Resolvingthe Big Data

Dilemma

www.impetus.com

Explore for Hidden Insights or ExecuteBased on Pre-determined ROI?

This white paper talks about ways to resolve organizational tension

which may exist between big data opportunity management and financial investment concerns.

Big Data is a big deal.

But how do we find, fund and monetize the best data-driven opportunities?

In recent times, “big data” has been a big buzzword for a new kind of

large-scale analytics and data processing made possible by extraordinary

computing power, in-expensive storage, and the widespread availability of

disparate and deep data sets.

Today, that buzz is becoming big business. For ambitious enterprises intent on

leading their industry categories, big data is no longer an option – it’s an

obligation.

But as businesses -- in everything from financial services to healthcare,

telecommunication, consumer retail products to luxury, leisure and travel --

look to their data for new opportunities, executives and IT professionals often

find themselves in conflict. In one corner there are big data enthusiasts eager

to explore, to poke and probe the data like gold rush prospectors panning for

hidden treasure. In the opposite corner are the business pragmatists, fiscal

realists who demand a solid business case – a clear demonstration of potential

ROI – before committing time and money to complex or large-scale analytics.

Unfortunately, these conflicting visions have inhibited forward momentum for

many companies. Yet there is a pathway ahead that can reconcile the two

sides. In the following pages, this paper will offer a working model that

satisfies the demand for fiscal responsibility while rewarding the passion for

discovery. By synthesizing open exploration and prudent execution into one

ongoing program for research and discovery, enterprises can fulfill the big

business promise of big data.

Introduction

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Big data means big business

Are there real dollars in the data? For more and more companies, the

answer is a resounding, “Yes!” Just one example: UPS dived into their

logistics and transportation data to explore ways to save costs. As a

result of their analytics, the company is saving more than eight million

gallons of fuel and 80 million miles of driving annually.

Where do you stand with big data?

Attendees of a recent webinar on big data

described their company’s big data

status as:

27%

33%

40%

Well underway

Just beginning

Not close to startingor “not applicable

Ask a finance officer to support a big data initiative and, chances are, you will

hear a desire for clarity, a demand for fiscal responsibility, an insistence on

ROI – in other words, “Show me the money!” For the bottom-line business

professional, the potential for returns must exceed the probable costs in order

to justify investment. To proceed, the big data project needs a business case

that visualizes the risks and rewards.

Responsible Exploration: The Proof of Value Project

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But for the budding new generation of “big data adventurers and

truth-seekers”, the real value of big data is in its ability to capture previously

invisible value, to bring to light opportunities that may be buried deep within

the data. For them, the insistence on a business case holds an inherent

contradiction: How can we assign a numerical value to assets we haven’t yet

discovered?

Hence the frustration: trapped in this chicken-and-egg dilemma, many

businesses freeze when they should be moving forward.

Prove the Value, Make the Case

The desire to explore and the demand for fiscal responsibility need not

conflict. In reality, every business case for any kind of investment, be it for

new resources, new real estate or a new retail channel, demands some

investment in research. In the case for big data, this initial research phase can

be – in fact, should be – an opportunity to explore. You can think of a big data

initiative as having two fundamental stages:

1. Exploration: The business makes a small investment, up front, to explore the data and test for potential business opportunities

2. Execution: If the first stage proves promising, the business makes a larger investment to execute a plan for extracting value from the data

Make a small investment to explore the possibilities first; if fruitful, the results become the foundation of a business case for further investment.

Big Data ProjectExecuteBusiness Case

Exploration,Data R&D!

No Dilemma: Exploration and Execution co-exist

Y

N

One time POV, Ideally ongoing

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$

The initial, small investment stage, typically six to eight weeks long, in which

you explore the data to test its potential can be called the “Proof of Value” or

POV. The Proof of Value project is an exercise in which you verify the data’s

potential to create value by:

• Reducing Costs: Unearth new efficiencies, new ways to optimize operations

• Reducing Risks: Uncover and resolve credit risks, exposures to fraud

• Increasing Revenues: Detect new markets, new segments, new ways to up-sell or cross-sell

• Discovering Possibilities: Find entirely new products, service lines or business models

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To test the possibilities, a POV team of business analysts, data scientists and

IT architects must do the following:

Identify a Business Hypothesis Worth Testing

Call it a hunch, a problem statement or even a dream, the hypothesis is a

business idea, linked to data, whose pursuit represents a goal worth obtaining.

For example:

• “We suspect we can double our leads without increasing our marketing budget.”

• “Overriding false-positives for fraud could increase our credit card revenues.”

• “Mid-process manufacturing metrics can help us boost output yields.”

Transform the Hypothesis into a Data-centric Statement

A business objective isn’t enough. To become a workable analytics test, the

objective has to be translated into a data question. First, the team has to dive

more deeply into the context: Why is this objective related to analytics? Who

holds the relevant data? What data sets could contribute to the analysis? How

are the sets related and how should they be analyzed? Example statements for

the previous objectives might look like this:

• “Analyze lead generation response rates to identify the most productive

channels, offers, media and events.”

• “Review false-positives against individual credit histories to unearth low-risk

fraud overrides.”

• “Establish the relationship between measurement parameters x, y and z

against quality output metrics.”

Establish the Analytics Methodology

This is the domain of applied math, statistical methods and algorithms. In

addition to assembling the appropriate data assets, the POV teams must

define the right ML (machine learning) and/or predictive modeling

methodology for the data analytics statement at hand. This may involve trying

various approaches and selecting the one which delivers the highest

accuracy or the best-fit for the problem at hand.

Go or No-go?

The whole point of the exercise comes at the conclusion: Does the resulting

analysis support a business case with a reasonable expectation of ROI or

does it not?

