Organizations today want to be driven by data. They want to anchor daily and strategic decisions in a bedrock of solid, extensive, and timely analysis and reporting. Organizations want to reach out to more data sources and integrate diverse data to gain single views of important business objects and domains, including customers, products, and services. Business intelligence (BI) and analytics are essential technologies, methods, and processes to support data-driven decisions. However, in most organizations, only a minority of users can access them. This needs to change if data-driven objectives are to be achieved. Past attempts at extending BI out to users in operations have been met with mixed success. Users have gained significant advantages, including better data quality, data access, and reporting; yet, they are frustrated by their inability to tailor the BI environment to their personal needs. Customizing the environment and performing ad hoc, what-if analytical queries can require significant IT involvement. Many organizations are deepening their analytical power by investing in hiring specialists dedicated to marketing, finance, or other business functions to implement data mining tools and methods; however, the mass of users share only indirectly in the fruits of their labors. Most users cannot do advanced discovery analysis on their own. Personal, self-service BI and analytics tools are now available that can give users more control over how they view and access data and the reports, dashboards, and visualizations they need to perform their roles in the enterprise. Easier deployment and configuration, improved self-service features, and technology advances such as in-memory computing are giving users work environments that are robust enough to perform significant BI and analytics processes without involving IT at every step, as has been necessary in the past. This TDWI Checklist Report details seven steps toward personal BI and analytics success. Note that IT is not left out of the picture; on the contrary, this checklist shows that IT has an important role to play in guiding users and provisioning enterprise data resources. Users are more productive when personal, self-service BI and analytics technologies are well integrated with enterpriselevel systems.

1. T DW I R E S E A R C H
T DW I CHECK L IS T RE P OR T
SEVEN STEPS TO
ACTIONABLE PERSONAL
ANALYTICS AND DISCOVERY
By David Stodder
Sponsored by
tdwi.org

2. JUNE 2012
T DW I CHECK L IS T RE P OR T
TABLE OF CONTENTS
SEVEN STEPS TO
ACTIONABLE PERSONAL 2 FOREWORD
ANALYTICS AND DISCOVERY 2 UMBER ONE
N
Determine user needs for personal BI and analytics
functionality.
By David Stodder 3 NUMBER TWO
Increase users’ personal productivity with enterprise
BI management.
3 NUMBER THREE
Provide users with complete views of data using
enterprise integration.
4 NUMBER FOUR
Strengthen performance management with personal
BI and analytics.
4 UMBER FIVE
N
Capitalize on in-memory computing for advanced
BI and analytics.
5 NUMBER SIX
Enable users to realize the power of dashboards and
visual analysis.
5 NUMBER SEVEN
Make collaboration a priority in the deployment of
personal BI and analytics.
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© 2012 by TDWI (The Data Warehousing InstituteTM), a division of 1105 Media, Inc. All rights
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3. TDWI CHECKLIST REPORT: SE VEN STEPS TO ACTION A BLE PERSON A L A N A LY TICS A ND DISCOVERY
FOREWORD NUMBER ONE
DETERMINE USER NEEDS FOR PERSONAL BI AND
ANALYTICS FUNCTIONALITY.
Organizations today want to be driven by data. They want to Some users are satisfied with standard BI reports as delivered
anchor daily and strategic decisions in a bedrock of solid, by IT, and they will continue to need such reports. However,
extensive, and timely analysis and reporting. Organizations want to research by TDWI and market analyst firms shows that the BI
reach out to more data sources and integrate diverse data to gain penetration rate in most organizations hovers between 15 and
single views of important business objects and domains, including 25 percent of the total user community—below what it could be.
customers, products, and services. Business intelligence (BI) and With application backlogs bulging, IT is struggling to satisfy user
analytics are essential technologies, methods, and processes to requirements, not to mention frequent “what-if” analysis requests
support data-driven decisions. However, in most organizations, from users who want to explore data fully on their own. A delay of
only a minority of users can access them. This needs to change if weeks or months before users can implement BI or get answers to
data-driven objectives are to be achieved. queries is a severe problem when organizations are under pressure
to compete on intelligence.
