NEXT GENERATION
BUSINESS ANALYTICS TECHNOLOGY TRENDS
TECHNOLOGIES AND TECHNIQUES
FOR
BUSINESS INTELLIGENCE & PERFORMANCE MANAGEMENT
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/.
Presenters
Michael Beller
10 years of executive management experience leading major growth and change initiatives as
COO
CIO
EVP of Strategy Management
15 years of management consulting experience helping clients with operations and IT strategy, planning, and execution
Alan Barnett
25 years of retail management experience with Steve and Barry’s, Levitz Furniture, Loehmann’s, Victoria’s Secret Stores, and Barney’s New York
Merchandising
Planning
Information Technology
Frequent speaker industry events on systems and operational planning
© 2009 LIGHTSHIP PARTNERS LLC 2
• 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
© 2009 LIGHTSHIP PARTNERS LLC 3
Learning Objectives
• 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
Agenda
© 2009 LIGHTSHIP PARTNERS LLC 4
Business analytics is more than just traditional business intelligence and reporting
Business Intelligence
• Oriented to standard and consistent metrics and analysis
• Focused on dashboards and pre-defined reports
• Primarily answers predefined questions
• Provides end users indirect raw data access through cubes, reports, and summarized data
• Exception based reporting
Business Analytics
• Oriented towards ad-hoc analysis of past performance
• Focused on interactive and investigative analysis by end users
• Used to derive new insights and understanding
• Explore the unknown and discover new patterns
• Relies on low-level data to provide visibility to unexpected activity
BUSINESS ANALYTICS VS. BUSINESS INTELLIGENCE
© 2009 LIGHTSHIP PARTNERS LLC 5
BUSINESS ANALYTICS VS. BUSINESS INTELLIGENCE
© 2009 LIGHTSHIP PARTNERS LLC 6
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
• What is business analytics?
Continuous iterative exploration and investigationof past business performance to gain insight and drive business planning
• What impacts and drives business analytics?
The quantity and detail of critical business transaction and related datacombined 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.
BUSINESS ANALYTICS VS. BUSINESS INTELLIGENCE
Business Analytics provides end users tools and data to explore and develop broader and deeper business insight
© 2009 LIGHTSHIP PARTNERS LLC 7
“there are $8B (yes, billion) of internally developed analytic applications with Excel as their front end. The BI players treat the output to Excel as a feature” [3]
• Level of granularity
Transaction data is summarized and aggregated for analysis
• Historical context
Technical constraints often lead to less than optimal data retention
• Consolidated view
Data warehouses often focus on closely related systems, not enterprise views
Multiple disparate data silosWebsites and ecommerceSupply chainEnterprise resource planning (ERP)CRMFinancialOther, e.g., weather, competitor, etc.
CHALLENGES FOR CURRENT BA ENVIRONMENTS
Organizations struggle to aggregate sufficient breadth and depth of data for thorough Business Analytics
© 2009 LIGHTSHIP PARTNERS LLC 8
“80% of companies use three or more business intelligence (BI) products” [1]
• Complex tier of tools
ETL and EAI platforms
Data warehouses
Dashboards and reports
Ad-hoc analysis
• CostlyCapital
Effort
Duration
• Oriented to IT
Cumbersome for end users
Puts IT in the middle
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
© 2009 LIGHTSHIP PARTNERS LLC 9
Complexity leads to fragile systems and long lead times for changes
• Understanding of past performance leads to quality of future planning
• End users often develop cursory and summary level insight into business performance which leads to sub optimal plans
• BI tools have multiple versions of the truth
Uncertainty
Wasted effort
CHALLENGES FOR CURRENT BA ENVIRONMENTS
Current BI environments pose numerous challenges for Business Analytics and impact quality of business planning
© 2009 LIGHTSHIP PARTNERS LLC 10
“the only way to make a difference with analytics is to take a cross-functional, cross-product, cross-customer approach” [5]
The BA market is dynamic, rapidly expanding and poised for high growth and adoption beyond early adopters
Business trends
• Companies look to leverage investments in ERP and legacy systems
• Economic environment driving low risk projects with quick payback
• Existing data warehouse and reporting systems have limitations
Cost
Flexibility
Data Quantity and Granularity
Technology trends
• Massively scalable data and processing clouds for data aggregation, storage, and analysis
• SaaS and managed service offerings for low cost quick payback projects
Minimal, if any, capital
Fast implementation
• Next generation tools, portals, and visualization for data analysis and presentation
NEXT GENERATION BA VENDORS AND TOOLS
© 2009 LIGHTSHIP PARTNERS LLC 11
• 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
NEXT GENERATION BA VENDORS AND TOOLS
Next generation BA vendors and tools address current limitations and complement existing environments
© 2009 LIGHTSHIP PARTNERS LLC 12
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
Why are companies adopting new SaaS BI solutions?
NEXT GENERATION BA VENDORS AND TOOLS
© 2009 LIGHTSHIP PARTNERS LLC 13
Source: BeyeNetwork Research Report – May 2009
By one expert estimate, there are 2 new players entering the BI and BA market every week
NEXT GENERATION BA VENDORS AND TOOLS
© 2009 LIGHTSHIP PARTNERS LLC 14
QUESTIONS?
© 2009 LIGHTSHIP PARTNERS LLC 15
THANK YOU!
MIKE BELLER [email protected]
ALAN BARNETT [email protected]
WWW.LIGHTSHIPPARTNERS.COM
© 2009 LIGHTSHIP PARTNERS LLC 16
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.
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_Difference_?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/
End Notes and References
© 2009 LIGHTSHIP PARTNERS LLC 17