Big Data
Realized:
Developing New
Data-Driven
Products and
Services to
Drive Growth
Perspective by
Waterstone
Management Group
Hubert Selvanathan, Principal
John Zuk, Principal
August 2014
Chicago | San Francisco | Boston
(877) 603-1113
Big Data Realized: Developing New Data-Driven Products
and Services to Drive Growth
A Tested Approach for Technology Companies to Identify, Monetize,
and Commercialize Big Data Offerings
Introduction
“Big Data” are high-volume, high-velocity and high-variety information assets that demand cost- effective, innovative forms of information processing for enhanced insight and decision making.”
– Gartner’s Top 10 Strategic Technology Trends for 2014
After years of excitement—even hype—“Big Data” technologies have reached a level of maturity that make them increasingly relevant and accessible to established enterprises. However, outside of a few specific and well publicized use cases—internet applications, personalized retail, optimized logistics— established technology companies frequently struggle to identify and profitably commercialize new data-driven offerings.
In Waterstone’s experience, many tech companies possess a range of potentially valuable information assets that can fuel innovative Big Data-driven offerings. Unlocking and capturing that value, however, requires a methodical approach to conceptualize, incubate, and launch new offerings. With focus and discipline, organizations can leverage internally generated data to deliver new offerings that provide distinct value for their customers while simultaneously sustaining differentiation for themselves. Waterstone’s research has shown that the successful definition, launch, and scaling of a Big Data offering requires four critical considerations:
1. Use a data-driven, workflow-centric approach to contextualize the offering 2. Plan the monetization approach early
3. Assess the operating model changes required for success
1. Use a Data-Driven, Workflow-centric Approach to Conceptualize the Offering
Successful Big Data-enabled offerings are grounded in rigorous, data-driven opportunity identification. Rather than starting with market trends or customer-needs analyses that often steer other approaches toward “me-too” plays, follow a series of defined steps to: Identify sources of data—what is being produced, what is being stored—across the value chain Determine applicable types of analyses and opportunities to combine and enrich datasets Evaluate the resulting information from three value contexts: productivity (i.e., more efficient
operations), effectiveness (i.e., better quality outcomes), and decision support (i.e., new insights)
Waterstone’s analysis of 80+ Big Data use cases has shown that a range of information assets and data-driven opportunities exists across a company’s value chain as illustrated below.
2. Plan the Monetization Approach Early
Big Data offerings can often require monetization methods that are outside the bounds of the
traditional value capture systems employed by an enterprise. These methods, while typically linked to data usage, must be tuned to match the context and consumption models for the target customer. Typical Big Data monetization strategies can be summarized into four distinct approaches shown below:
Platform Applications Data-as-a-Service Professional Services Description Platform to store,
analyze, and share data, typically as an extension/bolt-on to a core offering Priced typically on a resource consumption model (e.g., gigabits/month) Insights and analytical capabilities are packaged as new apps and sold as an add-on to the core offerings
Priced as a
term-based subscription (e.g., user/month)
Access to data and
insights owned by the vendor; is sold based on the amount of data consumed
Priced on a pay-per
-use basis such as the number of records consumed Insight and analytical capabilities are delivered via a Professional Services engagement that could include access to vendor proprietary data or benchmarks Examples Fluke’s Fluke Connect
offering is an add-on service to its portable test & measurement devices that enables users to save, share, and analyze the data in the Cloud. The offering consists of a smart phone app that extracts data from their instruments and saves it in the Cloud.
Yodlee’s Cross-Sell financial software is sold as an add-on to its core consumer financial account aggregation software. The software enables banks to leverage consumers anonymized transaction-level and account-level data to inform additional relevant bank services or product offers to consumers
Dunn & Bradstreet’s D&B 360 product offers business entity information and contact
demographics in those businesses. Data is delivered into user’s CRM systems based on the record sets they are
interested in. Pricing is based on the number of record sets delivered BlueKai supplements its infrastructure and data-as-a-service products with custom analysis for infrequent events, such as providing detailed
segmentation and predictive models for a new campaign.
3. Assess the Operating Model Changes Required for Success
Launching and scaling Big Data offerings requires careful design of several key elements of the
associated operating model: the go-to-market (GTM) approach, service delivery model, financial model, and operational processes.
A few critical considerations that are unique to data-driven offerings include:
Solution-like nature of Big Data offerings will require solution-selling capabilities.
Vertical industry solutions should align short- and long-term benefits of the Big Data solution with known segment pain points.
Constructing and delivering Big Data offerings will require new and emerging skill sets such as those of data scientists and data architects.
Adjustments needed to operational processes and systems, such as pricing systems, billing, customer support, etc., should not be underestimated.
4. Take a Structured Approach to Launching and Scaling the Offering
An integrated, cross-functional approach is critical to accelerate the time-to-market for the offering. The approach should methodically structure work streams and activities across offering definition, go-to- market approach, service model design, and operations and finance.
Summary
Big Data can fuel innovative new products and services by instigating more informed decision making and insights. Organizations that adopt—and stick to—rigorous, robust new offering methodologies that are properly tuned for the Big Data offerings context can achieve margin-rich revenues and sustained growth.
To help effectively prepare companies to identify and capture new revenue opportunities, senior executives should consider:
Ideation:
Over the last 12 months, has your organization identified opportunities for new data-driven products or services offerings? Have these opportunities been evaluated using traditional new product portfolio assessment tools, or have new evaluation frameworks and criteria been developed?
Design:
Has monetization of data assets been considered as a core or derivative component in any of your last three new product or service launches? If yes, how has the financial performance of those new offerings tracked relative to plan?
Launch:
Have you observed different adoption rates for your data-driven product or service offerings? If these adoption rates were lower than plan, what strategies or tactics have you deployed to help bolster up-take? If the adoption rates were higher than plan, do you know why? Have you codified any learnings from this and shared them across the organization?
Scale:
If you have scaled data-driven product or services, have you encountered unique localization or channel requirements? What customer retention strategies have you deployed? How successful have they been?
To learn more about how to unlock and capture the value of Big Data through the development of new data-driven products and services, please contact:
Hubert Selvanathan, Principal (650) 513-2528
[email protected] John Zuk, Principal
(650) 513-2529