Past attempts at extending BI out to users in operations have
been met with mixed success. Users have gained significant Traditional IT methods of gathering requirements and developing
advantages, including better data quality, data access, and applications are proving inadequate due to the variety of users and
reporting; yet, they are frustrated by their inability to tailor the BI needs. As organizations attempt to extend BI systems, they are
environment to their personal needs. Customizing the environment discovering that users have widely varying levels of experience.
and performing ad hoc, what-if analytical queries can require Nontechnical users have sophisticated questions that they can
significant IT involvement. Many organizations are deepening only articulate once they have explored the data; yet, because they
their analytical power by investing in hiring specialists dedicated are not “power users” who know how to pose queries and navigate
to marketing, finance, or other business functions to implement schemas, they are frustrated.
data mining tools and methods; however, the mass of users share
As users become familiar with BI, their needs often grow more
only indirectly in the fruits of their labors. Most users cannot do
diverse. For some requirements, users need visual dashboards
advanced discovery analysis on their own.
that convey easily understood information. The same users might
Personal, self-service BI and analytics tools are now available need the ability to do forecasting calculations that they have
that can give users more control over how they view and access previously known how to do only with spreadsheets. Meeting such
data and the reports, dashboards, and visualizations they need diverse needs from even single users can prove a headache for IT
to perform their roles in the enterprise. Easier deployment and organizations, let alone when multiplied by hundreds or thousands
configuration, improved self-service features, and technology of users.
advances such as in-memory computing are giving users work
The deliverable in this step is to evaluate personal, self-service
environments that are robust enough to perform significant BI and
BI and analytics. New versions of existing enterprise BI tools
analytics processes without involving IT at every step, as has been
as well as recently introduced tools have features that enable
necessary in the past.
users to personalize how they view, access, analyze, and share
This TDWI Checklist Report details seven steps toward personal BI information. Other steps in this checklist will discuss specific
and analytics success. Note that IT is not left out of the picture; areas for implementing self-service features. The focus here is
on the contrary, this checklist shows that IT has an important to begin at the beginning: evaluate user requirements. If your
role to play in guiding users and provisioning enterprise data organization plans to expand BI and needs to address diverse
resources. Users are more productive when personal, self-service user requirements, the best practice is to evaluate tools that can
BI and analytics technologies are well integrated with enterprise- integrate with enterprise BI and data warehousing systems to take
level systems. the load off of IT and give users the choice of features they need.
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4. TDWI CHECKLIST REPORT: SE VEN STEPS TO ACTION A BLE PERSON A L A N A LY TICS A ND DISCOVERY
NUMBER TWO NUMBER THREE
INCREASE USERS’ PERSONAL PRODUCTIVITY WITH PROVIDE USERS WITH COMPLETE VIEWS OF DATA
ENTERPRISE BI MANAGEMENT. USING ENTERPRISE INTEGRATION.
“Self-service” BI and analytics should not translate into “going Life would be simpler if BI and analytic applications users needed to
it alone” for users. Otherwise, users will remain dissatisfied and access only one data source, but in reality, life is complex. In most
will miss out on the productivity benefits of more sophisticated organizations, data integration problems are a significant source of
BI and analytics. Indeed, most of the 75 to 85 percent of users cost and delay in getting information flowing to users so that they
who have not been implementing BI have been going it alone for can gain complete views of data about customers, product lines,
some time; they have been using spreadsheets and application- territories, and more. Data integration projects never happen without
specific reporting tools with data limited to departmental silos, a reason; usually a critical business need exists for complete and
spreadsheets, and specialized application databases. These users consistent views of data. Business environments can be dynamic,
often employ manual, custom-coded methods to pull in data from with mergers and acquisitions, divisional or territorial restructuring,
popular sources such as Salesforce.com. Both business users and regulatory compliance, and more creating a continuous need for new
IT can do without yet more chaos bought about by unmanaged and data integration solutions.
poorly integrated tool implementations.
