The document discusses next generation business analytics technologies and techniques. It describes how business analytics goes beyond traditional business intelligence by allowing for more ad-hoc analysis of data to gain new insights. Current business intelligence tools have limitations in terms of data quantity, flexibility, and ease of use. Next generation business analytics vendors are addressing these challenges with cloud-based tools that provide massive data storage and processing capabilities, as well as user-friendly interfaces for interactive data exploration. These new tools complement and enhance existing business intelligence environments.

1. NEXT GENERATION
BUSINESS ANALYTICS
TECHNOLOGIES AND TECHNIQUES
FOR
BUSINESS INTELLIGENCE & PERFORMANCE MANAGEMENT
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2. Presenters
Michael Beller Alan Barnett
10 years of executive 25 years of retail management
management experience leading experience with Steve and
major growth and change Barry’s, Levitz Furniture,
initiatives as Loehmann’s, Victoria’s Secret
COO Stores, and Barney’s New York
CIO Merchandising
EVP of Strategy
Management Planning
15 years of management Information Technology
consulting experience helping Frequent speaker industry
clients with operations and IT events on systems and
strategy, planning, and operational planning
execution
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3. Learning Objectives
• Understand limitations of current Business Intelligence tools
• Discover how next generation tools for Business Analytics can supplement
and enhance current BI environments
• Identify vendors and characteristics of next generation Business Analytics
tools
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4. Agenda
• Business analytics vs. business intelligence
What is Business Analytics?
• Challenges for current BA environments
IT Limitations – Data and Tools!
Business Impact
• Next generation BA vendors and tools
Business trends
Technology trends
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5. BUSINESS ANALYTICS VS. BUSINESS INTELLIGENCE
Business analytics is more than just traditional business
intelligence and reporting
Business Intelligence Business Analytics
• Oriented to standard and consistent • Oriented towards ad-hoc analysis of
metrics and analysis past performance
• Focused on dashboards and pre- • Focused on interactive and
defined reports investigative analysis by end users
• Primarily answers predefined • Used to derive new insights and
questions understanding
• Provides end users indirect raw • Explore the unknown and discover
data access through cubes, reports, new patterns
and summarized data
• Relies on low-level data to provide
• Exception based reporting visibility to unexpected activity
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6. BUSINESS ANALYTICS VS. BUSINESS INTELLIGENCE
Part of routine daily, monthly, and
quarterly processes – not a sporadic or
exception based exercise
“Peel the onion” – answers to some questions generate
more questions – dive deeper and deeper into the data
Explore the unknown, search for new
patterns and new findings and new metrics
Investigate exceptions and
anomalies, research hypotheses
Gain broader and deeper
insight and understanding
into past performance
Stay focused on goal to improve
business planning and overall
business performance
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7. BUSINESS ANALYTICS VS. BUSINESS INTELLIGENCE
Business Analytics provides end users tools and data to
explore and develop broader and deeper business insight
“there are $8B (yes, billion) of
• What is business analytics? internally developed analytic
applications with Excel as
Continuous iterative exploration and investigation
their front end. The BI players
of past business performance treat the output to Excel as a
to gain insight and drive business planning feature” [3]
• What impacts and drives business analytics?
The quantity and detail of critical business transaction and related data
combined with powerful and flexible data analysis tools
• How do you improve business analytics?
Use next generation technologies to lower data warehousing and IT infrastructure
costs,
Store larger amounts of historical data at granular levels of detail, and
Provide ad-hoc analysis and data mining without IT development efforts.
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8. CHALLENGES FOR CURRENT BA ENVIRONMENTS
Organizations struggle to aggregate sufficient breadth and
depth of data for thorough Business Analytics
• Level of granularity
Transaction data is summarized and
aggregated for analysis
“80% of
• Historical context companies use
Technical constraints often lead to three or more
less than optimal data retention business
intelligence (BI)
• Consolidated view products” [1]
Data warehouses often focus on
closely related systems, not enterprise
views
Multiple disparate data silos
Websites and ecommerce
Supply chain
Enterprise resource planning (ERP)
CRM
Financial
Other, e.g., weather, competitor, etc.
