Resolving
the Big Data
Dilemma
Explore for Hidden Insights or Execute
Based 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
2
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 starting
or “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.
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 Project Execute Business Case
Exploration, Data R&D!
No Dilemma: Exploration and Execution co-exist
Y
N
One time POV, Ideally ongoing
0101010101 0101010101 0101010101 0101010101 010
$
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
4
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.
• 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 Technology Options Solution Architecture Vendor Evaluation Pilot/POC - Design and
Implementation Test at Scale People, Process Planning and Readiness
Production
Design, Implement Big 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.
6
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! 0101010101 0101010101 0101010101 0101010101 010
Big Data Project Implementation 0101010101 0101010101 0101010101 0101010101 010 0101010101 0101010101 0101010101 0101010101 010 0101010101 0101010101 0101010101 0101010101 010 0101010101 0101010101 0101010101 0101010101 010 0101010101 0101010101 0101010101 0101010101 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.
Conclusion
Data 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 [email protected]
© 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 [email protected]
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