We came across a comprehensive big data report which rank ordered Industry verticals on 2 dimensions
1. Big data spend
2. Business ROI realized
How did the the Utility industry fare ?
On the dollars spent in Big data, the Utility industry was ranked 4 th largest spender after Telecom, Healthcare, Retail . Whereas on the ROI realized it was ranked No 1.
What this implies is that even though they were spending relatively less on big data, the ROI realized is massive. There is a lot more headroom for growth in monetizing Utility data.
Over the last 2.5 years Flutura has deeply listened to the needs of the Energy markets globally while executing big data projects to solve business problems. We have synthesized 3 fast converging trends + 10 real problem statements at various stages of maturity.
SO WHAT ARE THE KEY TRENDS INFLUENCING THE ENERGY MARKET PLACE ?
The 3 D s - Irreversible forces which are redefining the energy markets are
1. DEREGULATION : Emergence of Retail Energy Providers in markets like Texas, NJ etc
2. DECENTRALIZATION: Emergence of distributed power from Wind / Solar where a Consumer transitions to being a Prosumer
3. DIGITIZATION : Increased instrumentation of the grid with sensors, SCADA and smart meters which emit unprecedented data with monetizable patterns buried within them
WHAT OPPORTUNITIES ARE UNLOCKED BECAUSE OF THESE 3 DS ?
Fluturas specialist team of Energy Data Scientists then segmented the use cases into 2 categories
1. Energy big data use cases which HAVE a Business Case
2. Energy big data use cases which DO NOT HAVE a Business Case.
We are happy to share our experiential list of Energy big data use case which have a business case and warrant an investment in big data applications.
1. How to Price energy in deregulated markets ?
2. How do we stabilize micro-grids power quality by deploying assets ( capacitor banks for example ) ?
3. How do we reduce last mile revenue leakage in distribution by detecting payment and power leakage in real time ?
4. In deregulated markets, how do we segment energy usage for commercial/industrial customers to detect signals for selling value added serviceslike outage insurance, energy audits and asset refinance ?
5. How do we minimize outages by doing detailed forensics by triangulating power , asset and ambient data ?
6. How do we arrest customer churn in deregulated energy markets by harvesting signals from power quality data pool, outage systems, billing resolution signals , customer experience signals,customer life time value etc ?
7. How do we minimize spot energy purchase ( which is quite expensive ) by aggregating bottom up load curves into the energy forecasting process ?
8. How do we gather granular asset intelligence to drive budget allocations to replace/refurbish grid assets ?
9. How to reduce peak power stress on grid and reduce need for expensive peaking plants by influencing customer actions driven thru bill and promotions (A/B tests to figure out action resonance )
10. How to enhance customer experience( brown outs /black outs /billing errors) by having a holistic 360 degree view of their energy usage profiles, outage experience metrics, billing patterns, call center recency etc ?
Depending upon the market and maturity, the rank order of the problem statements keep changing. If you are at Distributech in San Diego please stop by. Tony who is from our Houston office would be happy to show live demos of how each of these problems are being solved using Cerebra Nano Apps.
And please remember Data is a liability before it becomes an asset !
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