A modernized data and analytics program can enable utilities to start using analytics to ride out the disruptive storm and be more prepared for the future.
Utilities are in the center of a disruptive storm right now. At the macro level, such disruptors include digitized economy, digital environments, edge computing, and innovation explosion. On the other hand, digital native consumers looking for digital platforms are impacting business strategies and decisions at the micro-level. Utilities are realizing that they must take the storm head-on or risk losing competitive advantage. Many are seeing opportunities with data and analytics in the Utilities sector. Image analytics is one such opportunity area.
The need for digital transformation in the utility sector is apparent in a McKinsey playbook for utilities, which indicates significant value creation is possible through the digital transformation of operations and systems. It highlights a reduction to the extent of 25% in operating expenses and 20% – 40% performance improvement in critical aspects such as safety, reliability, customer satisfaction, and regulatory compliance. Utility organizations must tap the data residing in their information systems as well as the surrounding ecosystem and turn them into insights to enable such substantial outcomes.
Data is a board-level topic because the opportunities are abundant. In fact, every utility company’s board should be asking:
Is the abundance of data available to utilities companies being harvested for business?
What are their opportunities for improvement across the data value chain?
Is there the required organizational preparedness to support the complexities of modern data portfolios?
As they need to manage massive digital data for their business, utilities have started to lean on edge computing and edge analytics. A robust digital strategy and supporting system architectures are required to process crowdsourced image data provided by consumers reporting hazardous conditions of utility infrastructure.
Role of Image Analytics in Utilities
With the volume of image data generated from crowd sourcing, satellites, drones, GIS, and asset databases, image analytics can be applied to guide the utility sector in its digital transformation journey. By leveraging computer vision, remote sensing, AI, and deep learning, image analytics can identify objects, inspect assets, assess damages, improve detection, and reduce manual intervention across the utilities value chain. Image analytics adds value in the following applications:
Power line inspections: Grid reliability and resiliency demand robust power lines and therefore periodic inspections. Image analytics provides unmatched visibility, location precision and change detection accuracy.
Vegetation management: Automated and machine first processing of data sets from satellite imagery enables detection of vegetation against surveyed power lines in a target geo-area. Image analytics can embed customized utility specific guidelines for profiling vegetation and high-risk areas around transmission and distribution power lines. Data enhancement for further analysis is also possible.
Damage detection: Image processing, over a period, can enable detection of potential risks in power infrastructure and take preventive measures. This prevents unplanned outages, allows proactive asset management, and decreases cost and time in monitoring non-instrumented assets.
Data and Analytics in the Utilities Value Chain
Data quality is essential to this process and must be structured appropriately to generate accurate insights and formulate targeted interventions. Predictive, diagnostic and optimization and descriptive analytics play a crucial role through the value chain of power generation, transmission, distribution, and consumption by consumers. While data helps in addressing complexity through sensing and responding, analytics creates value when it helps utilities organizations to predict and act.
Descriptive utility analytics includes non-core functional ones such as inventory, field crew analytics, load research, and billing analytics.
Diagnostic and optimized analytics examples include plant optimization, fault optimization, advanced metering infrastructure analytics, customer segmentation, behavioral, and experience analytics.
Predictive analytics for utilities provide valuable insights in areas such as renewables forecast, grid reliability, pricing optimization, tariff modeling, load forecasting, to mention a few.
Data and Analytics Stewardship
To leverage image analytics and other analytical capabilities, utilities need to break data silos, ensure credibility of data solutions, modernize their data estates, and enable broad-based access to data. With a plethora of technologies and processes readily available, it is vital for enterprises to understand the organization structure, service methodologies, data governance techniques, maturity roadmaps, and alignment of their use cases with technology patterns to identify the best-fit methods.
The first step for a utility company is to look at data and analytics initiatives holistically and assess their data maturity level by how the data is managed and used across the organization. This step helps to identify gaps in the existing data and analytics programs and becomes the basis for developing specific approaches that plug these gaps.
A modernized data and analytics program can enable utilities to start using analytics to ride out the disruptive storm and be more prepared for the future. The key is to integrate people, process, technology, and data capabilities and drive a cohesive data strategy across the enterprise.
About the author(s)
Amit Bajaj leads the Consulting and Advisory function for TCS Analytics & Insights. With close to 20 years of experience in data & analytics across diversified set of industry verticals, Amit has played a vital role in helping TCS’ customers streamline their data strategies to progressively work towards mature and holistic analytics capabilities. Amit has significant skills and experience across data strategy, intelligent solutions, information management and analytics, and enjoys looking at technology through the lens of business problems.