Description
How organizations can take advantage of an expanding BI toolset
WHITE PAPER
2 Today’s BI
4 Determining Business Drivers
4 Lines of Business Needs
5 Packed & Process- or Industry-
speci?c Analytics Apps
6 Te Typical BI Stack
6 Te Pilot Program
7 Obtaining Executive Buy-in & Support
7 Follow Up for Success
8 CDW: A BI Partner Tat Gets IT
Table of Contents
DELIVERING BUSINESS..
INTELLIGENCE..
How organizations can take advantage of an
expanding BI toolset
Business intelligence has been a technology mainstay
of many organizations for decades. Today, it is playing an
ever larger role in the success of businesses of all sizes.
From small operations to global enterprises, more
organizations are recognizing BI as a powerful tool that
can help them survive and thrive in a challenging and
competitive business environment.
Te need for BI — or the expansion of BI for those that
already use it in some form — creates additional demands
on IT teams, who frequently take a leadership role in BI
initiatives. IT departments have faced numerous challenges
in recent years. Budgets have grown tighter than ever, even
as the IT group has been asked to deal with the security and
governance challenges of bring-your-own-device (BYOD)
programs and cloud computing.
Tis white paper provides an overview of BI building blocks
and deployments — from classic data warehouses to Big
Data and real-time analytics, as well as the demand for
mobile solutions that can provide actionable intelligence
from anywhere.
SHARE THIS
WHITE PAPER
DELIVERING BUSINESS INTELLIGENCE 2
Te good news is that BI has a well-deserved reputation
for providing insights based on the inherent power of
bringing data together (often from disparate sources) into a
central repository, or data warehouse, from which it can be
queried, analyzed and explored to guide decision-making.
Organizations can gain substantial bene?ts and clarity from
using well-de?ned reporting and analytic tools that provide
all decision-makers with deep insights that they previously
would have been unable to ?nd.
Expanding the power of BI to mobile, incorporating
nonstructured data from the vast realm of Big Data
or drawing upon cloud-based resources are all simply
extensions of the core BI story. Te IT team was there from
the birth of BI, and it is the group best prepared to help an
enterprise evaluate and deploy whatever BI toolsets and
data sources it needs.
Today’s BI
To appreciate where BI is today — and where it is headed —
it is helpful to look back to its origins, which can be traced
to the previous century and a German émigré named Hans
Peter Luhn, who joined IBM as a research scientist in 1941.
He is credited with creating the term business intelligence
in a 1958 article he wrote for the IBM Journal of Research
and Development. In de?ning what he meant by business
intelligence, Luhn simply cited the de?nition of intelligence
— straight from Webster’s Dictionary: “the ability to
apprehend the interrelationships of presented facts in such
a way as to guide action toward a desired goal.”
Tat search for “the interrelationships of presented facts” to
“guide actions toward a desired goal” is, more than 50 years
later, still the goal of all BI deployments — whether these
facts are pulled from an organization’s data warehouse or
churned out from crunching terabytes of Big Data.
Expanded Tools
For the past 50 years, the quest for BI has been one of the
drivers behind computer science, and BI has bene?tted
from a long series of breakthroughs in both hardware and
software. Today, with the ability to purchase terabyte hard
drives at commodity prices and the ubiquity of multicore
processing, much of the focus is on the software that can
be brought to bear on vast data stores.
In its most simple description, BI could be looked upon as a
two-step process: Step 1, gather a lot of data. Step 2, pull
actionable insights from that data.
Te basic tools of BI, the reporting, analytical and predictive
tools that make its actionable insights so valuable to an
organization, include the following:
Reporting tools: Reporting tools are the bread and butter of
BI. Tis functionality enables an IT department to de?ne and
run recurring reports against data imported from across an
enterprise. Whether referred to as an organizational data
store, a data warehouse or any number of other terms, the
power of classic BI is seen in the gathering of data and then
running reports against it.
Analytic tools: Analytic tools support the exploration of
data. Tey go beyond ?xed reports to seek out unusual
insights that might otherwise go unseen. For example, a
retailer might ?nd that a hunting jacket popular in Maine
isn’t as popular in Texas and adjust its sales strategies in
those states accordingly.
Predictive tools: In a way, predictive tools can be seen as a
third step along the path of BI — from reporting to analytics
to prediction. Of course, plenty of predictive value can be
derived from solid analytics, but the emphasis on predictive
tools is generally on real-time or near-real-time results.
Speaking of BI
Te wealth of data and promise of BI have inspired
thinkers for centuries. Here are some classic thoughts
on the matter:
“ Data! Data! Data!” he cried impatiently. “I can’t make
bricks without clay!”
— Sherlock Holmes in Sir Arthur Conan Doyle’s
Te Adventure of the Copper Beeches
“ Without Big Data analytics, companies are blind
and deaf, wandering out onto the web like deer
on a freeway.”
— Geo?rey Moore, Author, Crossing the Chasm:
Marketing and Selling Disruptive Products to
Mainstream Customers
“ Data is a precious thing and will last longer than the
systems themselves.”
— Tim Berners-Lee, Inventor of the World Wide Web
“ Numbers have an important story to tell. Tey rely on
you to give them a clear and convincing voice.”
— Stephen Few, Author, Show Me the Numbers
“ Hiding within those mounds of data is knowledge that
could change the life of a patient, or change the world.”
— Atul Butte, Associate Professor of Pediatrics,
Stanford University School of Medicine
Predictive analytics is based on feeding data into an
algorithm to generate what’s predicted to be the most
e?ective response. To an extent, the need for predictive
analytics has been driven by the world’s move to mobile
devices, as well as the demands of the mobile ad space.
800.800.4239 | CDW.com 3
Vendors have invested heavily in ?nding out who will
respond best to which ad or, given what is known about
certain visitors, which o?ers should be presented upon
their landing on a website.
Contextual tools: Contextual tools are focused on the
interaction between data and context. Te context can be
as simple as matching ZIP code demographics to sales, or
as subtle as evaluating the brand of smartphone a person
is using, the time of day and even his or her location (from
the smartphone’s GPS) to determine how to analyze data
points or how to ?ne-tune real-time predictions.
Cognitive tools: Cognitive tools incorporate models that
attempt to replicate the weighing of facts and context that
goes into human decision-making. Te quest for cognitive
tools goes back to the birth of BI, and later, the emergence
of arti?cial intelligence.
A cognitive element is seen in predictive analytic tools that
incorporate machine learning. Machine learning simply
means that once a system makes a prediction (such as
whether a user will click on a pro?ered link) the results are
monitored and fed back into the system, with the goal of
enabling it to make smarter choices in the future.
Social media tools: Social media tools are used to import
and analyze nonstructured data from the vast realm of
social media. For a business, this could involve gaining a feel
for customer sentiment by analyzing how the company is
perceived through automated searches of blogs, Twitter
feeds, Facebook posts and other social media. Te same
information can be analyzed to predict trends and patterns
that could better inform an organization’s BI e?orts.
Te Growth of Self-service BI
For as long as BI has existed, enterprises have held a strong
desire for self-service BI. With classic BI, the IT team works
with business groups to design and run a set of recurring
reports, while specialized analysts use complex tools to run
their own searches and ad hoc queries against the same
data store.
