Next Generation Business Analytics Technology Trends


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Next Generation Business Analytics Technology Trends

  1. 1. 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
  2. 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 © 2009 LIGHTSHIP PARTNERS LLC 2
  3. 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 © 2009 LIGHTSHIP PARTNERS LLC 3
  4. 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 © 2009 LIGHTSHIP PARTNERS LLC 4
  5. 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 © 2009 LIGHTSHIP PARTNERS LLC 5
  6. 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 © 2009 LIGHTSHIP PARTNERS LLC 6
  7. 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. © 2009 LIGHTSHIP PARTNERS LLC 7
  8. 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. © 2009 LIGHTSHIP PARTNERS LLC 8
  9. 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 © 2009 LIGHTSHIP PARTNERS LLC 9
  10. 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 © 2009 LIGHTSHIP PARTNERS LLC 10
  11. 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. 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 © 2009 LIGHTSHIP PARTNERS LLC 12
  13. 13. NEXT GENERATION BA VENDORS AND TOOLS Why are companies adopting new SaaS BI solutions? Source: BeyeNetwork Research Report – May 2009 © 2009 LIGHTSHIP PARTNERS LLC 13
  14. 14. NEXT GENERATION BA VENDORS AND TOOLS By one expert estimate, there are 2 new players entering the BI and BA market every week © 2009 LIGHTSHIP PARTNERS LLC 14
  16. 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 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. © 2009 LIGHTSHIP PARTNERS LLC 16
  17. 17. End Notes and References 1. Kelly, Jeff. “Key considerations for business intelligence platform consolidation.”, February 17, 2009. . 2. Kirk, Jeremy. “'Analytics' buzzword needs careful definition.”, February 7, 2006. . 3. Gnatovich, Rock. “Business Intelligence Versus Business Analytics--What's the Difference?”, February 27, 2006. e_?page=1 . 4. Hagerty, John. “AMR Research Outlook: The New BI Landscape.”, December 19, 2008. 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. 7. Imhoff, Claudio, and Colin White. “Pay as You Go: SaaS Business Intelligence and Data Management,” May 20, 2009. © 2009 LIGHTSHIP PARTNERS LLC 17