1. Empowering Business
through Big Data Analytics
Introduction
There is a new economy emerging, an economy based on
data. This data is being generated, stored, sold, consumed and
protected at a level commonly reserved for precious metals
and currency. Companies gather this data every time they
interact with a customer, partner, provider or competitor.
Why is this data so valuable? Because companies can use it
to better understand markets, customers and competitors
and therefore greatly speed their time to market and quality
of delivery.
However, the data being generated today is more complex
than ever, with more unstructured components. Therefore,
organizations must discover and evaluate new technologies
and paradigms for analyzing the data and using it to make
decisions. In particular, the business side of an organization
must clearly define a small number of use cases to enable the
information technology (IT) team to deploy technologies that
meet the needs of the business.
This white paper outlines those key use cases for each of
the common divisions in an organization and explains how
big data analytics can help drive more effective strategies and
decision making.
How data is produced and consumed
Two types of employees are involved with analytical
technologies (see Figure 1):
• The Analytical Consumer uses tools like visualization and search to
view complex data sets, complex relationships and interpretations of
cause-and-effect relationships, and uses these capabilities to drive
decisions about how to operate the business and impact the bottom
line in a positive way.
• The Analytical Producer uses advanced technologies like machine
learning and natural language processing to produce the
analytical models that the Analytical Consumer uses to analyze data.
The Analytical Producer has a skill set more tilted towards technology
capabilities and their applicability to complex data sets, while the
Analytical Consumer is an expert at the business.

2. Analytical consumers and analytical producers
Analytics
Sophisticated NLP, machine learning, predictive modeling,
sentiment analysis, social network analysis, and visualization,
No Hadoop/MapReduce programming expertise required.
Analytical
producer
Analyze unstructured,
structured and semistructured data from a
single work bench
Search
Interactive and intuitive. Search interface allows business
analyst to explore and exploit all data resources.
Analytical
consumer
Visualization
Interactive web-based authoring empowers business users
to perform analysis, visualize results and take decisions.
Analytics is
about enabling
effective decision
making and
measuring impact.
Figure 1. The Analytical Producer uses advanced technologies like machine learning
and natural language processing to produce the analytical models that the Analytical
Consumer uses to analyze data.
There is a corresponding distinction
between analytics and big data:
• Analytics is about the data and its impact
on the business, about putting in the proper
data models, algorithms and tools in place
to manipulate and understand the data in
a way that drives effective decision making.
In other words, analytics is about enabling
educated decisions and measuring impact
(see Figure 2).
• Big data is about the infrastructure,
about ensuring that the underlying
hardware and software have the ability to
enable analytics. Big data is about SLAs,
performance and speed.
Often, analytics and big data are spoken
about together because modern
infrastructure technologies are needed
to drive the new types of analytical
technologies available. They have a clear
impact on one another; in fact, big data
is driving newer analytical technologies
to enable organizations to take full
advantage of the insight contained in
their data.
Different divisions in an organization
face different business challenges
(see Figure 4). Let’s look at how analytics
can help each division improve the
organization’s bottom line through
better decisions, better measurement
and faster response to changing
market conditions.
Analytics and big data
Measuring impact
Integration
Curation
Understanding
Decision
Figure 2. Analytics is about enabling effective decision-making and measuring impact.
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3. Big data is the plumbing; analytics is the business context.
Figure 3. Big data provides the performance and speed to enable and drive analytics
Employee retention
More companies today are looking
for ways to ensure that high-quality
employees are effectively engaged so
that they stay at the company, lowering
costly turnover and project transitions.
Many companies today struggle to
engage with high-caliber employees
until it is too late and the employee has
given notice to leave the firm. That is, by
the time they realize a high performer
is unhappy, it is too late to make a
meaningful change. With the current
state of the job market, highly qualified
staff can find new positions very quickly.
Analytical tools, such as Dell™ Kitenga™
Analytics Suite, enable HR departments
to more effectively analyze whether
employees feel engaged with the
company, their managers and their
positions. By pulling in records from
employee surveys, interactions
with management, performance
information, social media information
and previous employee retention data,
HR departments can create models that
predict which staff are likely to leave the
firm and should be proactively engaged.
Customer retention
Because customers can switch
providers faster than ever today,
companies must compete harder to
retain their customers. To identify
customers likely to leave, a company
Key use for analytics and big data
HR
Customer support
Sales
Spend analysis
Retention
Marketing
Upsell
Competitive analysis
Due diligence, intellectual property analysis
Increase business
Decrease costs
Figure 4. Each division in an organization faces its own business challenges.
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The Kitenga
Analytics Suite
model proactively
identifies events
and experiences
that affect customer
retention.

