The emergence of new technology and web-based platforms has had a profound effect on the amount of data available, both on and offline. This massive increase in data has significant implications for today’s sales and marketing professionals.

1. Leveraging BI
Technologies to
Lift the Burden
of Big Data

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LEVERAGING BI TECHNOLOGIES
TO LIFT THE BURDEN OF BIG DATA
INTRODUCTION
The emergence of new technology and web-based platforms has had a profound effect on the amount of data
available both online and offline. This massive increase in data has significant implications for today’s sales
and marketing professionals (Figure A).
Almost every company endeavors to leverage and better understand this expanding universe of data, to
better manage their business, but many struggle to do so. One major reason for this is that they lack a solid
strategic and business outcome focused framework for managing their data and analytics plans. The
companies who are successfully able to leverage this data are able to better understand how customers
make purchase decisions, to intervene in the most relevant ways to impact those decisions, and then to
measure these results to continue to drive competitive advantage.
In this whitepaper, we will discuss technology’s impact on sales and marketing analytics and business
intelligence by examining the difference between analytics today vs. the future, the business user implications
of these technological changes, and recommended actions for executives to harness these exciting
opportunities.
SALES AND MARKETING: TODAY VS. THE FUTURE
One of the biggest changes we envision impacting the sales and marketing analytics space in the coming
months and years is technology. We see the growing power of the cloud, coupled with the steadily increasing
volume and accessibility of data as a key turning point in the evolution of business analytics.
The vision sales and marketing professionals have of analytics has not really changed in that the goal is to
use data and reporting and modeling to make better business decisions more in tune with the continually
evolving needs of their customers. That said, the reality of business analytics and reporting has frequently
fallen short of this goal for a variety of reasons, a primary one being technological limitations in our view.
That is all changing now with these emerging technologies that allow for more flexibility, speed to market, and
scalability than ever before. Three areas companies can expect to see the biggest changes as a result of
these technological advancements include time horizon, business intelligence and infrastructure footprint.
1. TIME HORIZON
• TODAY: Output remains heavily historically focused.
• FUTURE: Output is shifting to become real-time and future-focused.
2. BUSINESS INTELLIGENCE
• TODAY: The definition of “business intelligence” has devolved into series of slice and dice reports.
• FUTURE: “Business Intelligence” will begin to encompass its original meaning and intent – true
decision support
3. INFRASTRUCTURE FOOTPRINT
• TODAY: Analytics have a heavy infrastructure footprint and high IT involvement.
• FUTURE: Analytics will have light infrastructure, be user-driven as opposed to IT managed, and be
based in cloud distributed models.

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TECHNOLOGY’S IMPACT ON SALES AND MARKETING
The way that we think about Analytics technology is through a “stack” which connects data all the way
through reporting and business intelligence. Our perspectives on how technology is impacting each
component of this “stack” are as follows (see also, Figure B):
DATA - Variety of in-house and external sources (Omniture, Marketo, Microsoft
Dynamics)
When considering the impact of technology on data, the first thing a business user should be thinking about is
the emergence of “Big Data”.
Fundamentally, “Big Data” means large datasets that are difficult to store, manage, and work with. In order to
best understand the impact for business users, it is helpful to take a step back and look at the differences
between “traditional” and “Big Data”.
With “traditional” data, the interactions with customers are episodic or periodic (i.e., once a month, once a
year). While traditional data answers the question “what happened in the past”, traditional data can’t answer
how customers are using a product or service in the interim – (i.e., how are they using, thinking and talking
about it?).
With the emergence of Big Data, the amount of information and insight available about a customer greatly
increases, making it possible for Best-in-Class companies to monitor these activities and start to connect with
customers in real-time.
FIGURE A. - TRADITIONAL VS.
EMERGING DATA SOURCES

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What Does This Mean for Business Users?
The exponential increase in data is both a huge opportunity and a potential impediment. In analytics, one of
the most common pitfalls professionals make is gathering data simply because it’s available. The reality,
however, is that not all data needs to be collected, stored and aggregated. Best-in-class firms are able to turn
Big Data into an opportunity by adhering to a systematic and consistent process. Figure C shows an outline of
critical questions business users must ask when determining which data to collect and analyze.
FIGURE C. - DETERMINING DATA
TO COLLECT AND ANALYZE
FIGURE B. - DATA STACK