Business users often require a mix of different types of data,
Productive, “personal” BI and analytics requires a managed including structured, detailed data, aggregate or dimensional
environment. Personal BI and analytics users need IT’s ability to data, and semi-structured or unstructured content. They also need
provision users with quality data that adheres to governance and access to external sources provided by third parties. In addition,
regulatory requirements for access and sharing. IT can help users since spreadsheets are commonly used, personal BI and analytics
across the enterprise collaborate more effectively by looking for systems cannot ignore the need to import or export data and cubes
redundancy and consolidating data, as well as cutting down on the to and from spreadsheets. It can become highly time-consuming
number of “shadow” data systems where possible. Then, with more and error-prone for users to map dimensions into hierarchies, or
shared sources, users will waste less time trying to determine who map dimensions and measures into cubes, on their own. Search
has the correct data. and index capabilities for finding semi- or unstructured content can
vary from user to user, leading to inconsistency with these types of
IT can use its unique view of how users throughout the organization
information.
are working with data to improve workload performance on shared
servers. IT can monitor which sources are under- or over-utilized Organizations typically need tools for data integration, profiling,
to address hot spots, take unneeded sources offline, and plan quality, and data relationship discovery to provide single views
for future growth. If some users are working with the same data of all data that is physically located in multiple sources. Data
sources or are building similar reports and dashboards, IT can warehousing, transformation, and content management tools can
develop managed reports and dashboards that relieve users of also provide the unified structure, metadata, hierarchy management,
having to build them on their own. If available, IT can implement and search taxonomies that are critical to simplified user access.
enterprise BI platforms to help users publish and share their reports Master data management (MDM) systems can play a unifying
as well as online analytical processing (OLAP) cubes that support role, providing reference sources for all data relevant to particular
greater overall productivity for all users while ensuring data quality. business objects such as customers.
Establishing the right balance between self service and IT The key deliverable with this step is to provide an enterprise data
management is vital to the success of personal BI and analytics. integration infrastructure to address the variety of users’ data
The deliverable in this step is to foster a beneficial working needs. An important success factor for personal, self-service BI and
relationship between IT and business users to support independence analytics is to provide this access without requiring users to get
but avoid chaos. under the hood with the details of getting to each source; this is
what enterprise data integration can manage.
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5. TDWI CHECKLIST REPORT: SE VEN STEPS TO ACTION A BLE PERSON A L A N A LY TICS A ND DISCOVERY
NUMBER FOUR NUMBER FIVE
STRENGTHEN PERFORMANCE MANAGEMENT WITH CAPITALIZE ON IN-MEMORY COMPUTING FOR
PERSONAL BI AND ANALYTICS. ADVANCED BI AND ANALYTICS.
Performance management goals focus on improving the alignment One of the most important technology developments today is the
of daily decisions and actions with the organization’s high-priority falling cost and expanding real estate of addressable computer
strategic goals. Performance management metrics, including memory. Adoption of 64-bit operating systems has made it easier
key performance indicators, can be a significant advance over for developers and users of BI and analytics systems to exploit
voluminous “data dumps” or reports that force managers and very large memory and bring powerful functions closer to the data.
executives to hunt for what’s important. Large memory allows users to perform functions against much
larger data sets.
Often tied to business management methods such as Balanced
Scorecard, Toyota Production System, or Six Sigma, metrics can With in-memory computing, the traditional I/O bottleneck
be vital for monitoring how well the organization is progressing constraint—where queries have to read information from tables
toward enterprise goals that involve more than one business stored on disk—becomes less of a factor. Users can perform, on
operation or function, such as higher customer satisfaction. TDWI their own, analyses that would be too slow with disk-dependent
Research has found that performance management is a highly systems and limited in scope because not enough data is
important objective for mobile BI, as it gives executives and available. Algorithms can run faster in memory, making real-time,
managers access to metrics while on the go. “speed of thought” analysis a reality. Processes that stand to
benefit from in-memory computing include:
Performance management can sometimes have a mixed reputation
because initiatives do not always help users understand what is • Predictive analytics. Organizations can score models
“actionable.” This is often due to the lack of integration between locally against data for better performance. Models can
the metrics and BI systems, which could provide critical drill- be deployed against large data volumes that are refreshed
down analysis, reporting, and collaboration capabilities. Without through continuous loading and transformation. Speed is
this integration, performance management can become a silo critical to making predictive insights relevant in real time
unto itself—if not separated into multiple silos owned by finance for online customer interaction, fraud detection, and more.