Of course, it wasn’t long before knowledge workers
started making requests for custom reports. Traditionally,
such requests were submitted to an IT group or analyst
group, where the report would be created. Such requests
demanded resources from the IT and analyst groups,
especially when the person requesting the report —
perhaps after having waited days or longer to get it — later
added other variables to the request.
Now available user-friendly tools can create ad hoc reports
and let regular business users design, test and modify their
own queries. Tese tools have become very popular and
have boosted bottom-line productivity. Tools designed
with a user-friendly dashboard interface allow users to
explore data and drill down into exacting detail whenever
needed. Today, regular users can create their own reports
and de?ne their own key performance indicators —
without having to go to night school to learn a sequential
query language.
Mobile Trends
Te adoption of smartphones and other mobile devices has
been so strong that researchers at Stanford University
reported that most people are more likely to leave the
house without their wallet than forget their smartphone.
Similar studies have found that the majority of people take
their smartphones to bed with them, and that we come to
view them as extensions of ourselves.
Against that backdrop, it isn’t surprising that decision-
makers throughout an organization want to be able to
use mobile devices to access the same kinds of BI tools
that they have come to depend on when using desktop or
notebook computers.
Mobile applications also help workers for whom mobile
work is a requirement — ?eld technicians, delivery drivers,
plant workers and a world of others who can do their
job better by tapping into BI and analytics (whether to
determine an optimized delivery route or to project mean
time to failure for a piece of equipment) from wherever
they may be working.
Te mobile trend extends the path on which business
analytics has been growing. A generation of desktop BI
and analytics tools has freed BI from the back o?ce and
pushed it to the edges of the enterprise so that everyone
can make better decisions. Mobile is a powerful tool for
extending this practice.
Te challenge for IT departments is to ?nd the best mobile
self-service tools. Trends such as the consumerization of
IT and BYOD have sometimes forced the IT department
into a reactive position of accommodating multiple apps
that have been downloaded onto users’ mobile devices.
Some organizations have taken a pre-emptive position by
creating their own internal app stores where employees
can download apps that have been vetted and integrated
into back-end BI systems.
Most new BI solutions support the mobile workforces
that organizations employ. Many of these solutions let
mobile workers use a smartphone or tablet to make
queries, extract data, run analyses and conduct other
BI-related tasks.
DELIVERING BUSINESS INTELLIGENCE 4
Cloud Trends
Cloud computing represents an interesting challenge for
IT groups. Te cloud can look enticing: It o?ers all of the
computing and storage power an organization may need,
without the capital expenditures of deploying its own
servers. Te cloud certainly makes for easier capacity
planning too. An enterprise can spin up instances as
needed, and spin them down (and stop paying for the
resources) when not needed.
But IT teams must maintain careful oversight and control of
cloud resources. Storing an organization’s data on devices
that are controlled and owned by another entity, perhaps
located in another country, raises a number of questions
with regard to security and governance.
Many organizations are taking a cautious approach to the
cloud. But the basic value proposition is strong enough that
small enterprises wishing to deploy or expand BI platforms,
or groups seeking to capture and explore immense volumes
of Big Data, may be drawn toward cloud-based resources.
Tis is an area where the IT department can provide
guidance in assessing security and governance concerns.
IT may also be able to help with the architectural design
of hybrid solutions that incorporate cloud resources
while maintaining local control of data to meet industry
compliance or internal governance restrictions.
Te consumerization of IT, again, can be a complicating
factor. In the same way that workers want BYOD, many are
also launching their own clouds, which can be accomplished
in minutes. Launching cloud-based resources in such an ad
hoc manner can make it di?cult for the IT sta? to maintain
control of enterprise information.
When considering cloud resources, an organization is well
served to remember that its BI platform, to a great extent,
constitutes its crown jewels. Wherever the infrastructure
is deployed — whether in the back o?ce or in the cloud — it
must be carefully secured and protected, which provides
good arguments for IT involvement.
50%: Te percentage of business leaders that say they
base at least half of their business decisions on data
and analytics
SOURCE: “Analytics: A blueprint for value” (IBM, October 2013)
Determining Business Drivers
Determining the business drivers for a BI implementation
should be a ?rst step for all projects — whether planning
to introduce BI for the ?rst time, or expanding an existing
BI solution to serve new groups or purposes. Tis means
walking stakeholders through a discussion of what they need
and why they need it. Analysts and someone with systems
integration experience would add value to this process.
Te purpose of this discussion is to de?ne needs and
propose solutions — while identifying a set of metrics that
can be measured both before and after deployment to
gauge the success of the project.
If an organization already has a data warehouse, the project
could be as simple as de?ning a set of reports for a speci?c
group and providing it with easy-to-use tools to enable
self-service ad hoc reporting and analytics. For groups
with heavier analytical needs, the IT team can provide a
separate data mart (a subset of the entire data warehouse)
with data structured (using a dimensional model) to
more directly meet their needs. Because a data mart is
essentially structured to answer common questions, a sales
department might be best served by one data mart, ?nance
by another, customer service or manufacturing by another.
Te IT group should ensure that all data marts are plugged
into the same central repository, or data warehouse, and
that a common set of data structures, or dimensions,
is established so that every data mart uses the same
de?nition of time, product, customer, supplier, branch, etc.
It can be surprisingly di?cult to get di?erent groups within
the same enterprise to agree on what constitutes basic
metrics. But without uniform de?nitions, the organization
can’t get to a single view of the truth, which should be a
common goal of all BI deployments.
When launching a new BI platform, it is perhaps even more
important to identify the business drivers and the metrics
that will determine success. To maximize the chance for
success, a BI project should begin small and then grow as
users and IT sta? gain experience and expertise. Rather
than rolling out a BI solution for the entire organization, the
IT team should ?nd the group that could most bene?t from
BI and work with it to create a pilot project with a set of
predeployment metrics that can be used to measure success.
Success breeds attention. Once a successful BI platform
is provided to one group, others will want the same. So
whatever is deployed should have a scalable infrastructure,
one that can be built out on an as-needed basis.
Lines of Business Needs
Once an organization has identi?ed its business drivers, the
IT department must work closely with each line of business
that will be drawing from the BI solution. Di?erent LOBs
use BI in di?erent ways. Some groups use the data store
to guide long-range planning, such as facilities expansion,
product development or entering new markets. Others
work with a shorter time frame, making decisions to guide
800.800.4239 | CDW.com 5
weekly, monthly and quarterly goals. Still others require
real-time analytics and predictive tools to make fast-paced
workday decisions.
Delivering Information Tat Is Relevant
and Actionable
Once the IT department has a good sense of what a group
needs, the next step is determining the best solution to
meet these needs. In many cases, a line of business can
be served by an organization’s existing BI infrastructure,
by creating a set of custom reports and giving the LOB
access to self-service reporting and analytic tools. If an
enterprise can’t use a solution that’s already in-house, it
should consider third-party products speci?cally designed
to meet its needs.
