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    Building efficient data processes in energy companies

    Learn how to build a change management plan for efficient data processes. Discover the importance of effective data management, the challenges companies face, and the steps to address them.

    The energy industry is at a unique intersection where emission data generation, transparency, expectations, and regulatory oversight are all converging.  As a result, companies must navigate these changes, ensuring they adapt and continue to make reduction progress during the evolving landscape.

    Validere recently hosted a webinar, where our Sr. Carbon Advisor Lindsay Campbell discussed an important topic: emission data management and processes. Campbell discussed why effective data management matters, explored the challenges, and shared best practices companies can apply to their processes. Read on to learn how Validere helps oil and companies overcome their data challenges.

    Why effective data management matters

    Government and regulatory bodies are becoming increasingly stringent with emissions regulations. As a result, companies are recognizing the importance of effective emissions data management. This recognition is fueled by the understanding of how environmental responsibility, public reputation, and investor confidence are all interconnected.

    Ensuring the accuracy and reliability of emissions data is crucial. By aligning it with your emission reduction and corporate goals, you not only enhance your organization's credibility but also unlock several other benefits. These include cost savings, improved resource allocation, and overall economic advantages.

    In the face of evolving regulations and growing environmental concerns, effective data management is important but can be a challenging feat when faced with time constraints, conflicting priorities, and team misalignment.

    Effective data management has its challenges

    Emission data management hurdles and complexities hinder companies’ progress towards a sustainable future. Campbell highlighted four key challenges: insufficient resources, competing priorities, the initial cost of change and implementation, and the misconception of needing perfect data.

    Insufficient resources
    Managing emissions data requires time, expertise, resources. Many organizations are faced with constraints with staffing, budget and access to technology. The scarcity of resources can impede the efficient collection, analysis, and upkeep of emissions data.

    Competing priorities 
    As companies juggle various competing interests, Emission data management might not always take precedence. This lack of prioritization can lead to the lack of focus on data accuracy and management.

    Initial cost of change and implementation
    Investing in new tools and technologies does require financial commitment, and the upfront costs of the tool and change management might deter organizations from adopting more efficient systems. So it's imperative to understand the value it will provide in the long term and ensure there is corporate alignment for smoother implementation.

    Waiting for Data Perfection
    Lastly, there is the misconception that perfect, clean data is required prior to starting work on better emissions data management processes. Data will always be dynamic and changing,  and the clean-up can take place while you're making progress on your processes or tools.

    These challenges are significant, but they shouldn't be seen as insurmountable barriers. Companies can make incremental improvements and can yield substantial benefit by adopting a structured approach to data management that includes data audit, needs assessment, onboarding and testing, documentation, and feedback loop.

    Addressing the Challenges: Steps Forward

    "Data is like garbage. You had better know what you are going to do with it before you collect it."

    ~ Mark Twain

    As Campbell mentioned in the webinar, addressing the challenges requires a structured approach to emission data management. She highlights five key steps to effective data management: 

    1. Data audit - Gather and document your facilities types, and the equipment details relevant to air emissions programs. Use this to establish the hierarchy and organization of emissions and operations data
    What data source systems are currently used to track and gather information necessary for emissions quantification?
    -    Inventory systems
    -    Production volumes
    -    Gas analysis
    -    Emission surveys (LDAR, compressor venting, etc.)
    -    Emissions events (blowdowns, etc.)
    -    Operational data (hours of equipment, volumes, etc.)

    2. Data assessment - The goal is to transform the data into useful information that can be used by multiple parties. Engage with stakeholders, including the end-users of the data, to gather detailed requirements. What do they need to do with emissions-related data? What regulatory or voluntary reporting are you looking to support? Is your data complete on a facility level? If it’s not, does your company have the time and resources to complete it? 

    3. Implementation - Onboarding and testing are important in successfully implementing new systems or tools.  This includes data gathering, data flow mapping, mapping validation, automation of data flows, testing and evaluation. These steps ensure that the implemented changes align with your requirements.

    -    Data gathering - Identify the source systems of data and obtain the necessary exports or integrations to access the required data.
    -    Data flow mapping - Understand how equipment, meters, measurements, volumes, samples, and estimates all work together. 
    -    Testing - Perform final confirmation and quality assurance/quality control checks on the data mapping.
    -    Automation -  Configure and automate data flows.
    -    Evaluation - Develop a plan to evaluate the effectiveness of new systems or tools.

    4. Documentation - Documentation is one of the most critical pieces to ensure the long-term success of a project. Teams need to create or update documentation to capture new system information, so it can inform and train other teams or users to maintain consistency. If you haven't documented along the way, your ability to reuse the data will be limited.

    -   Create meaningful documentation along the way. Design the system as you're processing the data.
    -    Preserve the utility of the data for the future. Avoid creating findings that no one can interpret in a year. Your use of the data today could be different from what you'll want to do later – be thoughtful about how you do it.

    5. Feedback loop for continued improvement - After implementing new systems or tools, it’s important to have a feedback loop in place to gather user feedback and continuously improve the system.
    The implementation of modern emission data management is not an overnight occurrence. It is a detailed, diligent process that calls for transparent communication, flexibility, modernization, and automation.

    Tackle your data challenges. Start today. 

    “I'm always willing to accept change, just as long as it isn't change for the sake of change. If that change will result in a better way of doing things, then I'm all for it” 

    ~ US Army Officer James Van Fleet

    Managing emission data is time-consuming and complex. The Validere platform can automate and eliminate manual processes in emissions data collection, and surface insights previously hidden. 

    See how Carbon Hub helps clients navigate these data management challenges.


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