What does Data Governance mean for Utilities?

As Utilities embark on the ambitious journey towards the Clean Energy Transition, by modernizing their grid and adapting to the “Green is the new black” philosophy, the criticality of the data housed in their key systems become ever so clear. For a successful transition to the new, the legacy must be strong, which means underlying data such as distribution network model, asset attribute data, communication channels to real-time systems, customer information and distributed generation data must be Available, Accurate, and Analyzed (The 3-As of Data Quality). This can only be achieved by implementing robust “Data Governance”, which means standardizing methodologies (Business Processes and Quality Assurance) and technologies (Data Analytics and Automation) to ensure that the data meets the 3-A requirement. 

The case for Baking the Cake, before Icing it!

Before Utilities can implement and drive value out of complex solutions like Volt Var Optimization (VVO), Distributed Energy Resource Management (DERM) and Real-time Demand Response (DR), essential for Grid Modernization (i.e., the icing), the foundational data enabling these technologies (i.e., the cake), need to be in place. For example, for VVO to generate accurate optimization results, the power flow results must be precise, which is driven by accurate connectivity model and associated asset attribute data. Therefore, collection of the key data elements and implementing Data Governance to maintain the integrity of these data elements will be crucial before initiating implementation of any advanced technologies to enable grid modernization. 

Data Governance can be Scary without Technology

The most challenging aspect of Data Governance is dis-integration among multiple systems housing the data, multiple personnel creating and editing the data, and parallel stakeholders accessing the data. This dis-integration across systems, processes and data warehousing can lead to data corruption. However, standardization and technology can be the friend that lights the way towards successful Data Governance. Below are just a few ways technology can help: 

  • Automated Quality Checks: Implementing automated QA/QC embedded into Enterprise Asset Management (EAM) systems to prevent introduction of errors, allow utilities to reduce data corruption at the source level. If a Designer attempts to post a new work order request using a Graphic Work Design (GWD) tool and it does not meet the quality standards, these automated rules would prevent the posting of the work order and hence preventing introduction of bad model data into a real time system like Advanced Distribution Management System (ADMS). This could have tremendous benefits improving not only operational efficiency but also field worker safety by providing an accurate representation of the model to distribution operators who are directly
  • Modern Data Architecture: A robust cloud based data architecture providing an integrated view of critical data from various systems, could enable Utilities to perform advanced data analytics to gather insights and perform predictive studies to proactively resolve data issues 
  • Artificial Intelligence: Creating Machine Learning based models to identify data anomalies and discrepancies and then using Robotic Process automation for implementing data clean up, can add significant efficiencies and reduce O&M costs associated with data governance 
However, Technology can only do so much

Technology is a piece of the puzzle when it comes to Data Governance. People and Processes are the other two pieces that complete the trifecta that leads to a good data governance model. Therefore, it is crucial to make organizational change and create a multidisciplinary Governance Council that will be responsible for executing process improvements and sustaining technology advancements towards data governance. The objectives of the data governance council could include the following elements: 

  • Define: Develop the overall Data Governance framework and processes required to be executed and be held accountable for Data Governance discipline 
  • Identify: Identify the areas of focus needed and the associated data elements that will need to fall under governance 
  • Assign: Establish the core concepts of data ownership and data stewardship, as well as the responsibilities that these critical data governance roles entail 
  • Measure: Formulate acceptable Data Quality Service Level Agreements (DQSLAs) for each required data element and create metrics, analytics, dashboards, and reports to measure acceptance criteria on a regular cadence as defined by the DGC 
  • Improve: Implement business processes and/or technology/tools to improve data quality metrics when they do not meet the acceptable DQSLA 
  • Align: Aligning data across the multiple distribution information systems, both in the office and in the field, improves clarity and understanding, especially where confusion could lead to mis-operation of the system.
Icing tastes better on a well-baked cake

The idea of “Grid Modernization” is more energizing and exciting than “Data Governance”, just like eating an iced cake is a lot more exciting than the process of baking the cake (i.e., collecting the ingredients, mixing in right amounts, and baking for the right time). However, no matter how much icing one puts on a poorly baked cake, the cake will still taste unappetizing! Similarly, without implementing robust Data Governance, a Utility cannot move towards successful Grid Modernization. 

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Priyam Chakravarty

Senior Principal – Industry Solutions & Services

Monica Yeung

Managing Director – Strategy & Consulting, Utilities Executive

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