Here are potential responses to the questions:1. Yes, Birst is designed to support repetitive self-service analytics. Its multi-tenant architecture allows different groups to independently manage and analyze their own data sources alongside centralized data, without impacting other groups. 2. Birst scales horizontally to meet increasing performance demands. Its distributed architecture leverages cloud infrastructure to automatically add nodes and optimize queries across large clusters. Performance monitoring ensures workloads are distributed efficiently.3. Birst's automated multi-dimensional database optimizes analytic workloads like dashboards, reports, and ad-hoc queries against large, diverse data sets. It automatically materializes aggregates for fast response times and integrates data from multiple sources into a unified semantic model
The Briefing Room with Robin Bloor and Birst
Live Webcast on Feb. 5, 2013
All the effort that goes into data governance can quickly be lost if effective guard rails aren't in place. However, end users invariably need additional data sets in order to get a complete picture of what's happening. All too often, some or all of those additional data sources have not yet run the gauntlet of governance. Striking a balance between core and contextual data can help ensure that your business stays on top of opportunities without straying from the path.
Check out this episode of The Briefing Room to learn from veteran Analyst Dr. Robin Bloor, who will explain the nuances of integrating governed and ungoverned data in ways that business users can easily leverage. He'll be briefed by Brad Peters of Birst who will demonstrate how managed data mashups can provide the kind of flexibility and agility that can lead to valuable insights. He'll explain how Birst's architecture can significantly lighten the load on IT without sacrificing data integrity, security or governance.
Visit: http://www.insideanalysis.com
Semelhante a Here are potential responses to the questions:1. Yes, Birst is designed to support repetitive self-service analytics. Its multi-tenant architecture allows different groups to independently manage and analyze their own data sources alongside centralized data, without impacting other groups. 2. Birst scales horizontally to meet increasing performance demands. Its distributed architecture leverages cloud infrastructure to automatically add nodes and optimize queries across large clusters. Performance monitoring ensures workloads are distributed efficiently.3. Birst's automated multi-dimensional database optimizes analytic workloads like dashboards, reports, and ad-hoc queries against large, diverse data sets. It automatically materializes aggregates for fast response times and integrates data from multiple sources into a unified semantic model
Semelhante a Here are potential responses to the questions:1. Yes, Birst is designed to support repetitive self-service analytics. Its multi-tenant architecture allows different groups to independently manage and analyze their own data sources alongside centralized data, without impacting other groups. 2. Birst scales horizontally to meet increasing performance demands. Its distributed architecture leverages cloud infrastructure to automatically add nodes and optimize queries across large clusters. Performance monitoring ensures workloads are distributed efficiently.3. Birst's automated multi-dimensional database optimizes analytic workloads like dashboards, reports, and ad-hoc queries against large, diverse data sets. It automatically materializes aggregates for fast response times and integrates data from multiple sources into a unified semantic model (20)
Here are potential responses to the questions:1. Yes, Birst is designed to support repetitive self-service analytics. Its multi-tenant architecture allows different groups to independently manage and analyze their own data sources alongside centralized data, without impacting other groups. 2. Birst scales horizontally to meet increasing performance demands. Its distributed architecture leverages cloud infrastructure to automatically add nodes and optimize queries across large clusters. Performance monitoring ensures workloads are distributed efficiently.3. Birst's automated multi-dimensional database optimizes analytic workloads like dashboards, reports, and ad-hoc queries against large, diverse data sets. It automatically materializes aggregates for fast response times and integrates data from multiple sources into a unified semantic model
2. Welcome
Host:
Eric Kavanagh
eric.kavanagh@bloorgroup.com
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3. Mission
! Reveal the essential characteristics of enterprise software,
good and bad
! Provide a forum for detailed analysis of today s innovative
technologies
! Give vendors a chance to explain their product to savvy
analysts
! Allow audience members to pose serious questions... and get
answers!
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6. Analyst: Robin Bloor
Robin Bloor is
Chief Analyst at
The Bloor Group
robin.bloor@bloorgroup.com
Twitter Tag: #briefr The Briefing Room
7. Birst
! Birst offers a SaaS-based, multi-tenant BI platform; it can
also be deployed on-premise
! The Birst solution is capable of unifying siloed technologies,
automating data management and providing agile
enterprise-class analytics
! Birst’s approach enables self-service analytics by allowing
business users to manage and add new data sources, create
custom dashboards and collaborate across the organization
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8. Brad Peters
Brad Peters is the CEO and co-founder of
Birst. Brad has spent the last 10 years building
analytics products and solutions. Prior to
working at Birst, he helped found and later
led the Analytics product line at Siebel
Systems, which forms the basis of Oracle’s
current OBIEE product family. Brad started his
career as an investment banker for Morgan
Stanley in the New York M&A practice. Brad
regularly blogs for Forbes.com where he
writes about Cloud and business software
related issues.
