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© TechTarget
Operational Analytics
Benchmark Report Results
December 2013
BI Framework 2020
2© TechTarget
Analytics Intelligence
ContinuousIntelligence
ContentIntelligence
Data Warehousing
Ad hoc query, Spreadsheets,
OLAP, Visual Analysis, Analytic
Workbenches, Hadoop
Analytic Sandboxes
Event-driven
Reports and Dashboards
MAD Dashboards
Data Ware-
housing
End-User Tools
Event-DrivenAlertsand
Dashboards
Ad hoc SQL
DashboardAlerts
Eventdetection
andcorrelation
CEP,Streams
Analytic
Sandboxes
Design Framework
Architecture
Reporting
&
Analysis
Excel, Access, OLAP, Data
mining, visual exploration
Keywordsearch,BItools,
Xquery,Hive,Java,etc.
MapReduce,XMLschema,
Key-valuepairs,graph
notation,etc.
HDFS,NoSQL
databses
Business Intelligence
Two Worlds of Operational Analytics
3© TechTarget
Batch-loaded
Data
Warehouse
Mini-batch fed
Data
Warehouse
Trickle-fed DW
with CDC
Complex Event
Processing
Stream-based
Processing
Update Cycle
DW Architecture
Non-DW
Architecture
Days Hours Minutes Seconds Milliseconds Microseconds
Definition
4© TechTarget
● Operational analytics analyzes data on the fly. Real time
data "streams" from multiple systems into an analytical
engine without landing to disk or a data warehouse. The
analytical engine monitors operational processes in real
time, displaying activity and trends on an interactive
dashboard. When data exceeds predefined thresholds or
matches a rule, the engine can take automated actions,
such as alerting users, executing a lookup, triggering a
worfklow, executing a script, delivering a page, or
updating a database.
Respondent departments
Key Takeaways
• It’s interesting to
note that 50% of
buyers operate
outside the IT
department
• We continue to see
growth in the
number of users
who have an IT title
but who are more
closely aligned with
the business (21%)
© TechTarget 5
With which part of the organization do you more
closely align?
I'm in the IT
department
51%I have an IT
role outside
of the IT
department
21%
I'm in a
business
department
not related
to IT
28%
Operational analytics adoption
Key Takeaways
• One-third of
respondents have
either “fully” or
“partially” deployed
operational
analytics
© TechTarget 6
What is the status of operational analytics at your
organization?
26%
25%
18%
22%
10%
No plans
Under consideration
Under development
Partially deployed
Fully deployed
Build or buy operational analytics
Key Takeaways
• Of those who have
“fully” or “partially”
deployed operational
analytics, 57% have
both built and bought
their system
• Traditionally, compani
es build operational
analytics system but
there is a shift to buy
full-fledged systems.
This 17% will rise.
© TechTarget 7
Did you build or buy your operational analytical
platform?
27%
17%
57%
Built
Bought
Both
Scope of operational analytics deployments
Key Takeaways
• Most operational
analytics
implementations
are guided by the
corporate IT
department and
integrate data from
applications
© TechTarget 8
Which best describes the scope of your operational
analytic deployment(s)?
60%
21%
13%
5%
Enterprise
Business unit
Departmental
Inter-enterprise
Operational analytics functional areas
Key Takeaways
• The top areas that
use operational
analytics are those
that generate a lot
of data on a daily
basis and benefit
from monitoring its
predefined
rules/functions
• These include
Operations, Financ
e and Sales
© TechTarget 9
What functional areas use operational analytics
software?
65%
51%
50%
46%
42%
39%
24%
23%
21%
21%
21%
17%
6%
Operations
Finance
Sales
Marketing
IT
Service
Supply chain
Risk
Product management
E-commerce
Logistics
Manufacturing
Other
Operational analytics primary users
Key Takeaways
• Operational
analytic users are
equally split
between analysts
and casual users
© TechTarget 10
Who are the primary users of your operational
analytics software?
