In Fluturas experience before one starts massively squeezing big data using map reduce and other new age big data processing frameworks, there are 3 important questions which many organisations fail to answer.
1. “What are the right problem to solve using big data use cases?”.
2. “How much is the specific monetary outcome expected from solving the problem?”
3. In layman’s terms – IS THE JUICE WORTH THE SQUEEZE?
Flutura has created a unique methodology which takes this to the next level by codifying specific steps to zero in on the $ denting use cases.
Let’s deep dive into one specific real life example where Flutura is working for in the upstream section of the Oil n gas industry in US. In the upstream side there are a lot of heavy engineering assets increasingly going digital as a result of sensor instrumentation .This results in a lot of bottom up sensor data which is collected from the digital oil field – mud flow rates, CO emissions, vibration sensors all of which are “seen” using historian or out of condition monitoring systems. So what can one do with the sensor data ocean captured for months and years from the various oil fields and rigs ? So what are some of the problems which can be solved using the sensor data use cases?
- Upstream Option -1: SOLVING MAINTENANCE PROBLEM - Can we predict mean time between failures for crucial upstream assets by triangulating crucial patterns in sensor and human generated data?
- Upstream Option -2: SOLVING HSE PROBLEM - Can we reduce probability of blowout by detecting new last mile signals, not seen before? ( Health , Safety and environment dimension )
- Upstream Option-3 : SOLVING COMMAND CENTER ENGAGEMENT PROBLEM - How do I increase the engagement of command center folks looking at the mundane alert screens which impact the last mile ?
Depending upon the market and the maturity of the organisation the flavour of the immediate problem to solve and priority may change. So here they come – 5 penetrative “kick in the gut” questions for getting enough “juice from the squeeze”
- Kick in the gut question-1 – “Making it real”: What is our situation – do we have a problem looking for a solution or do we have a solution looking for a problem?
- Kick in the gut question-2 – “Painkiller or Vitamin” : : Is the problem a “Pain Killer” or a “Vitamin” – Painkillers are problems where the intensity of pain is explicit and experientially felt, whereas vitamins are problems which are implicit and need education in acknowledging the pain ?
- Kick in the gut question-3 “Budget line item”: Is the problem acknowledged and are there line items in budgets allocated to solving the pain ?
- Kick in the gut question-4 : “Modellable data” : Is the problem “modellable” using the data which is available or are there blind spots in the data which
- Kick in the gut question-5 : “Is juice worth the squeeze ?” : Is it REALLY worth it ?
Good luck “Squeezing your big data” from all of us at Flutura … And make sure you solve the right problem J
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