This document discusses business analytics and next-generation business intelligence tools. It describes how business analytics is used to gain insights from data to inform business decisions and optimize processes. It also explains that successful business analytics depends on data quality, skilled analysts, and organizational commitment to data-driven decision making. The document then profiles the capabilities of next-generation BI tools, including their support for top-down reporting, bottom-up analysis, self-service capabilities, and their ability to provide insights quickly through in-memory processing and interactive visualizations.

1. Definition
business analytics (BA)
This was last updated in August 2010
Editorial Director: Margaret Rouse
http://searchbusinessanalytics.techtarget.com/definition/business-ana
報告人 陳衍華
2012.07.04
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2. BA
• BA is used to gain insights that inform business
decisions and can be used to automate and
optimize business processes.
• Data-driven companies treat their data as a
corporate asset and leverage it for competitive
advantage.
• Successful business analytics depends on
data quality, skilled analysts who understand the
technologies and the business and an
organizational commitment to data-driven
decision making
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3. BA (Cont.)
• Exploring data to find new patterns and
relationships (data mining)
• Explaining why a certain result occurred
(statistical analysis, quantitative analysis)
• Experimenting to test previous decisions
(A/B testing, multivariate testing)
• Forecasting future results (
predictive modeling, predictive analytics)
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4. BA (Cont.)
• Once the business goal of the analysis is
determined, an analysis methodology is
selected and data is acquired to support
the analysis
• Analytic tools range from spreadsheets
with statistical functions to complex data
mining and predictive modeling
applications
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5. BI vs BA Business Intelligence Business Analytics
What happened? Why did it happen?
Answers the When? Will it happen again?
questions: Who? What will happen if we
change x?
How many? What else does the
data tell us that
never thought to
ask?
Reporting (KPIs, Statistical/Quantitative
Includes: metrics) Analysis
Automated Data Mining
Monitoring/Alerting
(thresholds) Predictive Modeling
Dashboards
Scorecards Multivariate Testing
OLAP (Cubes, Slice &
Dice, Drilling) 5
Ad hoc query

6. In-Memory Analysis
Delivering Insights
at the Speed of Thought
BY WAYNE ECKERSON
Director of Research, Business Applications and
Architecture Group, TechTarget, December 2011
報告人 陳衍華
2012.07.04
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7. THE PURPOSE OF THIS REPORT
• profile the capabilities of next-
generation business intelligence (BI) tools
with emphasis on new visual analysis
tools
• and in-memory processing.
• interviews with BI practitioners and
briefings with sponsors of this report.
• also based on a survey of 240 BI
professionals
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8. BI framework 2020
Casual
Power & Casual User – 80%
user
Power User- 20%
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9. • Continuous intelligence
– Accelerates the delivery of information to users, and in some
cases, correlates events and triggers alerts when it’s time for
humans to intervene
• Analytics intelligence
– gives power users a variety of desktop analysis tools to explore
and analyze data in an unfettered fashion so they can answer
unanticipated questions.
• content intelligence
– both casual and power users can access and analyze
to include semi-structured and unstructured data.
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10. Reporting vs. Analysis
• At the highest level, the two primary BI
applications—reporting and analysis—are
fundamentally different applications with
very different types users and unique
workloads, design frameworks and
architectures.
• Top down and Bottom up
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11. Reporting Versus Analysis: Distinct Workloads, Users and Architectures
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12. Top down
• Reporting
– monitors business activity using metrics that are aligned with
strategic goals and objectives
• To design reports and dashboards—visual exception
reports
– need to know in advance the questions casual users are going
to ask
– will differ depending on their roles in the organization.
• To create reports
– the typical organization first builds a data warehouse or data
mart that contains a model of how
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13. Bottom up
• analysis is a “bottom-up” activity in which
analytical experts use a variety of tools
• power users often need to access a variety of
data sources, explore and merge the data
• analyze the results and present their findings in
a concise and comprehensive way to business
executives and managers.
• answering unanticipated business questions
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14. Misplaced expectations
• .The challenges that most organizations
experience with BI tools
– often have less to do with vendor products
than with customer expectations about the
products.
• Recognize that you need both top-down
• and bottom-up BI tools and that these
tools need to work together, not against
each other.
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15. Next-Generation BI Capabilities
• Top-down capabilities
• Bottom-up capabilities
• Self-service
• END-USER CHARACTERISTICS
– enable users to change what they see on the screen without IT
or power-user involvement
– Interactive , Visual, Flexible, Analytical, Predictive, Collaborative,
Mobile
• IT CHARACTERISTICS
– Fast, Deploys quickly, Any data source, Scalable, Reusable,
Maintainable, Manageable, Comprehensive, Portable
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16. Top-Down and Bottom-Up Approaches to Self-Service BI
Self-service BI promises to provide business users with easy-to-use tools that enable them
to get the information they want, when and how they want it without IT or power-user
intervention.
Functionality on
Demand
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17. Mapping Types of Users to Self-Service
Hierarchies
Both casual users
and power users both
consume and produce
reports and analyses,
but power users exploit
more advanced
features.
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18. In-Memory Databases vs. Intelligent Caches
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19. In-Memory Databases vs. Intelligent Caches (cont.)
• Intelligent caches speed response times for pre-run
queries
– still giving users access to data stored in remote databases (of
any size).
• In-memory databases speed response times for all
queries running against a downloaded data set
– but don’t provide direct access to remote data.
• Some tools use a hybrid approach users can choose to
query remote data or download data to a local server or
desktop to improve query performance.
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