45%
44%
6%
5%
Business analysts
Casual users
Application developers
Statisticians
Operational analytics future plans
Key Takeaways
• 73% expect their
company to expand
deployment of
operational
analytics
• This is a strong
endorsement that
gaining insight
using the freshest
data possible
delivers strong
business benefit.
© TechTarget 11
What are your future plans for the deployment of
operational analytical tools?
73%
23%
1%
4%
Expand deployment
Maintain, but not expand
Decrease deployment
Other
Operational analytics engine data feed
Key Takeaways
• The biggest data
source is the data
warehouse
• These results
suggest most
companies are
delivering near
real-time
data, instead of
real-time data using
a CEP or ESP
system
© TechTarget 12
Which types of data feed your operational analytic
engine?
59%
45%
42%
39%
34%
34%
27%
27%
24%
24%
20%
19%
17%
15%
11%
Data warehouse data
Service or call center data
Point-of-sale or sales data
Local files (e.g., Excel, CSV)
Network data
Call detail records
Server logs
Email data
Trading or financial data
Clickstream data (e.g., Web logs)
Social media data (e.g., Twitter, Facebook)
Sensor data
Claims or warranty data
Hadoop/NoSQL data
Other
Operational analytics engine data sources
Key Takeaways
• Since the
respondent pool
runs operational
analytics using a
near real-time data
warehouse, it’s not
surprising that a
majority of
respondents cite
more than six data
sources.
© TechTarget 13
How many sources of data does your operational
analytic engine combine in your primary
application?
0%
8%
9%
16%
10%
8%
50%
0
1
2
3
4
5
6-10
Operational analytics engine data throughput
rate
Key Takeaways
• More than two-
thirds support
throughput rates of
more than 100
records per
second, with 10%
recording more
than 100,000
records per second
© TechTarget 14
What is the data throughput rate on average?
5%
13%
13%
27%
18%
14%
10%
< 1 record per second
< 10 records per second
< 100 records per second
< 1,000 records per second
< 10,000 records per second
< 100,000 records per second
>100,000+ records per second
Operational analytics engine rule creation
Key Takeaways
• Not
surprisingly, busine
ss analysts and
business users
create the rules for
governing how the
operational
analytical engine
manipulates data
© TechTarget 15
Who creates the rules that govern how the
operational analytical engine manipulates data?
53%
50%
37%
24%
24%
16%
5%
Business analysts
Business users
Application developers
Data scientists
IT administrators
Statisticians
Other
Operational analytics engine data output
Key Takeaways
• Given the data
warehousing
platform, it’s not
surprising that the
most common
output of an
operational analytic
system is a real-
time dashboard
(74%)
© TechTarget 16
What is the output of the operational analytics
engine?
74%
53%
46%
37%
27%
24%
22%
20%
4%
Real-time dashboard
Alerts via Web, email or pager
Database updates
Workflow
Triggers (scripts)
New queries
Recommendations/offers
Trouble ticket
Other
Operational analytics supporting technologies
Key Takeaways
• More than three-
quarters (76%)
cited business
intelligence tools as
the most commonly
used technology in
an operational
analytic system.
© TechTarget 17
What technologies do you use in conjunction with
operational analytics?
76%
47%
43%
42%
39%
33%
32%
27%
17%
11%
10%
9%
Business intelligence tools
Analytical databases
Data mining tools
Specialized "operational analytics" tools
In-database analytics
Rules engines
OLAP tools
Open source tools
Complex event processing engines
Streaming engines
Hadoop/HBase
Other
Operational analytical applications governing
rules
Key Takeaways
• Respondents apply
a mix of Boolean
and statistical rules
in their operational
analytics systems
to automate alerts
and actions
© TechTarget 18
Which best describes the rules that govern your
operational analytical applications?
20%
21%
68%
Boolean
Statistical
Both above
Operational analytics software vendors
Key Takeaways
• More than one-third
use
Oracle, followed by
IBM, SAS, open
source software
and Informatica
• These results show
there is a clear
need for Vitria to
enhance their
market presence
among these
buyers.
© TechTarget 19
Which vendors supply you with operational analytics
software?