Te discussion of which solution an enterprise decides
to implement should be guided by the goal of providing
relevant, actionable information. Several questions
can help determine what information is truly relevant:
What information does the organization have? What
information does it really need? What kind of actions will
the information guide? Will a proposed report or analytic
capability meet that need?
As the IT team asks these questions, it shouldn’t be afraid
to follow up with more detailed queries. Te goal is to get
as clear a picture as possible of the business intelligence
the enterprise seeks. To achieve this goal, IT sta? must
understand what information is needed and why.
Packaged & Process- or
Industry-speci?c Analytics Apps
Sometimes it makes sense to take advantage of the
entrepreneurial e?orts of third parties. Whatever
challenges a group may face, someone else has likely
already faced the same challenge and devised a solution.
Such custom solutions can draw on deep industry-speci?c
knowledge that makes it useful for similar organizations.
An enterprise considering such a solution must exercise due
diligence, including ensuring that it is easily customizable to
meet its speci?c needs, and that it will seamlessly integrate
with the existing infrastructure. Te organization must
make a concerted e?ort to prevent the creation of silos of
data that can’t be easily shared and that don’t employ data
de?nitions used elsewhere within the enterprise.
Even when an outside systems integrator is used for the
deployment, the IT department can add value by guiding
architectural and data modeling decisions. Once a project
is implemented, the solution should be ?ne-tuned on an
ongoing basis to help ensure that the organization derives
maximum value from the investment.
Determining Where Predictive Analytics
Fits In
Predictive analytics is a popular topic among organizations
that are considering BI, often in association with Big Data.
In one sense, predictive analytics has been around for a
long time. It’s how insurance companies determine how
much to charge a 57-year-old smoker for life insurance, or a
16-year-old boy with a sports car for auto insurance.
Many enterprises use predictive analytics for real-time or
near-real-time decision-making. Within the retail sector,
predictive analytics can be used to link point-of-sale
devices with back-o?ce BI infrastructure to make split-
second o?ers to customers while they are still at the cash
register. Financial service organizations can use predictive
analytics to make swift recommendations on ?nancial
instruments or other services a speci?c customer might
be interested in. And within the fast-paced world of the
web, predictive analytics can use customer account data to
make product recommendations.
An organization that is considering a predictive analytics
solution should explore the intended use and expectations
Data Warehouse vs. Data Mart
Te stores of data that power a BI solution are critical
to its success.
A data warehouse draws information from sources across
the enterprise, providing a central repository for all data.
A data mart contains just a subset of this data — for example,
departmental budget information — and can be structured
for fast query responses and analytics.
Traditionally, a data mart is dependent on the data
warehouse, meaning that it pulls its data from the data
warehouse. An independent data mart pulls its data directly
from departmental line-of-business (LOB) applications
and data stores.
A data mart is much easier to create than a full data
warehouse, so many independent data marts have long
existed within organizations. But the danger of having
multiple independent data marts (especially those that use
di?erent data structures and de?nitions) is that they can
become functional silos, defeating the value of the data
warehouse as a central repository.
Sometimes, especially in smaller enterprises, the birth
of a data warehouse is traced to an early data mart that
other departments wanted for their own use. Generally,
the sooner an enterprise can move from data mart to data
warehouse, the better. Once a central repository is created,
the organization can create as many dependent data marts
as needed, without fear of creating the disparate silos.
DELIVERING BUSINESS INTELLIGENCE 6
to determine what will provide the best ?t. Basic questions
include: What is the user’s identity? What actions does the
enterprise want to predict? What are the data points? Do
these data points need to be captured and stored?
If the identity of the user isn’t known, then much of the e?ort
will go into crunching contextual data to create a best-guess
model of who the user is and what his or her interests may
be. Classic examples include: If geospatial data indicates that
a smartphone user is in the restaurant area of a city at 7 p.m.
on a Friday, the user may be interested in special o?ers from
nearby restaurants. Whereas, if the same phone is being
used at 7 a.m. on Saturday morning to watch cartoons, it
may be in the hands of kids whose parents are hoping for a
few more hours of sleep.
Te Typical BI Stack
Generally, the heart of a BI stack is the data warehouse,
the central repository for information. A data warehouse
simply brings together all relevant information from across
an enterprise. Before organizations established data
warehouses, information tended to be trapped in silos —
di?erent departments within an enterprise deploying point
solutions designed to meet speci?c needs, but that may not
have been designed to integrate easily with others.
Once data has been compiled in a centralized data
warehouse, all kinds of value can be extracted. Here’s a
look at some of the basic elements of the BI stack:
Central repository: Usually called a data warehouse, this
is where an organization collects data from a variety of
applications and data stores from across the enterprise.
Data integration services: Tis layer serves as the
integration point for importing data from multiple sources
into a uni?ed data warehouse. Te power of data integration
is that it can take information from an Oracle database
in sales and combine it with feeds from a SQL Server
database in customer support, as well as mainframe ?les
from operations. Tis layer serves to extract, transform
and load data from any source so that it is seamlessly
available from the data warehouse.
Master data services: Tese provide synchronization and
deduplication to protect the integrity of the central data
repository, helping to ensure that, among other things,
insertions, updates and deletions from source locations are
posted to the central repository.
Reporting services: Once data from throughout an
organization has been fed into a data warehouse, reporting
services are used to extract its value. Reporting tools,
whether created internally or purchased from a vendor,
should support the creation of recurring reports — such as
weekly or monthly sales ?gures or operational expenses.
Te tools should also support self-service reporting so that
users can create their own reports.
Analytics services: Analytics tools can be used to create
analytical databases that make it faster and easier to run
custom queries or perform data mining. Enterprises can
use analytics tools to create the multidimensional cubes
of a data mart speci?cally designed to meet the needs of a
particular group or function.
Predictive analytics services: Predictive analytics uses
techniques such as statistical, regression, correlation and
cluster analysis. By leveraging these measures, along
with text mining, data mining and social media analytics,
organizations can learn what to expect in a given area. Tey
can use the models and patterns created, along with real-
time data, to improve decision-making in situations such as
loan approvals or product development.
Te Pilot Program
Creating a successful pilot program is as much about
people as it is about technology. A successful pilot should
include a cross-functional team that can work together to
identify a strong business need that is well served by BI.
Along the way, the team should also determine benchmark
metrics that can be used to measure success.
A pilot team can pull valuable insights from the LOB
workers who use the BI, helping to ensure that relevant
data is captured and actionable intelligence is produced.
An executive sponsor with su?cient clout to remove any
roadblocks that might arise can be essential to the success
of a BI pilot. Most important, the sponsoring executive
should be able to understand the value that BI can bring to
the organization.
Te hallmark of a successful pilot program is to start small
with a well-de?ned project that will produce high-value
data. In addition to the cross-functional team, the IT group
should seek input from others who will be involved in day-
to-day use of the information. Tis pays a double dividend.
First, it provides real-world insight into how the data
will be gathered and the BI used. Also, allowing users to
have a hand in crafting the solution promotes buy-in
that increases the chances of success. Users who feel
empowered in planning the solution are more likely to share
lessons learned and seek ways to make improvements as
the project matures.