and business operations, each implementing separate and
• hat-if scenario building and write-back capabilities.
W
incompatible metrics. This can frustrate the very cross-functional
Traditional BI and analytics systems make it prohibitively
collaboration that performance management is meant to foster.
costly and slow to develop what-if scenarios for planning
Organizations should seek technology that can support common and forecasting. For users of tools that support these
models, hierarchies, and dependency mapping, so that while processes, in-memory computing can enable them to write
metrics may be different depending upon the business function back changes to the data to see the potential impact of
or objectives, there is a way to understand how they relate their analysis and share the data with other users.
to each other, how they roll up, and how they share common
• mproved OLAP performance and scalability. OLAP
I
definitions. This is critical for connecting higher-level performance
users can employ more and bigger dimensions and
management with underlying data sources. Organizations should
slice and dice through larger volumes of detailed data
evaluate whether they can integrate metrics mapping with their
with less need to build cubes and design aggregate
MDM processes to ensure that business object definitions are
tables to work around the I/O bottleneck.
shared across metrics.
The deliverable in this step is to evaluate how in-memory
The deliverable in this step is to ensure integration of performance
computing can enable users to explore data with far less concern
management metrics with personal BI and analytics. Critical areas
from IT about what iterative, ad hoc styles of investigation might
to examine include (1) whether the enterprise data integration
mean for performance. In-memory computing is not a silver bullet;
infrastructure is properly supporting users’ performance
organizations should evaluate how systems manage and refresh
management data sharing requirements, and (2) whether users
data in memory and synchronize it with data on disk. However,
have adequately powerful data servers to perform deeper analysis
in-memory’s potential is enormous for faster BI and analytics.
behind the metrics, including what-if scenarios and calculations.
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6. TDWI CHECKLIST REPORT: SE VEN STEPS TO ACTION A BLE PERSON A L A N A LY TICS A ND DISCOVERY
NUMBER SIX NUMBER SEVEN
ENABLE USERS TO REALIZE THE POWER OF MAKE COLLABORATION A PRIORITY IN THE DEPLOYMENT
DASHBOARDS AND VISUAL ANALYSIS. OF PERSONAL BI AND ANALYTICS.
Pictures can be worth thousands of data points, especially for Most users do not work alone, nor do they make decisions alone. Thus,
nontechnical users who need to grasp the importance of information it is critical for personal BI and analytics applications to make it easy
changes quickly and easily. Dashboards are already critical to many for workgroups to share reports and analysis, including visualizations.
BI and analytics applications as single interfaces for consolidating The simplest way that users collaborate is by e-mailing reports and
KPIs, scorecards, alerts, reports, graphs, charts, and visual drill- spreadsheets to each other. However, while integration between BI
down analysis through levels of aggregate and detailed data. TDWI and collaborative e-mail applications is important, this method can
Research has found that the rapid adoption of mobile platforms introduce security, data, and versioning errors that proliferate as
such as tablets is driving even stronger interest in data visualization users share reports, spreadsheets, and BI artifacts. In addition, this
and dashboards. Clear and compelling data visualization makes it method does not enable sharing of the full user workspace, including
easier for users on the go to connect insights to choices for action dashboards; the sharing is piecemeal.
based on the data.
