even if suboptimal financially in the short-term?
Legacy obstacles – How can analytics augment rather than displace existing tools across teams? Are their skills gaps that require change management?
Transitioning from ambition to action requires both cultural and technological transformations. Our accompanying guide details proven steps bridging vision with outcomes. But journey mapping begins with databased truths – benchmarking current states upon which to build future successes.
Looking Ahead
We stand at the advent of sustainability‘s data-driven revolution. One where resilience considers not just economic but environmental parameters – aligning social responsibility with shareholder obligations. Technologies explored here (plus adjacent innovations like 5G connectivity, drones and digital twins) provide tools for reality‘s dynamic measurement and meaning.
But lasting progress relies on continued questioning. Can we leverage data more effectively? What adjacent opportunities exist to incentivize sustainable behaviors? Building a future where business fosters balance across profits, people and our shared planet. There is much work ahead but beginnings forged by asking the right questions.
To explore digital sustainability in your organization, leverage our team of experts.