Once the pilot has been successful, stakeholders should
work with the IT department on ways to enhance its
usability and output.
800.800.4239 | CDW.com 7
Obtaining Executive Buy-in
& Support
In general, as the cost and complexity of an IT project
increases, the greater the need for high-level support within
the enterprise. A large project ideally will garner the backing
of a C-level executive other than the CIO, who should be
expected to support deployment of new IT solutions.
A good strategy for obtaining high-level sponsorship
is to use metrics identi?ed by the pilot team to create a
compelling case to present to the executive. Projected
metrics can be powerful allies. Executives are more likely to
be swayed in favor of a project from which they can expect
results such as reduced time to insights, increased sales or
a greater return on the organization’s investment.
When proposing the project, the IT team should explicitly
draw connections between the enterprise’s strategic goals
and the intelligence delivered by the BI solution. A value-
based business case will explain how the investment will
pay o? and maximize the likelihood that it does so.
Te organization should develop a roadmap for BI
implementation that explains the analytics and how they
will a?ect each facet of the enterprise. In addition to
reducing the risk of redundant BI investments, the roadmap
also will foster data sharing among di?erent departments.
Once the pilot has gone live, IT sta? should determine
whether the metrics demonstrate the anticipated results.
If they do, the organization should begin the process for
scaling the pilot into full production.
Follow Up for Success
With the right teamwork leading up to the launch, many
users will be eager to get their hands on the BI solution.
Users who participate in the process of de?ning the
requirements of and planning for a BI project will be
far more invested in its success. Once in production,
project leaders should gather success stories and share
them widely. Te IT group should hold training events to
make sure users are comfortable with the solution and
understand the capabilities it o?ers.
Trust is another key element to a successful BI
implementation. Users who trust the BI solution and the
data it relies on will make the best use of it. Tis demands
human interaction. Organizations should invest the
necessary time in building trust between all the parties
involved in planning, implementing and using a BI solution —
from executives to IT personnel to analysts to end users.
Once the initial rollout is successful, the enterprise should
market its BI solution to other departments. Tis is another
area where executive support is essential. Senior leaders
who use BI transparently establish a top-down culture that
engrains such behaviors throughout the organization.
Automation is another helpful tool for fostering BI adoption
throughout an enterprise. By streamlining the data cycle
through automation, an organization can deliver more
timely and relevant insights to end users and decision-
makers. Automating parts of the analytics cycle also
increases the productivity of analysts by giving them more
time to focus on driving insights from the data, rather than
performing routine maintenance tasks.
As an enterprise plans to expand its BI projects, it should
duplicate the process that delivered a successful pilot.
IT and project stakeholders should de?ne the bene?ts,
establish metrics, garner executive support and assemble
an implementation team that takes user input into account.
As an organization’s BI success widens, these steps should
become easier to replicate.
Te Elements of a BI Solution
An e?ective BI solution includes the following elements:
Software: Te software platform that facilitates gathering,
analyzing and reporting on the data is an essential key to a
successful BI solution.
Hardware: Servers and storage are among the hardware
components that provide the engine for disseminating
information to clients.
Appliances: Appliances are preloaded, all-in-one solutions
that package hardware and software components together.
Services: A BI solution’s holistic components — hardware
and software — must meet the organization’s reporting
requirements with implementation and con?guration services.
Te ROI of BI
Te bene?ts of a successful BI project are many and
can vary widely, so calculating a return on investment
can be tricky. But research clearly indicates that many
organizations quickly achieve a signi?cant ROI. In fact, in its
study “Analytics: A blueprint for value,” IBM found that more
than 40 percent of business leaders reported that their
organizations realized a return on investment within the ?rst
six months of a business analytics deployment.
Among the bene?ts identi?ed in a Forrester Research
study were lower procurement spending, accounts payable
savings, IT and labor savings, lower inventory working
capital, higher management productivity, and the reduction
of unnecessary discounting. Further complicating the
ROI equation are intangible advantages, such as faster
reporting, better decision-making, more productive users,
improved customer satisfaction and lower risk.
While the results can be di?cult to measure, enterprises
should use the metrics that are most meaningful to them to
determine ROI.
DELIVERING BUSINESS INTELLIGENCE
8 800.800.4239 | CDW.com
Te information is provided for informational purposes. It is believed to be accurate but could contain errors. CDW does not intend
to make any warranties, express or implied, about the products, services, or information that is discussed. CDW
®
, CDW•G
®
and
Te Right Technology. Right Away
®
are registered trademarks of CDW LLC. PEOPLE WHO GET IT
™
is a trademark of CDW LLC.
All other trademarks and registered trademarks are the sole property of their respective owners.
Together we strive for perfection. ISO 9001:2000 certi?ed
145541 — 140228 — ©2014 CDW LLC
CDW: A BI Partner Tat Gets IT
CDW business intelligence solutions help organizations
extract actionable information for better decision-making.
CDW o?ers a wide variety of BI products from numerous
vendors. Tis allows us to build holistic BI solutions that
include software, servers, storage, networking and
services. Tese solutions bring together best-of-breed
products from industry-leading technology partners,
including SAP, Microsoft, IBM, Oracle and EMC, to form
complete solutions.
Te solutions can be deployed on-premises, via the cloud
or as a hybrid. CDW backs up its BI solutions with dedicated
account managers, solution architects and advanced
technology engineers who provide a variety of services.
Tese services extend beyond the BI solution alone, to
assessment, design, proof-of-concept, implementation
and support.
We o?er:
• A business-driven approach toward BI solutions
for customers
• Te capability to build holistic BI solutions
• Full-service BI solution expertise
• Choice in BI deployment models
From business intelligence
to ?nancial performance and
strategy management to
analytics applications, IBM
®
Cognos
®
software can provide
what organizations need
to become top-performing
and analytics-driven. With
products for the individual,
workgroup, department,
midsize business and large
enterprise, Cognos software
is designed to help everyone
within the enterprise to make
the decisions that achieve
better business outcomes —
for now and in the future.
Achieve remarkable
results with SAP
®
business
intelligence solutions.
Make fact-based decisions
throughout the organization
by relying on SAP’s business
intelligence solutions. Easily
access relevant information
when and wherever its
needed to better understand
business, act quickly and
con?dently and ultimately
achieve remarkable results.
Using Business Intelligence
from Microsoft
®
through
Excel
®
2013, SharePoint
®
2013 and SQL Server
®
can
empower users of all levels
with new insights through
familiar tools, balancing
the need for IT to monitor
and manage user-created
content. Deliver access to all
data types across structured
and unstructured sources.
IT systems and technology
infrastructure—websites,
applications, servers,
networks, sensors, mobile
devices and the like —
generate massive amounts
of machine data.
By monitoring and analyzing
everything from customer
clickstreams and transactions
to network activity and call
records, Splunk Enterprise
turns machine data into
valuable insights.
CDW.com/ibm CDW.com/microsoft CDW.com/sap CDW.com
To learn more about CDW’s business intelligence solutions, contact your CDW account manager,
call 800.800.4239 or visit CDW.com/businessintelligence.
