Establishing a Scalable and High-Performance Data Analytics Infrastructure for the Utilities

With respect to some statistics for the global utility analytics market, the investments are expected to be in the range of USD $3.8 billion to $4.2 billion (by 2020). With these statistics, I can presume that adopting analytics in the energy sector is going to define the value quotient of suppliers – an upgrade in technology that cannot be ignored for too long.

Some immediate processes to benefit from data analytics would include improved utility system planning, improved infrastructure resilience, evasion of outages, implementation of demand-response, improved asset monitoring and management, proliferation of value-added services with newer business models, and better understanding of customer usage patterns and preferences.

The first step for the energy suppliers is to immediately revisit their infrastructure and get answers for the following questions:
Is the utility infrastructure capable of capturing and analysing the avalanche of data, including:
The AMI data from smart meters with the hiking sampling frequencies
The distribution automation data, from grid equipment (substations, transformers) facilitating real-time monitoring and control
The third-party data sets from off-grid components presenting demand response pricing data, weather data or the energy generation forecast.
The smart device data helping in predictive monitoring and maintenance of the grid assets.

Once the need to upgrade to a big data scalable and analytics-enabling infrastructure is confirmed, the next set of questions include

Which data is relevant?
How to integrate data from the systems-in-silos?
How to ensure regulatory compliance for data storage?
Is the captured data collected in real-time to justify real-time analysis use cases?
How to avoid rip-n-replace of current systems, ensuring a quick and smooth upgrade to a big data scalable and analytics-friendly infrastructure?
Which technology systems one needs to invest in, to capture and analyse data?
Should one hire the right skilled resources in-house or engage external consultants to manage an aided shift?
Which vendors should one engage with – The IT providers already in the ecosystem, The OT vendors or the Pure-Play solution providers?

With this series, I would like to first discuss the set of questions in and around the vendor ecosystem, resting the discussion around solutions for the next upcoming part.

The vendor engagement options, suppliers have today include

Developing in-house systems, offering flexibility to shape-up tailor-made solutions with lower external dependencies, but, thwarted by challenges of skill and resource gaps. – Not every utility is housed in the Silicon Valley.
Engage with the OT (Operational Technology) vendor, offering pin-point solutions for setting up analytics.
Rely on analytics offered as a component of the pure-play solution providers, evading the need to establish additional systems, restricting the scale of analytics to specific product work zones.
Engage with traditional IT vendors, already in the ecosystem delivering enterprise applications which form a part of the smart grid infrastructure.
Outsource the analytics delivery to third-party providers, with the limited choice of vendors as of now while managing data regulatory concerns first-hand.

Either of the above can be a viable choice, post budget analysis. One can set strategic alignments look-in and buy-in from the internal teams and management.

Once the vendor choice is made, one needs to ensure maximum ROI from the analytics infrastructure. To map the ROI, it is necessary to understand the new possibilities that effective analytical solutions offer and if the invested solutions can do complete justice to the same.

Catch up with the potential data analytics use cases and revenue opportunities of analytics in the next series!