4. must create a 360-degree view of
its customers, their interactions, and
the leading indicators that might mean
customer loss is imminent.
Kitenga
Analytics Suite
enables companies
to analyze
complex sets of
data to identify
potential buyers.
The natural language processing
capabilities in Kitenga Analytics Suite,
combined with the scalability
of Apache® Hadoop®, enables
companies to combine disparate
data sets—including CRM, support
records, component failures, field
dispatches and other events—into a
single model of how customers react to
changes in their experience. This model
can be used to proactively identify events
and experiences that affect customer
retention, enabling staff to respond more
quickly than with traditional triggers.
Spend analysis
In order to spend their marketing dollars
effectively, marketing organizations have
to regularly balance the available budget
with a complex combination of possible
events, campaigns and other methods
of achieving maximum brand awareness
and lead generation.
Kitenga Analytics Suite enables
marketing teams develop models of
various combinations of spending and
to analyze each model against market
conditions and past campaign results
so they can make better decisions
about the most effective way to spend
marketing dollars.
Upsell
Today’s customers have more
information than ever before about
available products, and they are more
proactive than ever about understanding
their options before making purchases.
Companies want to proactively engage
with customers to ensure they have
relevant information when making
purchasing decisions.
Kitenga Analytics Suite enables
companies to analyze complex set
of data to identify potential buyers.
This data can be sourced from social
media sites, public discussion forums
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and internal data, such as historical
buying patterns, to develop models for
potential buyers. With this information,
the company can proactively contact
the most likely buyers with product
information in order to drive a customer
engagement that leads to a product sale.
Competitive analysis
Organizations must be able to respond
quickly to changing market conditions,
competitor threats and technology
changes. Competitive analysis requires
understanding the market, the current
landscape and how that landscape is
evolving over time, such as through
changes to messaging or introduction of
new products.
Kitenga Analytics Suite enables
companies to pull in public information
sources and assess changes in
messaging and product offering.
Those changes can be evaluated and
compared with internal plans to ensure
that product messaging is evolving
ahead of the competition and enabling
customers to clearly understand product
value propositions.
Intellectual property due diligence
In today’s competitive business market,
intellectual property is more valuable
than ever. In particular, it is often the
driving force behind company mergers
and acquisitions (M&As). During these
complex M&A activities, it is critical that
all parties have a clear understanding
of the intellectual property owned,
managed and accessible by the firms
involved in negotiations.
Advanced analytical tools like Kitenga
Analytics Suite enable companies to
combine and analyze public and private
documents relating to intellectual
property, including patent filings,
contracts, documentation and software
source code. With Kitenga Analytics
Suite, teams can quickly determine what
documentation is relevant during due
diligence processes and to assess the
value of specific intellectual property, its
applicability in complex markets and the

5. availability of similar technologies in the
open market.
Improving the bottom line
In the simplest terms, a company has
two ways to improve the bottom line:
by increasing the amount of business
they do or by reducing costs while
maintaining the current business levels.
Today’s analytical tools enable companies
to evaluate a multitude of information
when developing corporate strategies,
targeting new customers, retaining
existing customers, and managing an
effective and stable workforce.
Tips for a successful
analytics project
When beginning an analytics project,
your company should focus on a small
number of identifiable and measurable
processes. Analytical projects should
not be planned across an entire
company, or even division-wide. Initial
pilots should focus on small, identifiable
challenges and work to resolve the
single challenge. Once a project has
been successfully piloted and measured,
other teams within the organization
will see the value to new analytical
technologies, and also understand the
organizational changes required to
adopt a new mindset and technology.
All successful analytics projects start
with clearly defined and managed
metrics. These metrics enable consistent
communication across teams, show
how changes to the project affect
key decision factors, and enable
separate teams to understand how
they contribute to the end goals of an
analytics project.
Enterprise architecture standards are
also important to analytics projects.
They enable staff to clearly understand
where data is coming from, assess
any transformations that occur, and
understand what impact decisions have
on processes and infrastructure.
Conclusion
Analytics is about enabling business
users to understand and drive their
organizations with technologies that
provide visibility into complex data sets.
Big data is about providing the tools that
deliver that increased visibility to make
staff more effective.
Kitenga Analytics Suite enables
companies to make better decisions
through analytics. The solution’s
unique integration of natural language
processing with visualization and a
search interface enables a broad range
of staff to quickly consume complex
data sets, identify relationships and
model outcomes from a variety of
factors influencing the business, so
everyone can better contribute to the
organization’s success.
For more information, please visit
software.dell.com/products/kitengaanalytics-suite/.
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Advanced
analytical tools
like Kitenga
Analytics Suite
enable companies
to combine and
analyze public and
private documents
relating to
intellectual property,
including patent
filings, contracts,
documentation
and software
source code.

6. For More Information
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WhitePaper-EmpoweringBusiness-US-PL-2013-9-6
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