With data volumes growing, extracting business value from that data is becoming more critical but while investments in analytics are growing, those investments may not yield all of the intended benefits.
The latest annual 2018 Bridge Index Grid Analytics survey from utility consultants Bridge Energy Group indicates that while almost two-thirds of the respondents have a data quality programme in place, almost three-quarters are not employing active data governance frameworks for their operational technology (OT) data needs.
Both data quality and governance are essential for analytics initiatives to be successful, Bridge Energy points out. Further, the overall effectiveness of analytics projects is significantly improved when using a defined use case approach versus an enterprise-wide programme.
The survey involved a broad range of approximately 20,000 North American electric, gas and water utility employees. The findings obviously are restricted to that region but given that it has the greatest penetration of utility analytics, the implications are of wider applicability.
Predictive analytics preference
The survey found a significant increase in utilities that are developing predictive analytics capabilities, with asset health and outage management topping the stated reasons alongside customer analytics. For the first time the latter has now been overtaken but nevertheless remains important. However, these have to be implemented into day-to-day operations but the utilities are lagging in this.
Moreover, over half have immature analytics with 11% having no visualisation tools in place. Yet data visualisation is the easiest place to begin a successful analytic journey and for achieving self-service analytics, states Bridge Energy.
As a guide to the level of investments going into analytics, almost half are investing less than $1m during 2018, while 40% will invest between $1-6m. Just 13% will invest more than $10m.
According to Bridge Energy, most of these are investing “far too little to have a real impact on their analytics maturity and usefulness”, while the latter (>$10m) group are embracing analytics as a core competency.
The survey also found that analytics tools are plentiful and capable with their availability indicated as the least likely issue impeding the success of analytics programmes. The top impediments are data integration and the availability of the required skills.
Data analytics challenges
The findings are very much in line with an earlier 2016 analysis by Navigant Research, which indicated that while there are significant differences in the types of driver and their effects in different markets, all utilities in the majority of them essentially have to face two business imperatives: to drastically improve the customer experience and to make operations considerably more efficient.
Since that time two factors with a bearing on analytics have become more significant. One is the potential of the cloud, with its opportunities both for data storage and for analytics as a service. The cloud is for example core to Navigant’s ‘neural grid’ conception as the ‘brain’ of the future grid. The other is the rapid growth of new technologies including artificial intelligence and blockchain, which are opening up new data handling and analytics techniques.
While presenting new challenges these also can support the take-up and implementation of analytics capabilities.