• Go: Credit Card Finds Overrides Worthwhile

The credit analysis example is based on a real-life case in which a major

credit card company suspected that its fraud model could become

significantly more accurate by reducing the percentage of false positives.

Their hypothesis: Enhancing the model with insights from individual

purchase histories would identify safe overrides. The POV supported this

conclusion, estimating an additional $112M in revenues that would come

with production and deployment.

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• No-go: Mutual fund doesn’t find sufficient “signal” in the data

In another case, a mutual fund provider believed it could generate more

sales among its mid-tier financial advisors if they could leverage CRM data

to more deeply understand their customers’ needs that were embedded in

unstructured data assets like emails, CRM notes and survey responses .

But the POV project could not find sufficient meaningful data to reach that

deeper understanding. Instead of deploying a full-scale big data platform

for mining unstructured data, the company chose to defer that decision

and meanwhile train agents to capture richer information.

As a one-off for big data beginners, the POV serves as the perfect entryway to

getting started: the POV provides a clear rationale for proceeding or aborting,

and serves as the foundation for moving into a pilot program and then into full

production.

Lather, Rinse, Repeat: Turn Exploration into Ongoing R&D

Energy and excellence: For a big data roll-out, this sequence harnesses the excitement of exploration to the accountability of sound business practices.

Proof of Value / ROI

Strategy

Business & IT Aligned

Stakeholders Educated on Big Data Basics

Data Sources Analyzed

Use-case Discovery, Selection, Development

Pilot

Evaluate TechnologyOptions

Solution Architecture

Vendor Evaluation

Pilot/POC - Design and Implementation

Test at Scale

People, Process Planning and Readiness

Production

Design, ImplementBig Data Cluster

Ingest Data from Various Sources

Iterative Analytical Modeling and New

Use-case Development

Develop, Deploy, Manage New Analytics Apps

Successful big data initiatives tend to follow a three-part sequence in which

subsequent phases leverage lessons learned in the previous parts:

Analyzing the Roll-out

Strategy: Bring Everyone on Board

The heart of the Strategy stage is the POV that validates the business case for

proceeding with a big data analysis. Business and IT professionals unite

behind a common business objective; the potential data sources are gathered

and analyzed; and the objective is translated into a data statement ready for

testing. If the POV project is successful, ROI is established and the project

moves to the next phase.

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Pilot: Give It a Test Run

The POC defined the “why,” now the pilot tests the “how.” In this stage, the

business chooses the appropriate technologies, the necessary vendors, and

the most effective solution architecture. The pilot run serves as a Proof of

Concept, a demonstration (or not) that the company has the right people,

plans and processes for executing a successful Big Data Analytics initiative .

Production: Reap the Rewards of a Full Roll-out

Scale up and build out – the production stage expands the lessons learned

from the pilot into full-fledged production. The POV and POC teams transfer

responsibility to operations, where they design and implement the Big Data

cluster; manage the ingestion of data from multiple sources; and develop,

deploy and manage the new analytics applications. A stable production

environment for the new initiative then starts generating the intended new

business value in terms of revenues or cost-efficiencies as planned.

Once You’ve Made Your Case, Make Another

Long term, the real value of analytics comes through dogged persistence. The

big winners of big data will be those enterprises that build a “Data R&D”

infrastructure in which they routinely cycle from exploration through execution,

strategy through production.

POV vs. POC: What’s the Difference?

The Proof of Value (POV) tests the idea,

validates the business objective, and serves

as the gate to the business case.

The Proof of Concept (POC or pilot) tests

the technology, validates the system

(methodology and architecture), and

serves as the gate to full-scale production.

Learn from science-based industries, such as pharmaceuticals and high-tech, who have made research and development an ongoing asset in the pursuit of business value.

Big Data Value Realized

Business Case

Exploration,Data R&D!

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010

Big Data Project Implementation

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010 Business Case

Time $

$

As a system, Big Data R&D works like this:

• Exploration: A team of business and IT and data research professionals regularly propose and define ideas submitted to POVs.

• Case-building: Successful ideas are incorporated into business cases for approval.

• Project Implementation: Approved cases become pilots; successful pilots become operational productions.

• Reap and Repeat: The enterprise reaps the rewards of big data analytics and simultaneously feeds a continuous stream of ideas into the big data exploration hopper.

ConclusionData R&D: What’s the big idea?

In conclusion, big data analytics represents an extraordinary opportunity for

uncovering buried treasure hidden within your data. A few takeaways to

consider:

• No Conflict: Exploration and execution need not be at odds with each

other. In fact, they should both be part of one system of big data analytics.

However, the journey always begins with a small “exploration” investment.

• Step-by-step: A phased methodology of Strategy, Pilot and Production

lowers the risks and costs of investment while increasing the potential of

previously unexplored opportunities.

• Start with one, then do it all over again – and again: Try out the process

with one Proof of Value, one pilot, and one production run. Once you’ve

experienced the power of the process, turn it into an ongoing Data R&D

operation that consistently creates value for your enterprise.

If you’re ready to extract more value from your data, you’re ready for proven big

data and analytics solutions. To learn more about processes that have worked

for clients in digital media, financial services, healthcare, manufacturing, retail

and e-commerce, telecom, travel and entertainment industries, contact an

Impetus big data expert at bigdata@impetus.com

© 2014 Impetus Technologies, Inc.

All rights reserved. Product and

company names mentioned herein

may be trademarks of their

respective companies.

Feb 2014 #81554

Impetus Technologies is a leading provider of Big Data solutions for the

Fortune 500®. We help customers effectively manage the “3-Vs” of Big Data

and create new business insights across their enterprises.

Visit http://bigdata.impetus.com or write to us at bigdata@impetus.com

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