Tapping the Power of Big Data and R&D Tax Credits for Utility Companies

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Current Big Data Landscape

        As a society, we are producing more data than ever before-with 90% of the world's data created in the past two years alone. Ubiquitous information-sensing mobile devices, aerial sensory technologies, software logs, cameras, microphones, and wireless sensor networks all contribute towards the meteoric expansion of our digital universe. Although the explosion of data has stressed the processing capacities of conventional database systems, new systems and approaches have emerged as cost-effective alternatives to storing and processing data.

        There is a tremendous value for organizations in collecting, managing, and analyzing this data. Big data can reveal patterns and trends providing insights into customer behavior, which could improve decision-making at every level. Successfully leveraging big data can increase competitive advantage, provide insights into new markets, help with system optimization, improve security protocols, and much more.

The Research & Development Tax Credit

        Enacted in 1981, the federal Research and Development (R&D) Tax Credit allows a credit of up to 13% of eligible spending for new and improved products and processes. Qualified research must meet the following four criteria:

  • New or improved products, processes, or software
  • Technological in nature
  • Elimination of uncertainty
  • Process of experimentation

        Eligible costs include employee wages, cost of supplies, cost of testing, contract research expenses, and costs associated with developing a patent. On January 2, 2013, President Obama signed the bill extending the R&D Tax Credit for 2012 and 2013 tax years.

Big Data in the Power and Utility Industry

        The power and utilities industry is positioned to realize major gains in productivity by the increase in volume of collected data. Electric grids all over the world are getting smarter. Outage management systems, customer data, and feedback are simultaneously being accessed to improve grid performance and customer service. More and more power and utility companies are transforming themselves into data-driven organizations in order to accelerate the opportunities big data and predictive analytics can create:

  1. Optimize asset productivity:
    With the help of digital sensors and intelligent communication networks, utility companies can receive information about how an asset is being used and allocated, what the condition of the asset is, and how best to utilize resources to manage costs. Using a combination of location intelligence and real-time network monitoring it is possible for companies to reduce asset maintenance costs, improve network reliability, and lower asset replacement costs.

  2. Predict and shorten power outages:
    Power outages during severe weather related events have tremendous implications for utility companies. Information gathered using historical data allows for companies to proactively estimate and prepare for outages. Placing the necessary crew members and equipment closest to the areas that are prone to damage from extreme weather shortens power outage durations. Another way of proactively addressing a potential equipment failure could be social media analytics. Analyzing social media messages, companies can navigate the source of outage and pinpoint where their crews are needed, much faster than traditional systems. Reduction of prolonged outages enhances a utility's reputation with customers as well as regulatory agencies.

  3. Enhance power consumption and prediction models:
    Due to the roll out of smart meters, huge volumes of interval data need to be analyzed more frequently. Predictive analytics on this data allows utility companies to forecast energy usage of their customers in order to match supply and demand more closely. Guiding consumers to move electricity consumption to off-peak hours, reducing the need for dirtier and more expensive power plants is the holy grail of consumption prediction models. Analytics based on smart meters not only allows companies to provide customers with their usage patterns, but also target the "right" customers with new programs, establish time variable pricing models, or even alert customers when usage spikes.

        The U.S. utility industry is beginning to confront intense pressure to achieve cost reductions related to revenue decline while improving service levels. The nation's largest utilities that could potentially benefit from an enhanced call center big data driven machine system are:

Top 10 Electric Utilities in the U.S. (based on Market Value)

top ten electric utilities

        Each utility has differing customer needs based on ratepayer characteristics. Duke Energy and Southern Company located in the South will be addressing more utility air conditioning related issues. Pacific Gas & Electric of California will have more solar P.V. inquiries and Con Edison located in the Northeast will encounter more oil to gas heating conversion inquiries.

Enhancing Customer Service via Automated Conversational Data

        Besides the data generated by smart sensors mentioned above, there are other forms of data that can be used to create substantial efficiencies in the customer service process.

        Utility consumer preferences have undergone a major shift in the type of information they expect from their providers. These empowered consumers want access to information in their own terms, even outside of regular business hours. Online information, frequently asked questions, and site search just aren't capable of delivering the in depth information that customers need to resolve their basic queries. Simultaneously, they seem to have a heightened need for personalized service for more complicated queries. Due to the need for personalized service, utility call centers receive questions related to billing, electronic payments, service outages, installation times, repair service call scheduling, utility rebates, alternative energy programs, smart metering, and a myriad of other issues. To handle the ever-increasing demand for service, companies can hire more client service staff- but is this really the most cost-effective solution?

        Why not automate the process of answering repetitive questions, so service agents can focus on building exceptional customer relationships by providing personalized service? Big data platformed with artificial intelligence and natural language processing capabilities allows companies to cull social media, product databases, private and public databases, online chats, SMS (short message service), public feeds, and the like to rapidly assemble information. This information can be used to dynamically guide customers and enable quick resolution. For example: when a person visits New York City's website to understand the permits required to open a restaurant, an automated AI software can pull information from various agencies including the Department of Buildings, New York City Fire Department, etc.

        After aggregating this information, it can provide step-by-step information on how to apply and schedule inspections based on specific user responses- instantaneously. This sort of dynamic conversation has advantages beyond providing quicker resolutions:

  1. The quicker the resolutions, the more customers are served with fewer resources. These automated conversations with customers can then be mined to gain insights into what issues are most prevalent. Identifying trending topics through data mining is also helpful in identifying new opportunities that the utility companies can capitalize on.

  2. Different metrics can be used to track response and resolution times, conversation quality and length, topic clusters, and customer satisfaction graphs in order to target specific areas of improvement in the customer lifecycle.

The list below describes some developing big data applications for utility's customer service where new algorithms and analytics will need to be developed.

  • Utility Big Data Information Input
  • Weather sensors: monitor wind & water levels and electricity outage locations
  • Customer billing data
  • Customer time of day energy usage data
  • Utility crew scheduling information
  • Current location of utility crew
  • Feedback on customer experience - including social media inputs
  • Utility Rebate Eligibility Standards
  • Smart Metering
  • Alternative Energy Programs

        Artificial Intelligence (AI) machine engines are rapidly mainstreaming. In September 2013, Rocket Fuel, an artificial intelligence machine software solutions provider for the advertising industry, founded by two former NASA PhDs went public and raised $116 million. The company's 2012 sales of $106.6 million increased 139% from 2011 sales.

        Also in September 2013, Toy Talk released a free AI based iPad app called the Winston Show, which aimed at children 4 years and older. The app has already been downloaded more than 100,000 times. It can generate more than 4,000 queries and responses on matters important to children such as "how many licks does it take to reach the center of a Tootsie pop?".


        Firms are constantly exploring new ways to communicate with their customers. The more real-time data a company can provide, the more empowered and engaged a customer becomes. This helps companies not only win customers' trust, but helps drive revenue as well. Data points from this sort of customer interaction can be mined in order to glean insights into customer behavior.

Article Citation List



Lynn Bertrand is a Tax Analyst with R&D Tax Savers.

Charles G Goulding is a practicing attorney with experience in R&D tax credit projects for a host of industries.

Shikshya Khatiwada is the VP of Business Development at Fusemachines

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