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DATA MANAGEMENT - Design, build and manage databases (Oracle, MySQL,
Greenplum)
The next level of the “stack” is data management. Here, the largest impact technology has had is a significant
increase in processing with corresponding decreased costs. It is important for business owners to understand
the current state of data management which we view as a combination of both expanded and enhanced
relational systems and a migration to existing and emerging data warehousing systems. Figure D gives an
overview of the benefits and pitfalls of using traditional relational systems and/or data warehousing systems.
What Does This Mean for Business Users?
Data management can be a huge investment (in some cases millions of dollars). Before taking the plunge and
investing in data management software, business users must define the outcomes they need the system to
accomplish:
A stable, flexible data environment. The system needs to be able to accommodate the data identified.
Acceptable amount of time for generating results. Time is a huge driver of system functionality and cost,
but the faster a business wants the system to render data, the more expensive it will be. Business users
must be deliberate and thoughtful about the amount of time needed vs. wanted when making their
decision.
Seamlessly generated output for one of two purposes:
- Datasets for advanced analytics (statistics, predictive modeling, etc.)
- Datasets for basic analytics and reporting
ADVANCED ANALYTICS - Descriptive stats, causal & predictive models (SAS, SPSS)
With the emergence of new technologies, the utility of Advanced Analytics tools has greatly increased. While
the core elements of these tools are not changing drastically, the world of Advanced Analytics continues to
evolve and expand.
As Data Management platforms continue to evolve and improve, the amount of information and insight that
can be derived via queries and basic tools like Excel is growing. Proprietary platforms such as SAS and
SPSS continue to evolve their offerings while open source platforms such as R continue to grow.
FIGURE D. – BENEFITS & PITTFALLS
OF TRADITIONAL DATA SYSTEMS

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There is also a significant focus on embedding next best action capabilities into CRM tools. A
convergence between tools and analytics in the CRM world is helping users not just learn additional
information about customers, but incorporate real-time analytics to help sales reps decide what to
do next with each customer.
What Does This Mean for Business Users?
Advanced Analytics tools are very powerful; however they are also over-purchased and underused
assets. Just because your organization owns a suite of SAS licenses does not mean that effective
analytics or anything remotely close to them are being conducted. Figure E outlines critical
questions for avoiding a breakdown and developing a clear process to establish Advanced
Analytics needs.
REPORTING AND BUSINESS INTELLIGENCE - Reporting & BI solutions (Dundas,
Cognos, QlikView)
One of the greatest sources of frustration that analytics and business professionals alike encounter
is dissatisfaction with Reporting and BI tools. However, technology is quickly evolving in this space
bringing enhanced versions of traditional enterprise systems as well as new, emerging technologies
and approaches. Both Traditional Enterprise and Emerging In-Memory Reporting Systems have
positive and negative aspects, outlined in Figure F.
What Does This Mean for Business Users?
In many respects, none of the other analytics elements matters if the BI and reporting engine falls
apart, as it frequently does. Data and insight are only as good as an analytics team’s ability to
deliver it and as a result the business users’ ability to act on it. Figure G outlines several strategies
for building a successful reporting and BI function.
FIGURE E. – AVOIDING A
BREAKDOWN

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RECOMMENDED ACTIONS
The development of an effective analytics strategy is a significant obstacle to building an effective
foundation for supporting critical data-driven business decisions and as highlighted here rapidly
evolving technology provides but a significant opportunity but also a challenge for firms lacking a
coherent, integrated analytics strategy. Following the steps below or something closely
approximating them, firms can begin to develop a strategy today that will give their organization the
ability to make timely, cost-effective business decisions built on fully leveraging and taking ad-
vantage of the exciting technological innovations occurring in this space:
Restate and clarify business objectives as they relate to sales/marketing and analytics; how is
the business acquiring, growing, or retaining customers?
FIGURE F. - BENEFITS & PITFALS OF
TRADITIONAL ENTERPRISE AND EMERGING
IN-MEMORY REPORTING SYSTEMS
FIGURE G. - STRATEGIES FOR
BUILDING A SUCCESSFUL REPORTING
AND BI FUNCTION

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ABOUT DEMANDANALYTICS
TM
Our DemandAnalyticsTM methodology integrates customer, marketing and channel data to drive customer performance and
profitability and optimize sales and marketing investment. By integrating our suite of solutions with best in class technologies, we
capture and leverage data from a wide range of customer interaction touch points – including online digital footprints, campaign
responses, and point-of-sale purchases. To learn more about how DemandAnalytics™ can help you acquire, penetrate and retain
new customers, and for more examples of our analytics work with clients, visit our website, www. market-bridge.com, or contact us at
marketing@market-bridge.com.
ABOUT MARKETBRIDGE
MarketBridge is a leading global provider of technology-enabled sales and marketing methodologies and solutions for Fortune 1000
and emerging growth companies. The firm offers the following methodologies: MarketBluePrint, RevenueEngines, and
DemandAnalytics. We work closely with CEOs and their senior Marketing and Sales leaders to solve their toughest business
challenges and create scalable, sustainable competitive advantage. Visit our website, Market-Bridge.com.
Headquartered in Bethesda, MD with offices in San Francisco, MarketBridge works with partners and associates across the globe to
build and execute our clients’ initiatives.
www.market-bridge.com • 1-888-GO-TO-MKT
Develop an 18-month analytics roadmap; understand the capabilities needed in place and
leverage emerging technology to get there
Establish a clear data strategy; determine why new data sources are critical for the business
(i.e., why is social media critical to the business and how will it drive ROI?).
View analytics through an ROI prism; while there are many exciting technologies coming onto
the market, it is important to clearly define how they will help the company make money.
Leverage existing tools; understand that emerging technologies do not mean existing tools must
be tossed. Determine how existing investments can be adapted to emerging and changing
needs.