SHARE THIS
WHITE PAPER
doc_309818957.pdf
How organizations can take advantage of an expanding BI toolset
WHITE PAPER
2 Today’s BI
4 Determining Business Drivers
4 Lines of Business Needs
5 Packed & Process- or Industry-
speci?c Analytics Apps
6 Te Typical BI Stack
6 Te Pilot Program
7 Obtaining Executive Buy-in & Support
7 Follow Up for Success
8 CDW: A BI Partner Tat Gets IT
Table of Contents
DELIVERING BUSINESS..
INTELLIGENCE..
How organizations can take advantage of an
expanding BI toolset
Business intelligence has been a technology mainstay
of many organizations for decades. Today, it is playing an
ever larger role in the success of businesses of all sizes.
From small operations to global enterprises, more
organizations are recognizing BI as a powerful tool that
can help them survive and thrive in a challenging and
competitive business environment.
Te need for BI — or the expansion of BI for those that
already use it in some form — creates additional demands
on IT teams, who frequently take a leadership role in BI
initiatives. IT departments have faced numerous challenges
in recent years. Budgets have grown tighter than ever, even
as the IT group has been asked to deal with the security and
governance challenges of bring-your-own-device (BYOD)
programs and cloud computing.
Tis white paper provides an overview of BI building blocks
and deployments — from classic data warehouses to Big
Data and real-time analytics, as well as the demand for
mobile solutions that can provide actionable intelligence
from anywhere.
SHARE THIS
WHITE PAPER
DELIVERING BUSINESS INTELLIGENCE 2
Te good news is that BI has a well-deserved reputation
for providing insights based on the inherent power of
bringing data together (often from disparate sources) into a
central repository, or data warehouse, from which it can be
queried, analyzed and explored to guide decision-making.
Organizations can gain substantial bene?ts and clarity from
using well-de?ned reporting and analytic tools that provide
all decision-makers with deep insights that they previously
would have been unable to ?nd.
Expanding the power of BI to mobile, incorporating
nonstructured data from the vast realm of Big Data
or drawing upon cloud-based resources are all simply
extensions of the core BI story. Te IT team was there from
the birth of BI, and it is the group best prepared to help an
enterprise evaluate and deploy whatever BI toolsets and
data sources it needs.
Today’s BI
To appreciate where BI is today — and where it is headed —
it is helpful to look back to its origins, which can be traced
to the previous century and a German émigré named Hans
Peter Luhn, who joined IBM as a research scientist in 1941.
He is credited with creating the term business intelligence
in a 1958 article he wrote for the IBM Journal of Research
and Development. In de?ning what he meant by business
intelligence, Luhn simply cited the de?nition of intelligence
— straight from Webster’s Dictionary: “the ability to
apprehend the interrelationships of presented facts in such
a way as to guide action toward a desired goal.”
Tat search for “the interrelationships of presented facts” to
“guide actions toward a desired goal” is, more than 50 years
later, still the goal of all BI deployments — whether these
facts are pulled from an organization’s data warehouse or
churned out from crunching terabytes of Big Data.
Expanded Tools
For the past 50 years, the quest for BI has been one of the
drivers behind computer science, and BI has bene?tted
from a long series of breakthroughs in both hardware and
software. Today, with the ability to purchase terabyte hard
drives at commodity prices and the ubiquity of multicore
processing, much of the focus is on the software that can
be brought to bear on vast data stores.
In its most simple description, BI could be looked upon as a
two-step process: Step 1, gather a lot of data. Step 2, pull
actionable insights from that data.
Te basic tools of BI, the reporting, analytical and predictive
tools that make its actionable insights so valuable to an
organization, include the following:
Reporting tools: Reporting tools are the bread and butter of
BI. Tis functionality enables an IT department to de?ne and
run recurring reports against data imported from across an
enterprise. Whether referred to as an organizational data
store, a data warehouse or any number of other terms, the
power of classic BI is seen in the gathering of data and then
running reports against it.
Analytic tools: Analytic tools support the exploration of
data. Tey go beyond ?xed reports to seek out unusual
insights that might otherwise go unseen. For example, a
retailer might ?nd that a hunting jacket popular in Maine
isn’t as popular in Texas and adjust its sales strategies in
those states accordingly.
Predictive tools: In a way, predictive tools can be seen as a
third step along the path of BI — from reporting to analytics
to prediction. Of course, plenty of predictive value can be
derived from solid analytics, but the emphasis on predictive
tools is generally on real-time or near-real-time results.
Speaking of BI
Te wealth of data and promise of BI have inspired
thinkers for centuries. Here are some classic thoughts
on the matter:
“ Data! Data! Data!” he cried impatiently. “I can’t make
bricks without clay!”
— Sherlock Holmes in Sir Arthur Conan Doyle’s
Te Adventure of the Copper Beeches
“ Without Big Data analytics, companies are blind
and deaf, wandering out onto the web like deer
on a freeway.”
— Geo?rey Moore, Author, Crossing the Chasm:
Marketing and Selling Disruptive Products to
Mainstream Customers
“ Data is a precious thing and will last longer than the
systems themselves.”
— Tim Berners-Lee, Inventor of the World Wide Web
“ Numbers have an important story to tell. Tey rely on
you to give them a clear and convincing voice.”
— Stephen Few, Author, Show Me the Numbers
“ Hiding within those mounds of data is knowledge that
could change the life of a patient, or change the world.”
— Atul Butte, Associate Professor of Pediatrics,
Stanford University School of Medicine
Predictive analytics is based on feeding data into an
algorithm to generate what’s predicted to be the most
e?ective response. To an extent, the need for predictive
analytics has been driven by the world’s move to mobile
devices, as well as the demands of the mobile ad space.
800.800.4239 | CDW.com 3
Vendors have invested heavily in ?nding out who will
respond best to which ad or, given what is known about
certain visitors, which o?ers should be presented upon
their landing on a website.
Contextual tools: Contextual tools are focused on the
interaction between data and context. Te context can be
as simple as matching ZIP code demographics to sales, or
as subtle as evaluating the brand of smartphone a person
is using, the time of day and even his or her location (from
the smartphone’s GPS) to determine how to analyze data
points or how to ?ne-tune real-time predictions.
Cognitive tools: Cognitive tools incorporate models that
attempt to replicate the weighing of facts and context that
goes into human decision-making. Te quest for cognitive
tools goes back to the birth of BI, and later, the emergence
of arti?cial intelligence.
A cognitive element is seen in predictive analytic tools that
incorporate machine learning. Machine learning simply
means that once a system makes a prediction (such as
whether a user will click on a pro?ered link) the results are
monitored and fed back into the system, with the goal of
enabling it to make smarter choices in the future.
Social media tools: Social media tools are used to import
and analyze nonstructured data from the vast realm of
social media. For a business, this could involve gaining a feel
for customer sentiment by analyzing how the company is
perceived through automated searches of blogs, Twitter
feeds, Facebook posts and other social media. Te same
information can be analyzed to predict trends and patterns
that could better inform an organization’s BI e?orts.
Te Growth of Self-service BI
For as long as BI has existed, enterprises have held a strong
desire for self-service BI. With classic BI, the IT team works
with business groups to design and run a set of recurring
reports, while specialized analysts use complex tools to run
their own searches and ad hoc queries against the same
data store.
