Why industrial dataOps is fundamental when building and scaling data-driven solutions to drive sustainable operations

Developing, tracking and meeting sustainability goals is becoming increasingly important for companies in the public sphere. Many of them are turning to data-driven solutions to help them monitor, report and reduce their environmental impact. Recent regulations in Europe recognises that Data-driven solutions for GHG emissions reductions are contributing to climate change mitigation (see official site from the European Commission - link)

At Cognite, we have encountered many innovative solutions that promote sustainability. Examples include: 

• Automating the recording and reporting of greenhouse gas emissions in industrial processes.

• Optimizing energy efficiency at the equipment and system level to minimize scope 2 emissions.

• Using robotics to detect dangerous leaks.

• Leveraging operational data to derive the environmental footprint of discrete products.

• Protecting biodiversity with the automatic detection of birds close to onshore windmills

After analysing dozens of those solutions,  3 key observations emerge:

1. Traceability and auditability are best supported with focus on the data flows.

2. A large set of important sustainability metrics in industrial settings are best managed by real time calculations.

3. While diligent and accurate metrics are important, decisive actions matter most.

All 3 observations demonstrate the importance of several DataOps capabilities as depicted here:

1. Traceability and auditability are best supported with focus on the data flows. With the increased scrutiny from regulators, auditors and customers to provide traceability and transparency behind reported environmental metrics (for instance GHG emissions metrics), -companies are spending -more time describing their reporting methodologies and key assumptions for calculations and estimations. Typically, data flows enable the schematic descriptions of which systems provide what kind of information, how this information gets aggregated for specific assets, and what steps are undertaken to process the source information into the metrics. Adding new data sources, auditing and enhancing these data flows must be efficient - this is where data onboarding and lineage all the way from a calculated metric to the data source is an absolute must-have to ensure trustworthy results, while reducing development and operational costs. Important capabilities: Data Lineage, Versioning, Access control and sharing. 

2. A large set of important sustainability metrics in industrial settings are best managed by real time calculations.We often see that data used for sustainability calculating metrics is hard to get, or comes from sources far from the actual operations ( Invoices for instance). The closest the data is to operations and real time, the more effective it is to get accurate metrics while monitoring them. Furthermore, given the wide variety of guidelines, protocols and choices that can be used for calculating metrics, it becomes increasingly important to effectively explore different scenarios and calculations methods. These metrics then need to be packaged as a trusted data product that can be consumed by different stakeholders across departments and functions. Important capabilities: Data Type Support, Live Data Access, Data Discovery, Model Governance and Time series data quality monitoring. 

3. While diligent and accurate metrics are important, decisive actions matter most. With a proper practice of recording and estimating the environmental impact of operations,  it becomes much easier to enable decisions that effectively drive positive impacts in sustainability. For the Oil&Gas industry, this can entail the substantial reduction of emissions while keeping production rates high, minimising oil in water concentrations in effluents, and finding strategies to effectively reduce flaring. With a solid data foundation, companies can  track sustainability metrics against given budgets on a day-to-day basis, triggering alerts to process engineers, forecasting possible scenarios based on historical data, and leverage those sustainability metrics to improve operational decision-making and further optimize operations. Highlighted capabilities: Incorporating Physics, Industrial equipment and process data models and templates

In the coming weeks, we’ll share more about how Cognite Data Fusion enables companies to deliver more positive impact supporting their sustainability strategies with digitalisation and data-driven solutions. 

In the meanwhile reach out if you have a great case you’d like to share, or a challenging sustainability challenge you’d like to get solved!