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9. REIN
IN
DATA
CHAOS:
BRINGING
FLEXIBLE
GOVERNANCE
TO
ALL
DATA
SOURCES
Brad
Peters
CEO
and
Co-‐Founder
February
5,
2013
10. BI
AS
ORIGINALLY
CONCEIVED
• A
centralized
data
warehouse
• Data
is
“clean”
and
run
through
rigorous
checks
• IT
is
the
steward
of
master
data
10
11. X
BI
AS
ORIGINALLY
CONCEIVED
Except
It
Doesn’t
Work
As
AdverDsed
• Does
not
scale
organizaJonally
• Very
inflexible
• IT
cannot
possibly
take
responsibility
for
all
data
• For
users
where
100%
of
their
data
is
not
in
the
warehouse,
they
must
resort
to
extracts
• A
centralized
data
warehouse
• Data
is
“clean”
and
run
through
rigorous
checks
• IT
is
the
steward
of
master
data
11
12. BI
VERSION
2.0
-‐
HUB
AND
SPOKE
• Warehouse
is
a
“staging
area”
• Departments
build
their
own
data
sets
• IT
is
the
steward
of
master
data
12
13. X
BI
VERSION
2.0
-‐
HUB
AND
SPOKE
Except
It
Also
Doesn’t
Work
• Scales
slightly
beRer
• Hugely
labor
and
integraJon
intensive
• Requires
deep
technical
skill
at
mart
level
• Loss
of
central
data
integrity
• Latency
• Loss
of
control
and
governance
• Warehouse
tandards
for
uJlizing
central
data
• No
s is
a
“staging
area”
• Departments
build
their
own
data
sets
• No
“single
version
of
truth”
• IT
is
the
steward
of
master
data
13
14. WHAT
REALLY
HAPPENS
Business
Users
“Go
Rogue”
Extracts
Are
Taken
And
Combined
With
Local
Data
In
Excel
For
One-‐off
Analysis
• No single version of truth
• Infrequent analysis and stale data
14
15. WHAT
REALLY
HAPPENS
• Really
need
an
environment
that
can
host
mulJple
different
sets
of
data
–
some
high
quality,
some
not
• That
allows
IT
to
manage
their
data
• But
allows
other
organizaJons
to
self-‐serve
with
their
own
data
AND,
most
importantly,
combine
these
data
sets
Business
Uneed
a
mRogue”
analyJcs
infrastructure
with
• I.e.
You
sers
“Go
ulJ-‐tenant
Extracts
Are
Taken
And
Combined
With
Local
Data
that
allows
business
users
to
manage
their
own
data
nalysis
In
Excel
For
One-‐off
A
• No single version of truth
• Infrequent analysis and stale data
15
23. End-‐user
can
select
columns
from
either
place
seemlessly
Metadata
coming
from
parent
space
Metadata
coming
from
child
space
24. ABOUT
BIRST
Key
Birst
Facts
• #1
Cloud
BI
Provider
Market
&
Product
Leader
• Over
1,000
organizaJons
rely
on
Birst
across
all
verJcals
• Direct
customers
• ISV’s
for
embedded
analyJcs
• Typical
deployment
have
mulJple
data
sources
with
large
data
volumes
(>100’s
M
records)
Slide
24
25. FIND
OUT
MORE
Test
Drive
Birst
Express
• Register
at
birst.com/express
Join
a
Birst
live
demo
• Register
at
birst.com/livedemo
Contact
us
• Email:
info@birst.com
• Phone:
(866)
940-‐1496
Slide
25
28. Data Pools and Flows
DATA POOLS DATA FLOWS
! Transactional databases ! Data integration flows
! Data warehouse ! External streams
! Operational data store ! Emails, texts, etc.
! Hadoop ! Log files
! Data marts ! RFID, embedded sensors
! Desktop data ! People data (social media)
! Archiving
The Bloor Group
29. Data Flow Processes
HADOOP/DBMS (QUERIES) ETL
CLEANSING
GOVERNANCE
SECURITY
BI/ANALYTICS
The Bloor Group
31. The Challenge
And at the same time, the data has to move as fast
as possible…
The Bloor Group
32. The Challenge
And at the same time, the data has to move as fast
as possible…
THIS IS NOT SO EASY TO ACHIEVE
The Bloor Group
33. Questions
! In my presentation I highlight the issue of
“repetitive self-service.” Is this something that Birst
can cater for?
! Performance is in our view increasingly becoming a
factor in “data flow management.” How does Birst
scale to meet escalating performance demands?
! Can you describe the nature of the automated multi-
dimensional database – what workloads does it
optimize?
The Bloor Group
34. Questions
! How does Birst fit data governance in with the flow
of data?
! Which types of business/size of business do you see
as most suited to this capability?
! Which companies/products do you regard as
competitors (either direct or near)?
! Which companies/products do you partner with?
! Does Birst offer this as an appliance?
The Bloor Group
36. Upcoming Topics
This month: Analytics
March: Operational
Intelligence
April: Intelligence
May: Integration
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37. Thank You
for Your
Attention
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