35%
27%
22%
21%
17%
11%
11%
11%
7%
7%
6%
2%
2%
1%
1%
0%
42%
Oracle
IBM
SAS
Open Source (Flume, Storm, Kafka)
Informatica
SQLStreams
Sybase (SAP)
Splunk
HP
Tibco
Streamworks
InfoChimps
ZoomData
Splice
Vitria
Tervelo
Other
Operational analytics engine benefits
Key Takeaways
• Respondents cited a
litany of
benefits, including
improving
operational efficiency
and working more
proactively
• Given the high
scores across the
board, it’s clear that
operational analytics
creates a positive
ripple effect
throughout an
organization
© TechTarget 20
To what degree does your operational analytical
engine deliver the following benefits?
50%
44%
43%
41%
40%
35%
34%
31%
30%
25%
Improve operational efficiency
Work more proactively
Detect problems quickly
Increase competitiveness
Improve data quality
Increase business transparency
Improve customer experience
Reduce costs
Automate actions
Increase revenues
Operational analytics engine challenges
Key Takeaways
• Respondents cited
“sourcing data” and
“defining rules for
analysis and
actions” as the top
challenges.
• Surprisingly, “scala
bility” and
“performance” were
the least cited
challenges
© TechTarget 21
What challenges have you faced implementing
operational analytics?
42%
42%
25%
26%
36%
34%
32%
Sourcing - Capturing data from multiple, complex
systems
Complexity - Defining rules for analysis and actions
Scalability - Ingesting high volumes of data
Performance - Maintaining performance as query
and data complexity increase
Funding - Getting executives to fund the installation
or expansion of the software
Integration - Integrating tools with other information
environments
Data Quality - Identifying and fixing data quality
errors
Operational analytics software obstacles
Key Takeaways
• Respondents who
have not
implemented
operational analytics
cite that they “Don’t
know enough about
[it]”
• Since operational
analytics is a newer
discipline, it’s not
surprising that a large
percentage of
respondents haven’t
heard about it yet.
© TechTarget 22
What prevents you from deploying operational
analytics software?
34%
28%
17%
15%
13%
12%
10%
Don't know enough about them
Our budget is tapped out
No need
We built our own
Other
Performance and scalability issues
Not enough value for the price
Summary
23© TechTarget
● Operational analytics is an early adopter market.
● Lots of headroom among the BI audience
● BI audience using traditional BI technologies to satisfy
operational analytical applications and near-real-time
information delivery.

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Operational Analytics

  • 1. © TechTarget Operational Analytics Benchmark Report Results December 2013
  • 2. BI Framework 2020 2© TechTarget Analytics Intelligence ContinuousIntelligence ContentIntelligence Data Warehousing Ad hoc query, Spreadsheets, OLAP, Visual Analysis, Analytic Workbenches, Hadoop Analytic Sandboxes Event-driven Reports and Dashboards MAD Dashboards Data Ware- housing End-User Tools Event-DrivenAlertsand Dashboards Ad hoc SQL DashboardAlerts Eventdetection andcorrelation CEP,Streams Analytic Sandboxes Design Framework Architecture Reporting & Analysis Excel, Access, OLAP, Data mining, visual exploration Keywordsearch,BItools, Xquery,Hive,Java,etc. MapReduce,XMLschema, Key-valuepairs,graph notation,etc. HDFS,NoSQL databses Business Intelligence
  • 3. Two Worlds of Operational Analytics 3© TechTarget Batch-loaded Data Warehouse Mini-batch fed Data Warehouse Trickle-fed DW with CDC Complex Event Processing Stream-based Processing Update Cycle DW Architecture Non-DW Architecture Days Hours Minutes Seconds Milliseconds Microseconds
  • 4. Definition 4© TechTarget ● Operational analytics analyzes data on the fly. Real time data "streams" from multiple systems into an analytical engine without landing to disk or a data warehouse. The analytical engine monitors operational processes in real time, displaying activity and trends on an interactive dashboard. When data exceeds predefined thresholds or matches a rule, the engine can take automated actions, such as alerting users, executing a lookup, triggering a worfklow, executing a script, delivering a page, or updating a database.
