BIG DATA ANALYTICS the move towards rap id exper imentat ion

Tracey Moon | Naresh Agarwal

Tracey Moon, CMOTwitter : @tmoonliveLinkedIn : linkedin.com/in/traceymoonEmail: tracey.moon@brillio.com

Naresh Agarwal, Head of Information Management & Big DataTwitter : @naresh2204LinkedIn: linkedin.com/in/nareshaEmail: naresh.agarwal@brillio.com

Today’s Brillio Panel

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Let the data work for you to solve real business problems.

Setting the ContextValue from Big Data is well established, but very few enterprises are actually connecting insights to high confidence decision-making

Key to success is being able to ask real questions, and establish this massive quantity of data that can bring change that truly matters

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In this session, you will learnCommon challenges we hear from customers regarding Big Data projectsRealities that are driving the need for rapid experimentation around Big DataHow to setup your own rapid experiment

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What we are SeeingCompanies adopting technology and “rushing” towards Big DataInnovation is superseding decision-making The paradigm shift from ‘Known Known’ world to ‘Unknown Unknown’ world

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Focuson the business problem, not the technology.

Easier said than done.

Each organization is unique and has its own culture, challenges, people and secret sauce

Companies adopting technology and “rushing” towards Big Data

Too much emphasis on tools and technology

Technology is not the “silver bullet” for your business problems

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What is the cost of NOT doing anything while the competitor moves forward?

Rapidly evolving big data analytics market

Innovation superseding adoption

Time horizon of decision makingis much more, causing imbalance

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Vague business case and mismanaged

expectations

Shift from ‘known known’ to ‘unknown unknown’ world

Complex business problems

Continuously evolving problem scope, data, technology and methodology

known

unknown

Paradigm shift to ‘unknown-unknown’ world

unknown

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The Answer is Rapid Experimentation1 Not all big data efforts will generate ground-

breaking findings2 The key is to work quickly on a number of fronts3 Some of the findings will lead to insights that

impact the business

“The challenge is not about designing a data lake or otherwise for a business case that is clear, but the challenge is about building an ecosystem that will help you find the big idea that results in a $200m benefit.“

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“Commit to an experimentation mindset”

How you should setup your Rapid Experimentation

THE PLATFORM “the enabler” aka ‘Knowledge Repository- strategic platform should consist of data, tools, people- ability to spawn self contained analytics environment

THE EXPERIMENTRemember- its the mindset- not all experiments will yield positive ROI- make it real- once proven, make it scale

EXPERIMENTATION CYCLE

DESCRIBE

DEVELOP

REFINE

PROVE

SCALE

VALUE

Client Challenge

Our Approach

Result

Predict parts needed for replacement based on customer’s description of appliance problems.

Deep categorization of appliance problem symptoms, systematic identification of physical causal factors and linkages with service events & delivery chain that drive overall servicing cost.

Improved prediction accuracy to 82%, making in viable to implement in real life, resulting in annual savings of $9.5MM per year

.

What Rapid Experimentation Looks Like

Proved value of experimentation

Implementation ready

Measurable benefit

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Questions?

Check our blog for more updates :www.brillio.com/insights

Watch for announcements regarding our next webinar:

“Designing the Knowledge Repository”

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