Of course, it wasn’t long before knowledge workers
started making requests for custom reports. Traditionally,
such requests were submitted to an IT group or analyst
group, where the report would be created. Such requests
demanded resources from the IT and analyst groups,
especially when the person requesting the report —
perhaps after having waited days or longer to get it — later
added other variables to the request.
Now available user-friendly tools can create ad hoc reports
and let regular business users design, test and modify their
own queries. Tese tools have become very popular and
have boosted bottom-line productivity. Tools designed
with a user-friendly dashboard interface allow users to
explore data and drill down into exacting detail whenever
needed. Today, regular users can create their own reports
and de?ne their own key performance indicators —
without having to go to night school to learn a sequential
query language.
Mobile Trends
Te adoption of smartphones and other mobile devices has
been so strong that researchers at Stanford University
reported that most people are more likely to leave the
house without their wallet than forget their smartphone.
Similar studies have found that the majority of people take
their smartphones to bed with them, and that we come to
view them as extensions of ourselves.
Against that backdrop, it isn’t surprising that decision-
makers throughout an organization want to be able to
use mobile devices to access the same kinds of BI tools
that they have come to depend on when using desktop or
notebook computers.
Mobile applications also help workers for whom mobile
work is a requirement — ?eld technicians, delivery drivers,
plant workers and a world of others who can do their
job better by tapping into BI and analytics (whether to
determine an optimized delivery route or to project mean
time to failure for a piece of equipment) from wherever
they may be working.
Te mobile trend extends the path on which business
analytics has been growing. A generation of desktop BI
and analytics tools has freed BI from the back o?ce and
pushed it to the edges of the enterprise so that everyone
can make better decisions. Mobile is a powerful tool for
extending this practice.
Te challenge for IT departments is to ?nd the best mobile
self-service tools. Trends such as the consumerization of
IT and BYOD have sometimes forced the IT department
into a reactive position of accommodating multiple apps
that have been downloaded onto users’ mobile devices.
Some organizations have taken a pre-emptive position by
creating their own internal app stores where employees
can download apps that have been vetted and integrated
into back-end BI systems.
Most new BI solutions support the mobile workforces
that organizations employ. Many of these solutions let
mobile workers use a smartphone or tablet to make
queries, extract data, run analyses and conduct other
BI-related tasks.
DELIVERING BUSINESS INTELLIGENCE 4
Cloud Trends
Cloud computing represents an interesting challenge for
IT groups. Te cloud can look enticing: It o?ers all of the
computing and storage power an organization may need,
without the capital expenditures of deploying its own
servers. Te cloud certainly makes for easier capacity
planning too. An enterprise can spin up instances as
needed, and spin them down (and stop paying for the
resources) when not needed.
But IT teams must maintain careful oversight and control of
cloud resources. Storing an organization’s data on devices
that are controlled and owned by another entity, perhaps
located in another country, raises a number of questions
with regard to security and governance.
Many organizations are taking a cautious approach to the
cloud. But the basic value proposition is strong enough that
small enterprises wishing to deploy or expand BI platforms,
or groups seeking to capture and explore immense volumes
of Big Data, may be drawn toward cloud-based resources.
Tis is an area where the IT department can provide
guidance in assessing security and governance concerns.
IT may also be able to help with the architectural design
of hybrid solutions that incorporate cloud resources
while maintaining local control of data to meet industry
compliance or internal governance restrictions.
Te consumerization of IT, again, can be a complicating
factor. In the same way that workers want BYOD, many are
also launching their own clouds, which can be accomplished
in minutes. Launching cloud-based resources in such an ad
hoc manner can make it di?cult for the IT sta? to maintain
control of enterprise information.
When considering cloud resources, an organization is well
served to remember that its BI platform, to a great extent,
constitutes its crown jewels. Wherever the infrastructure
is deployed — whether in the back o?ce or in the cloud — it
must be carefully secured and protected, which provides
good arguments for IT involvement.
50%: Te percentage of business leaders that say they
base at least half of their business decisions on data
and analytics
SOURCE: “Analytics: A blueprint for value” (IBM, October 2013)
Determining Business Drivers
Determining the business drivers for a BI implementation
should be a ?rst step for all projects — whether planning
to introduce BI for the ?rst time, or expanding an existing
BI solution to serve new groups or purposes. Tis means
walking stakeholders through a discussion of what they need
and why they need it. Analysts and someone with systems
integration experience would add value to this process.
Te purpose of this discussion is to de?ne needs and
propose solutions — while identifying a set of metrics that
can be measured both before and after deployment to
gauge the success of the project.
If an organization already has a data warehouse, the project
could be as simple as de?ning a set of reports for a speci?c
group and providing it with easy-to-use tools to enable
self-service ad hoc reporting and analytics. For groups
with heavier analytical needs, the IT team can provide a
separate data mart (a subset of the entire data warehouse)
with data structured (using a dimensional model) to
more directly meet their needs. Because a data mart is
essentially structured to answer common questions, a sales
department might be best served by one data mart, ?nance
by another, customer service or manufacturing by another.
Te IT group should ensure that all data marts are plugged
into the same central repository, or data warehouse, and
that a common set of data structures, or dimensions,
is established so that every data mart uses the same
de?nition of time, product, customer, supplier, branch, etc.
It can be surprisingly di?cult to get di?erent groups within
the same enterprise to agree on what constitutes basic
metrics. But without uniform de?nitions, the organization
can’t get to a single view of the truth, which should be a
common goal of all BI deployments.
When launching a new BI platform, it is perhaps even more
important to identify the business drivers and the metrics
that will determine success. To maximize the chance for
success, a BI project should begin small and then grow as
users and IT sta? gain experience and expertise. Rather
than rolling out a BI solution for the entire organization, the
IT team should ?nd the group that could most bene?t from
BI and work with it to create a pilot project with a set of
predeployment metrics that can be used to measure success.
Success breeds attention. Once a successful BI platform
is provided to one group, others will want the same. So
whatever is deployed should have a scalable infrastructure,
one that can be built out on an as-needed basis.
Lines of Business Needs
Once an organization has identi?ed its business drivers, the
IT department must work closely with each line of business
that will be drawing from the BI solution. Di?erent LOBs
use BI in di?erent ways. Some groups use the data store
to guide long-range planning, such as facilities expansion,
product development or entering new markets. Others
work with a shorter time frame, making decisions to guide
800.800.4239 | CDW.com 5
weekly, monthly and quarterly goals. Still others require
real-time analytics and predictive tools to make fast-paced
workday decisions.
Delivering Information Tat Is Relevant
and Actionable
Once the IT department has a good sense of what a group
needs, the next step is determining the best solution to
meet these needs. In many cases, a line of business can
be served by an organization’s existing BI infrastructure,
by creating a set of custom reports and giving the LOB
access to self-service reporting and analytic tools. If an
enterprise can’t use a solution that’s already in-house, it
should consider third-party products speci?cally designed
to meet its needs.
