July, 22, 2017
Building a Data Analytics company for Industrial Sectors
16 Jun
2017
Posted by Poornima Kavlekar

When Krishnan Raman quit his job at Mindtree in 2011, there were three questions that intrigued him. The answers led him and his co-founders to establish Flutura Decision Sciences and Analytics.

“We were obsessed with three core questions,” recalls Krishnan Raman, co-founder, Flutura Decision Sciences and Analytics, reminiscing the time when he and his colleagues, Derick Jose and Srikanth Muralidhara, decided to start a venture together. “The questions were: how do we transform industries by selling the ‘monetise data’ offering directly to business leaders (who think about impact) as opposed to IT organisations (which thinks tools)? How do we scale using a product as opposed to traditional method of scaling analytics using data scientists? And how do we earn the trust of customers/employees and transform them in the process?”

The trio set out to get answers to these core questions through detailed research and they gathered some powerful insights. Raman says,“We discovered that customer data had been monetized using analytics whereas machine data had not been ‘juiced enough’.” For example, there were many boutique retail and risk analytics companies but they could not locate a single oil and gas-focussed analytics company. This insight led them towards the IOT arena.

While they identified the sector, they had to choose the vertical that needed focus. They narrowed down to energy and engineering verticals as these industries had a lot of machine data that was not explored and the headroom for operational transformation was huge. Raman cites an example: “We worked with a chemical process company where valve failures led to a loss of around US $33 million in revenue, and they found out that data based diagnostics could bring down that cost by 40 percent.” Another example was that of a fracking company in Houston where introducing remote digital diagnostics of fracking assets could dramatically reduce downtime (which translates into more oil being produced) and reduce cost of relocating troubleshooting staff to remote oil and gas fields.

All these factors led the three to set up Flutura Decision Sciences and Analytics in 2012 as an IOT intelligence company that is powering new monetizable business models using machine signals in the engineering and energy Industry. It aims to have an impact on the operational process and asset efficiency outcome of industries. Funded by The Hive, a pure play big data fund based out of Palo Alto, the company has main offices in Houston and Palo Alto in the US,and in Bengaluru, India.


The primary challenge we faced was in shifting traditional engineering mindsets intothinking for the digital age. There is a lot of change management required at the executive level  which extends the sales cycles sometimes


What does it do?

The IOT prognostics/diagnostics platform from Fluturafills a very important gap in the market by detecting the undetected machine signals that impact industrial outcomes. It does so by mining and streaming IOT sensor/asset/operations data using its data science platform – Cerebra.

With 5 people in 2012, the company has grown to become a 70-member product team with more than 30 customers. The company, which was accelerated by Microsoft Ventures in the Hi-Po Scale-up program, has established partnerships with firms including Intel and Hitachi for product and market access support.It is executing projects in Houston, Dallas, Paris, Dusseldorf, Tokyo, Singapore,Shanghai, Calgary, Toronto and Barcelona. In terms of industries, it is focussed on oil andgas, power, OEM and heavy process industries.

While it has come a long way since inception, the company faced many challenges along the way, primarily in shifting traditional engineering mindsets into digital age. “There is a lot of change management required at the executive level, which extends the sales cycles sometimes,” recalls Raman.

However, overcoming many of its challenges, the company has created a ‘per asset business model’, where the company charges a client, per year, per asset, whose signal and data it analyses.

Flutura’s future

Fluturahas raised US $7.5 million (around Rs. 50.2 crore) in funding led by Vertex Ventures, Lumis Partners and The Hive, an existing investor and a Big Data startup fund. The company will utilise these funds toexpand its markets in the US, Europe and Japan, deepen its current products and build a global brand for Flutura.

And these plans have been made at the right time since the industrial sector is at an inflection point where digitalisation is disrupting fundamental process/product design and transforming business models.

The company is all set to help its customers take advantage of this environment. This apart, the company believes that there will be accelerated demand emerging from countries such as Germany, France, US and Tokyo, where a heavy concentration of industrial energy and engineering companies exist. “We see ourselves as the leading, deeply virtualized I-IoT Intelligence platform in Energy and Engineering verticals,” says Raman on a parting note.


Problem:

  • How do we transform industries by selling the ‘monetise data’ offering directly to business leaders (who think about impact) as opposed to IT organisations (which thinks tools)?
  • How do we scale using a product as opposed to traditional method of scaling analytics using data scientists?
  • And how do we earn the trust of customers/employees and transform them in the process?

Solution: Flutura uses IOT intelligence to power new monetizable business models using machine signals in the engineering and energy Industry. It aims to have an impact on the operational process and asset efficiency outcome of industries. The IOT prognostics/diagnostics platform detects the undetected machine signals that impact industrial outcomes. It does so by mining and streaming IOT sensor/asset/operations data using its data science platform – Cerebra.


Snapshot

Flutura Decision Sciences and Analytics

Year: 2012

Headquarters:Houston and Palo Alto in the US and Bengaluru, India.

Funding: The Hive, Vertex Ventures, Lumis Partners

Business Profile:It is an IOT intelligence company that is powering new monetizable business models using machine signals in the engineering and energy Industry. It aims to have an impact on the operational process and asset efficiency outcome of industries.


Critical success factors

-         Rate of customer adoption

-         Revenue growth

-         Establish innovative deal structures and pricing models

-         Embedding critical features into the platform

-         Fostering a start-up culture as the company grows


Matters of Technology

There are 3 core layers in Cerebra

Layer-1: STORE layer

Where a huge mashup of sensor signals, maintenance tickets related to past asset care and ambient conditions are integrated into a central data lake, governed by Cerebra’s unified I-IoT Data model

Layer-2 : SENSE layer
Here deep learning algorithms run through the various electrical, mechanical, hydraulic, thermal, acoustic, vibration signals and uncover patterns buried deeply in the data which human eye cannot detect.

Layer-3 RESPOND layer

Here a specific action is triggered. For example, it could be an automated ticket generation in response to a fault signature which was encountered

There are 4 types of applications, within the Cerebra platform:

  1. State Assessment Appsare those that give the pulse of the asset or the process being monitored
  2. Diagnostic Apps which run a battery of physics and statistics based tests to isolate what went wrong with the asset or the process
  3. Prognostic Apps which predict the probabilistic likelihood of any failure
  4. Benchmarking Apps which help an OEM benchmark the performance of assets across installations and customers

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