  • 5. Respondent departments Key Takeaways • It’s interesting to note that 50% of buyers operate outside the IT department • We continue to see growth in the number of users who have an IT title but who are more closely aligned with the business (21%) © TechTarget 5 With which part of the organization do you more closely align? I'm in the IT department 51%I have an IT role outside of the IT department 21% I'm in a business department not related to IT 28%
  • 6. Operational analytics adoption Key Takeaways • One-third of respondents have either “fully” or “partially” deployed operational analytics © TechTarget 6 What is the status of operational analytics at your organization? 26% 25% 18% 22% 10% No plans Under consideration Under development Partially deployed Fully deployed
  • 7. Build or buy operational analytics Key Takeaways • Of those who have “fully” or “partially” deployed operational analytics, 57% have both built and bought their system • Traditionally, compani es build operational analytics system but there is a shift to buy full-fledged systems. This 17% will rise. © TechTarget 7 Did you build or buy your operational analytical platform? 27% 17% 57% Built Bought Both
  • 8. Scope of operational analytics deployments Key Takeaways • Most operational analytics implementations are guided by the corporate IT department and integrate data from applications © TechTarget 8 Which best describes the scope of your operational analytic deployment(s)? 60% 21% 13% 5% Enterprise Business unit Departmental Inter-enterprise
  • 9. Operational analytics functional areas Key Takeaways • The top areas that use operational analytics are those that generate a lot of data on a daily basis and benefit from monitoring its predefined rules/functions • These include Operations, Financ e and Sales © TechTarget 9 What functional areas use operational analytics software? 65% 51% 50% 46% 42% 39% 24% 23% 21% 21% 21% 17% 6% Operations Finance Sales Marketing IT Service Supply chain Risk Product management E-commerce Logistics Manufacturing Other
  • 10. Operational analytics primary users Key Takeaways • Operational analytic users are equally split between analysts and casual users © TechTarget 10 Who are the primary users of your operational analytics software? 45% 44% 6% 5% Business analysts Casual users Application developers Statisticians
  • 11. Operational analytics future plans Key Takeaways • 73% expect their company to expand deployment of operational analytics • This is a strong endorsement that gaining insight using the freshest data possible delivers strong business benefit. © TechTarget 11 What are your future plans for the deployment of operational analytical tools? 73% 23% 1% 4% Expand deployment Maintain, but not expand Decrease deployment Other
  • 12. Operational analytics engine data feed Key Takeaways • The biggest data source is the data warehouse • These results suggest most companies are delivering near real-time data, instead of real-time data using a CEP or ESP system © TechTarget 12 Which types of data feed your operational analytic engine? 59% 45% 42% 39% 34% 34% 27% 27% 24% 24% 20% 19% 17% 15% 11% Data warehouse data Service or call center data Point-of-sale or sales data Local files (e.g., Excel, CSV) Network data Call detail records Server logs Email data Trading or financial data Clickstream data (e.g., Web logs) Social media data (e.g., Twitter, Facebook) Sensor data Claims or warranty data Hadoop/NoSQL data Other
  • 13. Operational analytics engine data sources Key Takeaways • Since the respondent pool runs operational analytics using a near real-time data warehouse, it’s not surprising that a majority of respondents cite more than six data sources. © TechTarget 13 How many sources of data does your operational analytic engine combine in your primary application? 0% 8% 9% 16% 10% 8% 50% 0 1 2 3 4 5 6-10
  • 14. Operational analytics engine data throughput rate Key Takeaways • More than two- thirds support throughput rates of more than 100 records per second, with 10% recording more than 100,000 records per second © TechTarget 14 What is the data throughput rate on average? 5% 13% 13% 27% 18% 14% 10% < 1 record per second < 10 records per second < 100 records per second < 1,000 records per second < 10,000 records per second < 100,000 records per second >100,000+ records per second