Te discussion of which solution an enterprise decides
to implement should be guided by the goal of providing
relevant, actionable information. Several questions
can help determine what information is truly relevant:
What information does the organization have? What
information does it really need? What kind of actions will
the information guide? Will a proposed report or analytic
capability meet that need?
As the IT team asks these questions, it shouldn’t be afraid
to follow up with more detailed queries. Te goal is to get
as clear a picture as possible of the business intelligence
the enterprise seeks. To achieve this goal, IT sta? must
understand what information is needed and why.
Packaged & Process- or
Industry-speci?c Analytics Apps
Sometimes it makes sense to take advantage of the
entrepreneurial e?orts of third parties. Whatever
challenges a group may face, someone else has likely
already faced the same challenge and devised a solution.
Such custom solutions can draw on deep industry-speci?c
knowledge that makes it useful for similar organizations.
An enterprise considering such a solution must exercise due
diligence, including ensuring that it is easily customizable to
meet its speci?c needs, and that it will seamlessly integrate
with the existing infrastructure. Te organization must
make a concerted e?ort to prevent the creation of silos of
data that can’t be easily shared and that don’t employ data
de?nitions used elsewhere within the enterprise.
Even when an outside systems integrator is used for the
deployment, the IT department can add value by guiding
architectural and data modeling decisions. Once a project
is implemented, the solution should be ?ne-tuned on an
ongoing basis to help ensure that the organization derives
maximum value from the investment.
Determining Where Predictive Analytics
Fits In
Predictive analytics is a popular topic among organizations
that are considering BI, often in association with Big Data.
In one sense, predictive analytics has been around for a
long time. It’s how insurance companies determine how
much to charge a 57-year-old smoker for life insurance, or a
16-year-old boy with a sports car for auto insurance.
Many enterprises use predictive analytics for real-time or
near-real-time decision-making. Within the retail sector,
predictive analytics can be used to link point-of-sale
devices with back-o?ce BI infrastructure to make split-
second o?ers to customers while they are still at the cash
register. Financial service organizations can use predictive
analytics to make swift recommendations on ?nancial
instruments or other services a speci?c customer might
be interested in. And within the fast-paced world of the
web, predictive analytics can use customer account data to
make product recommendations.
An organization that is considering a predictive analytics
solution should explore the intended use and expectations
Data Warehouse vs. Data Mart
Te stores of data that power a BI solution are critical
to its success.
A data warehouse draws information from sources across
the enterprise, providing a central repository for all data.
A data mart contains just a subset of this data — for example,
departmental budget information — and can be structured
for fast query responses and analytics.
Traditionally, a data mart is dependent on the data
warehouse, meaning that it pulls its data from the data
warehouse. An independent data mart pulls its data directly
from departmental line-of-business (LOB) applications
and data stores.
A data mart is much easier to create than a full data
warehouse, so many independent data marts have long
existed within organizations. But the danger of having
multiple independent data marts (especially those that use
di?erent data structures and de?nitions) is that they can
become functional silos, defeating the value of the data
warehouse as a central repository.
Sometimes, especially in smaller enterprises, the birth
of a data warehouse is traced to an early data mart that
other departments wanted for their own use. Generally,
the sooner an enterprise can move from data mart to data
warehouse, the better. Once a central repository is created,
the organization can create as many dependent data marts
as needed, without fear of creating the disparate silos.
DELIVERING BUSINESS INTELLIGENCE 6
to determine what will provide the best ?t. Basic questions
include: What is the user’s identity? What actions does the
enterprise want to predict? What are the data points? Do
these data points need to be captured and stored?
If the identity of the user isn’t known, then much of the e?ort
will go into crunching contextual data to create a best-guess
model of who the user is and what his or her interests may
be. Classic examples include: If geospatial data indicates that
a smartphone user is in the restaurant area of a city at 7 p.m.
on a Friday, the user may be interested in special o?ers from
nearby restaurants. Whereas, if the same phone is being
used at 7 a.m. on Saturday morning to watch cartoons, it
may be in the hands of kids whose parents are hoping for a
few more hours of sleep.
Te Typical BI Stack
Generally, the heart of a BI stack is the data warehouse,
the central repository for information. A data warehouse
simply brings together all relevant information from across
an enterprise. Before organizations established data
warehouses, information tended to be trapped in silos —
di?erent departments within an enterprise deploying point
solutions designed to meet speci?c needs, but that may not
have been designed to integrate easily with others.
Once data has been compiled in a centralized data
warehouse, all kinds of value can be extracted. Here’s a
look at some of the basic elements of the BI stack:
Central repository: Usually called a data warehouse, this
is where an organization collects data from a variety of
applications and data stores from across the enterprise.
Data integration services: Tis layer serves as the
integration point for importing data from multiple sources
into a uni?ed data warehouse. Te power of data integration
is that it can take information from an Oracle database
in sales and combine it with feeds from a SQL Server
database in customer support, as well as mainframe ?les
from operations. Tis layer serves to extract, transform
and load data from any source so that it is seamlessly
available from the data warehouse.
Master data services: Tese provide synchronization and
deduplication to protect the integrity of the central data
repository, helping to ensure that, among other things,
insertions, updates and deletions from source locations are
posted to the central repository.
Reporting services: Once data from throughout an
organization has been fed into a data warehouse, reporting
services are used to extract its value. Reporting tools,
whether created internally or purchased from a vendor,
should support the creation of recurring reports — such as
weekly or monthly sales ?gures or operational expenses.
Te tools should also support self-service reporting so that
users can create their own reports.
Analytics services: Analytics tools can be used to create
analytical databases that make it faster and easier to run
custom queries or perform data mining. Enterprises can
use analytics tools to create the multidimensional cubes
of a data mart speci?cally designed to meet the needs of a
particular group or function.
Predictive analytics services: Predictive analytics uses
techniques such as statistical, regression, correlation and
cluster analysis. By leveraging these measures, along
with text mining, data mining and social media analytics,
organizations can learn what to expect in a given area. Tey
can use the models and patterns created, along with real-
time data, to improve decision-making in situations such as
loan approvals or product development.
Te Pilot Program
Creating a successful pilot program is as much about
people as it is about technology. A successful pilot should
include a cross-functional team that can work together to
identify a strong business need that is well served by BI.
Along the way, the team should also determine benchmark
metrics that can be used to measure success.
A pilot team can pull valuable insights from the LOB
workers who use the BI, helping to ensure that relevant
data is captured and actionable intelligence is produced.
An executive sponsor with su?cient clout to remove any
roadblocks that might arise can be essential to the success
of a BI pilot. Most important, the sponsoring executive
should be able to understand the value that BI can bring to
the organization.
Te hallmark of a successful pilot program is to start small
with a well-de?ned project that will produce high-value
data. In addition to the cross-functional team, the IT group
should seek input from others who will be involved in day-
to-day use of the information. Tis pays a double dividend.
First, it provides real-world insight into how the data
will be gathered and the BI used. Also, allowing users to
have a hand in crafting the solution promotes buy-in
that increases the chances of success. Users who feel
empowered in planning the solution are more likely to share
lessons learned and seek ways to make improvements as
the project matures.