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9. CHALLENGES FOR CURRENT BA ENVIRONMENTS
Traditional data analysis and reporting tools are oriented to
IT developers and difficult to modify at the speed of business
• Complex tier of tools
ETL and EAI platforms
Data warehouses
Dashboards and reports
Ad-hoc analysis
• Costly
Capital
Effort Complexity leads to fragile
Duration systems and long lead times
for changes
• Oriented to IT
Cumbersome for end users
Puts IT in the middle
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10. CHALLENGES FOR CURRENT BA ENVIRONMENTS
Current BI environments pose numerous challenges for
Business Analytics and impact quality of business planning
• Understanding of past performance
leads to quality of future planning
• End users often develop cursory
and summary level insight into
business performance which leads “the only way to make a
difference with analytics is
to sub optimal plans
to take a cross-functional,
• BI tools have multiple versions of cross-product, cross-
customer approach” [5]
the truth
Uncertainty
Wasted effort
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11. NEXT GENERATION BA VENDORS AND TOOLS
The BA market is dynamic, rapidly expanding and poised for
high growth and adoption beyond early adopters
Business trends Technology trends
• Companies look to leverage • Massively scalable data and
investments in ERP and legacy processing clouds for data
systems aggregation, storage, and analysis
• Economic environment driving low • SaaS and managed service offerings
risk projects with quick payback for low cost quick payback projects
• Existing data warehouse and Minimal, if any, capital
reporting systems have limitations Fast implementation
Cost • Next generation tools, portals, and
Flexibility visualization for data analysis and
presentation
Data Quantity and Granularity
© 2009 LIGHTSHIP PARTNERS LLC 11

12. NEXT GENERATION BA VENDORS AND TOOLS
Next generation BA vendors and tools address current
limitations and complement existing environments
• Data granularity, history, and
consolidation
Columnar, in-memory, and other
database technologies require
minimal data modeling and can load
diverse and complex data
• Technology cost, complexity, and end
user access
SaaS and managed service require
minimal initial cost
Cloud storage and processing enable
massive scalability at reasonable cost
SAP, Oracle, and IBM purchased three major BI vendors (Business Objects, Hyperion, and
Cognos) within months of one another – a clear sign of the importance of both BI and BA
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13. NEXT GENERATION BA VENDORS AND TOOLS
Why are companies adopting new SaaS BI solutions?
Source: BeyeNetwork Research Report – May 2009
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14. NEXT GENERATION BA VENDORS AND TOOLS
By one expert estimate, there are 2 new players entering the
BI and BA market every week
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15. QUESTIONS?
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16. MIKE BELLER MBELLER@LIGHTSHIPPARTNERS.COM
ALAN BARNETT ABARNETT@LIGHTSHIPPARTNERS.COM
WWW.LIGHTSHIPPARTNERS.COM
THANK YOU!
This work is licensed under the Creative Commons Attribution-Share Alike 3.0 Unported License.
To view a copy of this license, visit http://creativecommons.org/licenses/by-sa/3.0/.
Lightship Partners LLC, Lightship Partners LLC (stylized), Lightship Partners LLC Compass Rose are trademarks or service marks of Lightship Partners
LLC in the U.S. and other countries. Any other unmarked trademarks contained herein are the property of their respective owners. All rights reserved.
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17. End Notes and References
1. Kelly, Jeff. “Key considerations for business intelligence platform consolidation.”
searchdatamanagement.techtarget.com, February 17, 2009. http://tinyurl.com/lr4usk .
2. Kirk, Jeremy. “'Analytics' buzzword needs careful definition.” InfoWorld.com, February 7, 2006.
http://www.infoworld.com/t/data-management/analytics-buzzword-needs-careful-definition-567 .
3. Gnatovich, Rock. “Business Intelligence Versus Business Analytics--What's the Difference?” CIO.com,
February 27, 2006.
http://www.cio.com/article/18095/Business_Intelligence_Versus_Business_Analytics_What_s_the_Differenc
e_?page=1 .
4. Hagerty, John. “AMR Research Outlook: The New BI Landscape.” AMRresearch.com, December 19, 2008.
http://www.amrresearch.com/Content/View.aspx?compURI=tcm%3a7-
39121&title=AMR+Research+Outlook%3a+The+New+BI+Landscape.
5. Thomas H. Davenport. “Realizing the Potential of Retail Analytics.” Babson Working Knowledge Research
Center, June 2009.
6. van Donselaar, K.H.; Gaur, V.; van Woensel, T.; Broekmeulen, R. A. C. M.; Fransoo, J. C.; “Ordering Behavior in
Retail Stores and Implications for Automated Replenishment” Revised working paper dated May 12, 2009;
first version: January 31, 2006. http://papers.ssrn.com/abstract=1410095
7. Imhoff, Claudio, and Colin White. “Pay as You Go: SaaS Business Intelligence and Data Management,” May 20,
2009. http://www.b-eye-research.com/
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