Data Driven Approach

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Data-Driven Sustainability Framework

Data-driven sustainability is the process of making decisions that are centered on sustainability and are informed by the collecting, processing, and analysis of data. The data is then visualized to produce a greater, significant impact and more ethical business practices. Insights from sustainability data can create huge improvement while boosting revenue, whether it's reducing greenhouse gas emissions, improving supply chains, or reducing waste.

The three major stages to follow when using a data-driven sustainability strategy are:

  • Establish industry-specific sustainability objectives: It is essential to have an industry-led strategy.
  • Create a framework using artificial intelligence and machine learning so that businesses may use data modelling and forecasting methods to examine how current processes relate to sustainability objectives.
  • Combine human and machine intelligence: machine learning takes over many data processing duties, revealing insights quickly so that teams can make sure data-driven decisions.

A growing amount of pressure is being placed on organizations to manage their sustainability performance as a result of reliable data gathering, reporting, and analytics. In order to empower industries to make strategic, real-time decisions to meet sustainability goals (i.e Emission monitoring, GHG reporting and reduction etc), we apply data-driven sustainability frameworks to emission monitoring, reporting, and reduction through automatic data gathering, processing, and analytics.

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