Once the pilot has been successful, stakeholders should
work with the IT department on ways to enhance its
usability and output.
800.800.4239 | CDW.com 7
Obtaining Executive Buy-in
& Support
In general, as the cost and complexity of an IT project
increases, the greater the need for high-level support within
the enterprise. A large project ideally will garner the backing
of a C-level executive other than the CIO, who should be
expected to support deployment of new IT solutions.
A good strategy for obtaining high-level sponsorship
is to use metrics identi?ed by the pilot team to create a
compelling case to present to the executive. Projected
metrics can be powerful allies. Executives are more likely to
be swayed in favor of a project from which they can expect
results such as reduced time to insights, increased sales or
a greater return on the organization’s investment.
When proposing the project, the IT team should explicitly
draw connections between the enterprise’s strategic goals
and the intelligence delivered by the BI solution. A value-
based business case will explain how the investment will
pay o? and maximize the likelihood that it does so.
Te organization should develop a roadmap for BI
implementation that explains the analytics and how they
will a?ect each facet of the enterprise. In addition to
reducing the risk of redundant BI investments, the roadmap
also will foster data sharing among di?erent departments.
Once the pilot has gone live, IT sta? should determine
whether the metrics demonstrate the anticipated results.
If they do, the organization should begin the process for
scaling the pilot into full production.
Follow Up for Success
With the right teamwork leading up to the launch, many
users will be eager to get their hands on the BI solution.
Users who participate in the process of de?ning the
requirements of and planning for a BI project will be
far more invested in its success. Once in production,
project leaders should gather success stories and share
them widely. Te IT group should hold training events to
make sure users are comfortable with the solution and
understand the capabilities it o?ers.
Trust is another key element to a successful BI
implementation. Users who trust the BI solution and the
data it relies on will make the best use of it. Tis demands
human interaction. Organizations should invest the
necessary time in building trust between all the parties
involved in planning, implementing and using a BI solution —
from executives to IT personnel to analysts to end users.
Once the initial rollout is successful, the enterprise should
market its BI solution to other departments. Tis is another
area where executive support is essential. Senior leaders
who use BI transparently establish a top-down culture that
engrains such behaviors throughout the organization.
Automation is another helpful tool for fostering BI adoption
throughout an enterprise. By streamlining the data cycle
through automation, an organization can deliver more
timely and relevant insights to end users and decision-
makers. Automating parts of the analytics cycle also
increases the productivity of analysts by giving them more
time to focus on driving insights from the data, rather than
performing routine maintenance tasks.
As an enterprise plans to expand its BI projects, it should
duplicate the process that delivered a successful pilot.
IT and project stakeholders should de?ne the bene?ts,
establish metrics, garner executive support and assemble
an implementation team that takes user input into account.
As an organization’s BI success widens, these steps should
become easier to replicate.
Te Elements of a BI Solution
An e?ective BI solution includes the following elements:
Software: Te software platform that facilitates gathering,
analyzing and reporting on the data is an essential key to a
successful BI solution.
Hardware: Servers and storage are among the hardware
components that provide the engine for disseminating
information to clients.
Appliances: Appliances are preloaded, all-in-one solutions
that package hardware and software components together.
Services: A BI solution’s holistic components — hardware
and software — must meet the organization’s reporting
requirements with implementation and con?guration services.
Te ROI of BI
Te bene?ts of a successful BI project are many and
can vary widely, so calculating a return on investment
can be tricky. But research clearly indicates that many
organizations quickly achieve a signi?cant ROI. In fact, in its
study “Analytics: A blueprint for value,” IBM found that more
than 40 percent of business leaders reported that their
organizations realized a return on investment within the ?rst
six months of a business analytics deployment.
Among the bene?ts identi?ed in a Forrester Research
study were lower procurement spending, accounts payable
savings, IT and labor savings, lower inventory working
capital, higher management productivity, and the reduction
of unnecessary discounting. Further complicating the
ROI equation are intangible advantages, such as faster
reporting, better decision-making, more productive users,
improved customer satisfaction and lower risk.
While the results can be di?cult to measure, enterprises
should use the metrics that are most meaningful to them to
determine ROI.
DELIVERING BUSINESS INTELLIGENCE
8 800.800.4239 | CDW.com
Te information is provided for informational purposes. It is believed to be accurate but could contain errors. CDW does not intend
to make any warranties, express or implied, about the products, services, or information that is discussed. CDW
®
, CDW•G
®
and
Te Right Technology. Right Away
®
are registered trademarks of CDW LLC. PEOPLE WHO GET IT
™
is a trademark of CDW LLC.
All other trademarks and registered trademarks are the sole property of their respective owners.
Together we strive for perfection. ISO 9001:2000 certi?ed
145541 — 140228 — ©2014 CDW LLC
CDW: A BI Partner Tat Gets IT
CDW business intelligence solutions help organizations
extract actionable information for better decision-making.
CDW o?ers a wide variety of BI products from numerous
vendors. Tis allows us to build holistic BI solutions that
include software, servers, storage, networking and
services. Tese solutions bring together best-of-breed
products from industry-leading technology partners,
including SAP, Microsoft, IBM, Oracle and EMC, to form
complete solutions.
Te solutions can be deployed on-premises, via the cloud
or as a hybrid. CDW backs up its BI solutions with dedicated
account managers, solution architects and advanced
technology engineers who provide a variety of services.
Tese services extend beyond the BI solution alone, to
assessment, design, proof-of-concept, implementation
and support.
We o?er:
• A business-driven approach toward BI solutions
for customers
• Te capability to build holistic BI solutions
• Full-service BI solution expertise
• Choice in BI deployment models
From business intelligence
to ?nancial performance and
strategy management to
analytics applications, IBM
®
Cognos
®
software can provide
what organizations need
to become top-performing
and analytics-driven. With
products for the individual,
workgroup, department,
midsize business and large
enterprise, Cognos software
is designed to help everyone
within the enterprise to make
the decisions that achieve
better business outcomes —
for now and in the future.
Achieve remarkable
results with SAP
®
business
intelligence solutions.
Make fact-based decisions
throughout the organization
by relying on SAP’s business
intelligence solutions. Easily
access relevant information
when and wherever its
needed to better understand
business, act quickly and
con?dently and ultimately
achieve remarkable results.
Using Business Intelligence
from Microsoft
®
through
Excel
®
2013, SharePoint
®
2013 and SQL Server
®
can
empower users of all levels
with new insights through
familiar tools, balancing
the need for IT to monitor
and manage user-created
content. Deliver access to all
data types across structured
and unstructured sources.
IT systems and technology
infrastructure—websites,
applications, servers,
networks, sensors, mobile
devices and the like —
generate massive amounts
of machine data.
By monitoring and analyzing
everything from customer
clickstreams and transactions
to network activity and call
records, Splunk Enterprise
turns machine data into
valuable insights.
CDW.com/ibm CDW.com/microsoft CDW.com/sap CDW.com
To learn more about CDW’s business intelligence solutions, contact your CDW account manager,
call 800.800.4239 or visit CDW.com/businessintelligence.
SHARE THIS
WHITE PAPER
doc_309818957.pdf