A better course is to take advantage of technologies that support
Personal BI and analytics applications deployed in memory can sharing of dashboards and workspaces. Personal BI and analytics
give users more intensive data visualization experiences beyond tools that are Web-based rather than desktop-based can make
simple graphs and charts. Enhanced graphics, animation, real-time it easier to assign role privileges to broad user communities.
data feeds into graphics, and collaboration with others through Dashboards can then be managed and updated centrally, with access
graphics are some of the more advanced capabilities that in-memory provided via the Web. Users should also be able to publish their work
computing can support. These types of visualization can burn to a central BI and analytics hub so that others can view and work
processer cycles, making large memory (plus compression) ideal with their content. This way, user communities that are distributed
for supporting visual analysis. The evolving visualization features globally can all be working on shared materials over the Web on a
of in-memory BI and analytics applications may someday cause daily basis.
users to look back on older BI dashboards and be amazed that
Another key resource for collaboration is having a shared glossary,
visualization options were once so primitive.
dictionary, or other reference for standard definitions of data,
The key deliverable in this step is to give users more control business objects, and terms. Earlier, this report discussed MDM as a
over their dashboards and visualizations so they can personalize process and platform for developing and sharing common business
experiences based on their roles and information needs. Frontline definitions; glossaries and dictionaries can be important resources
sales, service, and support personnel, for example, can benefit from for higher-level MDM definitions, so it is important that they are
single views of customer data presented in a compelling visual well integrated. However, even if the organization has not deployed
fashion rather than through tabular data reports. Organizations MDM, establishing a shared resource is vital for user communities
should evaluate BI and analytics tools that enable users to easily to work well together without losing productivity due to confusion
determine the look and feel they want, including which charts, data, over definitions. Such resources are also valuable for tracking data
and text feeds they need in their dashboards. External, Web-based lineage, which is becoming important to organizations for data
content—including feeds coming from both internal and external governance and regulatory compliance.
social media networks—are popular if available.
The deliverable in this step is to make collaboration a priority.
With users free to personalize their visualization, however, IT will Organizations should evaluate technology resources that help users
need to provide guidance so that users do not get lost in the “eye to share their work and stay coordinated on definitions to avoid
candy.” Simpler is often better; organizations should encourage confusion and data inconsistency.
clear, actionable information delivery over clutter.
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7. TDWI CHECKLIST REPORT: SE VEN STEPS TO ACTION A BLE PERSON A L A N A LY TICS A ND DISCOVERY
ABOUT OUR SPONSOR ABOUT THE AUTHOR
David Stodder is director of TDWI Research for business
intelligence. He focuses on providing research-based insight
and best practices for organizations implementing BI, analytics,
performance management, data discovery, data visualization, and
IBM Business Analytics software delivers the actionable insights related technologies and methods. He is the author of a TDWI Best
decision makers need to achieve better business performance. IBM Practices Report on mobile BI and analytics and Checklist Reports
offers a comprehensive, unified portfolio of business intelligence, on data discovery and information management. He has chaired
predictive and advanced analytics, financial performance and TDWI conferences focused on BI agility, BI innovation, and big
strategy management, governance, risk and compliance, and data analytics.
analytic applications. With IBM software, companies can spot Stodder has provided thought leadership on BI, analytics,
trends, patterns, and anomalies, compare what-if scenarios, information management, and IT management for over two decades.
predict potential threats and opportunities, identify and manage Previously, he served as vice president and research director with
key business risks, and plan, budget, and forecast resources. With Ventana Research. He was the founding chief editor of Intelligent
these deep analytic capabilities, our customers around the world can Enterprise and served as editorial director for nine years. You can
better understand, anticipate, and shape business outcomes. reach him at dstodder@tdwi.org.
Please visit www.ibm.com/business-analytics. To request a call or
ask a question, go to www.ibm.com/business-analytics/contactus.
An IBM representative will respond to your inquiry within two
business days.
ABOUT TDWI RESEARCH
TDWI Research provides research and advice for business
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Research focuses exclusively on BI/DW issues and teams up with
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a specific project in business intelligence, data warehousing, or surrounding the deployment and use of business intelligence and
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