Define what is Business Intelligence, what is Big Data and the differences between them.

1. BI, Analytics and Big Data
A Modern-Day Perspective
By: Elad Israeli, Co-Founder, SiSense
http://www.sisense.com

2. WWW.SISENSE.COM
Business Intelligence (Analytics)
A set of theories,
methodologies,
processes,
architectures, and
technologies that
transform raw data
into meaningful and
useful information for
business purposes.

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This is a Report (= a query)

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This is a Dashboard (= several queries)

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…and BI/Analytics is:
The ability to create a new report, dashboard or just
get a new analytic question answered in real-time, or
at least in-time.

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What is Big Data?
A collection of data sets so large and complex
that it becomes difficult to process using on-
hand database management tools or traditional
data processing applications
Due to its technical nature, the same challenges
arise in Analytics at much lower volumes than
what is traditionally considered Big Data.

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..so Big Data Analytics is:
The same as ‘Small Data’ Analytics, only with the
added challenges (and potential) of large datasets
(~50M records or 50GB size, or more)
Challenges, such as:
• Data storage and management
• De-centralized/multi-server architectures
• Performance bottlenecks, poor responsiveness
• Increasing hardware requirements

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BI and Analytics
Projects

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Approaches to The Challenge
1. Project-Specific:
– The development of a specific dashboard/report
– An isolated initiative, with no forward-looking
implications from the prospect’s perspective
2. Solution-Oriented:
– The development of a specific dashboard/report,
with future ones (known or unknown) in mind

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E.K.G: Solution-Oriented vs. Project- Specific
Time
New Report New Report
BI/Analytics (Solution-Oriented)
Time
New Report New Report New Report
Report/Dashboard Project (Project-Specific)

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BI/Analytics E.K.G
Time
New Report New Report
The rate at which new reports are introduced into critical processes should
increase over-time, due to:
• Completed integration, customization & adaptation
• Time for training to sink in
• Adoption (more users generating reports)
New Report = Answer To New Question = New Insight

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How Raw Data Becomes Insight
Connect To
Source
Load &
Store
Clean &
Standardize
Grant
Access
Define
Queries
Format
The Report
Share the
Report
Respond to
Feedback
ETL / Data Management
BI/Analytics/Visualization

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Data Warehouse
• Clean and accurate data
recognized as the only
real business ‘truth’
• A central repository of
data which is created by
integrating data from
one or more disparate
sources
• Stores current as well as
historical data

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Existing Data Landscapes
Owner:
IT
Owner:
IT or Business
DW
• The data is in its detailed form (raw data)
• The data is located in multiple places
• The data may be dirty (i.e. entry-errors)
• The data is accessible to whoever owns
the application/database
• The data is not centralized
With an existing Data Warehouse Without an existing Data Warehouse
• The data is in its detailed form (raw data)
• The data clean (was already processed)
• The data is usually only directly accessible to IT
• The data is centralized (single version)
Operational DB
Application DB
Files
Operational DB
Application DB
Files
ETL
Data Marts
or OLAP Cubes (optional)

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Traditional BI/Analytics
Architectures
(Old-School)

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Traditional BI/Analytics Architectures
End-Users (Business) End-Users (Business)
Detailed
Dirty
Unstructured
Centralized / Data Warehouse Non-Centralized / No DW
Summarized
De-centralized
Clean
Structured
Detailed
Dirty
Unstructured
Detailed
Dirty
Unstructured
DW
Data Marts
or OLAP
Cubes
Owner:
IT
Owner:
IT or Business

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Traditional Architectures - Comparison
Centralized / DW Non-Centralized / No DW
Approach Solution-oriented Project-specific
Data Quality & Accuracy Higher Lower
Scalability Higher Lower
Single Version of the Truth Yes No
Initial Investment Higher Lower
Level of Detail Summarized Granular
Owner IT IT or Business (optional)
Implementation Time Longer Shorter
Technical Complexity Higher Lower
Advantage / Disadvantage

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Modern-Day BI/Analytics
Architectures

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Modern-Day BI/Analytics - Focus
• Self-Service
– Empower business users of varying skill-levels
– Keep IT in control, without becoming a bottleneck
• Agility
– Fast turnaround for new requirements
• Scalability
– Handle large, or rapidly growing volumes of data
– Handle fast, unpredictable usage patterns and adoption

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Modern BI/Analytics – How?
• Full-Coverage Solution
– Provide all functionality required, from data
management, ETL and end-user analytics
• Utilize modern technology
– Columnar databases
– In-Chip analytics technology
– Support for 21st century chip-sets

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Architecture: With a Data Warehouse
End-Users (Business)
Detailed
Centralized
Clean
Structured
Detailed
Dirty
Unstructured
DW
Owner:
IT
End-Users (Business)
Summarized
De-centralized
Clean
Structured
DW
Marts
or OLAP
Cubes
Owner:
IT
Modern Traditional
ElastiCube

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Modern vs. Traditional (DW)
Centralized / DW SiSense Architecture
Approach Solution-oriented Solution-oriented
Data Quality & Accuracy High High
Scalability High High
Single Version of the Truth Yes Yes
Initial Investment Higher Lower
Level of Detail Summarized Granular
Owner IT IT or Business (optional)
Implementation Time Longer Shorter
Technical Complexity Higher Lower
Advantage / Disadvantage

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Architecture: Without a Data Warehouse
End-Users (Business)
Detailed
Centralized
Clean
Structured
Detailed
Dirty
Unstructured
ElastiCube
Owner:
IT or Business
End-Users (Business)
Owner:
IT or Business
Detailed
Non-Centralized
Dirty
Unstructured
Detailed
Dirty
Unstructured
Modern Traditional

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Modern vs. Traditional (No DW)
Non-Centralized / No DW Modern Architecture
Approach Project-oriented Solution-oriented
Data Quality & Accuracy Lower Higher
Scalability Lower Higher
Single Version of the Truth No Yes
Initial Investment Lower Lower
Level of Detail Granular Granular
Owner IT or Business (optional) IT or Business (optional)
Implementation Time Short Short
Technical Complexity Lower Lower
Advantage / Disadvantage

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You Can Get Modern
BI/Analytics Today!
Schedule Your Free Demo Now!
http://pages.sisense.com/demo-request.html