This document discusses next generation big data business intelligence (BI). It describes traditional BI and how it is evolving to incorporate big data. Key points: - Traditional BI includes dashboards, KPIs, OLAP, reporting, and forecasting to provide insights from structured data. - Next generation BI leverages big data technologies like Hadoop and NoSQL databases to handle larger and more diverse unstructured data in batch and real-time. - This enables deeper insights through analytics across all data, from basic queries to advanced predictive modeling and streaming analysis. - The modern BI stack incorporates big data technologies alongside traditional data warehousing and OLAP for integrated insights.

1. Next Generation Big Data BI
STANLEY WANG
SOLUTION ARCHITECT, TECH LEAD
@SWANG68
http://www.linkedin.com/in/stanley-wang-a2b143b

2. What is Business Intelligence?
Improving organizations by providing
business insights to all employees leading to
better, faster, more relevant decisions
• Business Intelligence is the processes,
technologies, and tools that help organization
change data into information, information
into knowledge and knowledge into plans
that guide organization.
• Collections of technologies and approaches
for gathering, storing, analyzing and providing
access to data to help enterprise users make
better business decisions.

3. • Dashboards
• KPI
• OLAP
• Forecasting
• Reporting

4. • BI dashboards
Provide a customized snapshot of daily
operations, and assist the user in identifying
problems and the source of those problems, as
well as providing valuable, up-to-date
information about financial results, sales and
other critical information – all in one place
Components of Business Intelligence
• Key Performance Indicators
Provide simplified KPI management and tracking
with powerful features, formulae and expressions,
and flexible frequency, and threshold levels. This
module enables clear, concise definition and
tracking of performance indicators for a period,
and measures performance as compared to a
previous period. Intuitive, color highlighters
ensure that users can see these indicators in a
clear manner and accurately present information.
Users can further analyze performance with easy-
to-use features like drill down, drill
through, slice and dice and graphical data mining
• Graphical Reporting
Deliver web-based BI reports to anyone (or
everyone) in the organization within minutes! The
BI suite is simple to use, practical to implement
and affordable for every organization. With our BI
reporting and performance reporting module, you
just point-and-click and drag-and-drop and you
can instantly create a report to summarize your
performance metrics, or operational data
• Graphical OLAP
Makes it easy for users to find, filter and analyze
data, going beyond numbers, and allowing users
to visualize the information with eye-catching,
stunning displays, and valuable indicators and
gauges, charts, and a variety of graph types from
which to choose
• Prediction and Forecasting
Use historical product, sales, pricing, financial,
budget and other data, and forecasts the
measures with numerous time series options,
e.g., year, quarter, month, week, day, hour or
even second to improve your planning process

6. Traditional BI Architecture

7.  BI platform:
 Report
 OLAP
 Dashboard
 KPIs
 Charts
 Data Mining
 GEO-referenced analysis
 Free Inquiry (QbE)
 Collaboration
 ETL
 Enterprise level
 Integration platform (not a closed product)‫‏‬
 Multi engine (also mixing OS and proprietary)‫‏‬
 Scalability
 Security and visibility

8. BI Analytical Model
Analytical Model

9. BI Graphical Reports

10. BI Dashboard Documents

11. What Big Data Intelligence Looks Like?

12. When BI meets Big Data?
• BI with Big Data delivers greater acuity, deeper insight and
smarter focus;
• BI with Big Data leads to a proliferation of market
projections and forecasts;

13. Big Data Drive for Deeper Understanding

14. The Evolution of Business Intelligence
Big Data:
Batch Processing &
Distributed Data Store
Hadoop/Spark; HBase/Cassandra
BI Reporting
OLAP &
Data Ware House
Business Objects, SAS,
Informatica, Cognos
Interactive Business
Intelligence &
In-memory RDBMS
QliqView, Tableau, HANA
Big Data:
Real Time &
Single View
Graph Databases
1990’s 2000’s 2010’s
Speed
Scale
Scale
Speed

15. Business Intelligence Classifications
Traditional Analytics
1st Generation Analytics (Query & Reporting)
2nd Generation Analytics (OLAP, Data Warehousing)
Advanced Analytics/Optimization
Rules
Predictive Analytics
Real-time and traditional Data Mining
Stream Analytics*
Real-time, continuous, sequential analysis
(ranging from basic to advanced analytics)
* In line of stream analytics, “embedded analytics,” although architecturally
different, could potentially play the same role
3rd-Generation BI
Legacy BI
“New Traditional” Analytics
“2.5-Gen” Analytics (In-Memory OLAP, Search-Based)

16. Business Intelligence Use
Cases
Traditional Analytics
1st Generation Analytics (Query & Reporting)
2nd Generation Analytics (OLAP, Data Warehousing)
Advanced Analytics/Optimization
Rules
Predictive Analytics
Real-time and traditional Data Mining
Stream Analytics*
Real-time, continuous, sequential analysis
(ranging from basic to advanced analytics)
* In lieu of stream analytics, “embedded analytics,” although architecturally
different, could potentially play the same role
“New Traditional” Analytics
“2.5-Gen” Analytics (In-Memory OLAP, Search-Based)
Example Target Solutions:
Fraud Detection / Risk
CRM Analytic
Supply Chain Optimization
RFID / Spatial Data
Other High-VolumeFocus on what is
happening RIGHT NOW
Real-Time Threshold
Focus on what will
happen
Analytic applications that
apply statistical
relationships in the form
of RULES
Focus on what did
happen
Turning data into
information is limited by the
relationships which the
end-user already knows to
look for.
Data mining to determine
why something
happened by unearthing
relationships that the
end-user may not have
known existed.

17. Modern Big Data BI Reference Architecture

18. A New BI Stack with Big Data

19. Ficus Big Data BI Architecture

21. Location Intelligence Engine

22. Social Business Intelligence