From smart meters to equipment sensors, energy companies are gathering data from connected devices at an unprecedented rate. But as data grows—and BI, predictive analytics and artificial intelligence practices evolve—energy companies face a challenge: Innovation intended to improve the customer experience or operational efficiency can easily conflict with privacy laws, corporate data guidelines and regulatory mandates. So, how can the energy industry balance this data deluge with regulatory compliance and data protection laws?
Given the amount of data being collected daily, there is a growing likelihood that organizations will “cross the line,” either intentionally or, as in most cases, unintentionally—especially as they begin to leverage new technology like big data analytics, artificial intelligence (AI), machine learning (ML), Internet of Things (IoT), and sensor-driven devices. So, as these technologies and use cases evolve, regulatory requirements must also shift in response.
Regulatory agencies are tasked with protecting consumer privacy, enforcing existing regulations and constantly updating codes to adapt alongside changing technology, but they still require a healthy balance to maintain innovation: If agencies overregulate, they may unnecessarily hamper growth; too little, however, and they risk failing the public or corporate trust.
A Data Governance Scenario
Imagine a scenario where a customer service portal collects user preference data and shares it with an enterprise resource planning (ERP) system to enhance billing options (as intended). Later, that same data might be accessed through APIs to the ERP by a field service automation (FSA) solution, and then again accessed through other systems.
In this scenario, there hasn’t necessarily been any breach of data privacy—but one can easily see how extending data to other systems or to data stores meant for reporting and analytics can be risky. Sound data governance should prevent this from happening, but as development teams grow or go offshore, it becomes increasingly more difficult to ensure data accessed across systems is always protected.
Data Strategy & Governance—What It Is and Why It Matters
First, a recap on data governance: Data governance helps ensure formal data management within an organization, and also guides data stewardship and quality to help organizations gain control of data assets. Data governance typically includes methods, technologies and behaviors related to proper management of data like security, usability, integration, compliance and the overall management of the internal and external data flows.
Historically, energy companies have done a much better job of “collecting” data than leveraging data. When utilities employ analytics initiatives, it’s often to look backward and create static reporting instead of looking to the future. But as this reverses and analytics become more complex, data strategy and governance will need to evolve quickly.
GTM research predicts that global utility companies will grow investments in data and analytics to $3.8B by next year; most believe the energy sector, and particularly utilities, will completely restructure over the next decade, in part due to capabilities that come with new sources of data and connectivity to the end user. This massive change to operating models will require well-defined strategies for data management, analytics and adherence to regulatory changes.
A Look at Modern Technology & Data Governance
While data governance is important, it should not hold back innovation or take it hostage. Modern technologies usually work best when used in concert with other technologies—like using blockchain to secure data generated by non-traditional energy producers (solar, wind, private), or integrating machine learning and semantic processing to configure energy storage or retrieval options via an intelligent chatbot. But for these kinds of “breakthrough” use cases, developers and companies need room to experiment and innovate; seasoned developers and architects are more likely to resolve potential issues during design or development phases, so data governance should have some flexibility in these phases.
As Jeff Goldblum famously said in Jurassic Park in 1993: “Life finds a way.” Transformational technologies, if employed to the advantage of both customers and providers, will continue to evolve and contribute integrity to the data framework. Software developers and data architects will specialize beyond coding languages and platforms, focusing more on certain industries or business models where they can differentiate through knowledge of non-technical challenges. And new data governance approaches will have to be considered to allow innovation and protect data simultaneously.
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