  • 15. Operational analytics engine rule creation Key Takeaways • Not surprisingly, busine ss analysts and business users create the rules for governing how the operational analytical engine manipulates data © TechTarget 15 Who creates the rules that govern how the operational analytical engine manipulates data? 53% 50% 37% 24% 24% 16% 5% Business analysts Business users Application developers Data scientists IT administrators Statisticians Other
  • 16. Operational analytics engine data output Key Takeaways • Given the data warehousing platform, it’s not surprising that the most common output of an operational analytic system is a real- time dashboard (74%) © TechTarget 16 What is the output of the operational analytics engine? 74% 53% 46% 37% 27% 24% 22% 20% 4% Real-time dashboard Alerts via Web, email or pager Database updates Workflow Triggers (scripts) New queries Recommendations/offers Trouble ticket Other
  • 17. Operational analytics supporting technologies Key Takeaways • More than three- quarters (76%) cited business intelligence tools as the most commonly used technology in an operational analytic system. © TechTarget 17 What technologies do you use in conjunction with operational analytics? 76% 47% 43% 42% 39% 33% 32% 27% 17% 11% 10% 9% Business intelligence tools Analytical databases Data mining tools Specialized "operational analytics" tools In-database analytics Rules engines OLAP tools Open source tools Complex event processing engines Streaming engines Hadoop/HBase Other
  • 18. Operational analytical applications governing rules Key Takeaways • Respondents apply a mix of Boolean and statistical rules in their operational analytics systems to automate alerts and actions © TechTarget 18 Which best describes the rules that govern your operational analytical applications? 20% 21% 68% Boolean Statistical Both above
  • 19. Operational analytics software vendors Key Takeaways • More than one-third use Oracle, followed by IBM, SAS, open source software and Informatica • These results show there is a clear need for Vitria to enhance their market presence among these buyers. © TechTarget 19 Which vendors supply you with operational analytics software? 35% 27% 22% 21% 17% 11% 11% 11% 7% 7% 6% 2% 2% 1% 1% 0% 42% Oracle IBM SAS Open Source (Flume, Storm, Kafka) Informatica SQLStreams Sybase (SAP) Splunk HP Tibco Streamworks InfoChimps ZoomData Splice Vitria Tervelo Other
  • 20. Operational analytics engine benefits Key Takeaways • Respondents cited a litany of benefits, including improving operational efficiency and working more proactively • Given the high scores across the board, it’s clear that operational analytics creates a positive ripple effect throughout an organization © TechTarget 20 To what degree does your operational analytical engine deliver the following benefits? 50% 44% 43% 41% 40% 35% 34% 31% 30% 25% Improve operational efficiency Work more proactively Detect problems quickly Increase competitiveness Improve data quality Increase business transparency Improve customer experience Reduce costs Automate actions Increase revenues
  • 21. Operational analytics engine challenges Key Takeaways • Respondents cited “sourcing data” and “defining rules for analysis and actions” as the top challenges. • Surprisingly, “scala bility” and “performance” were the least cited challenges © TechTarget 21 What challenges have you faced implementing operational analytics? 42% 42% 25% 26% 36% 34% 32% Sourcing - Capturing data from multiple, complex systems Complexity - Defining rules for analysis and actions Scalability - Ingesting high volumes of data Performance - Maintaining performance as query and data complexity increase Funding - Getting executives to fund the installation or expansion of the software Integration - Integrating tools with other information environments Data Quality - Identifying and fixing data quality errors
  • 22. Operational analytics software obstacles Key Takeaways • Respondents who have not implemented operational analytics cite that they “Don’t know enough about [it]” • Since operational analytics is a newer discipline, it’s not surprising that a large percentage of respondents haven’t heard about it yet. © TechTarget 22 What prevents you from deploying operational analytics software? 34% 28% 17% 15% 13% 12% 10% Don't know enough about them Our budget is tapped out No need We built our own Other Performance and scalability issues Not enough value for the price
  • 23. Summary 23© TechTarget ● Operational analytics is an early adopter market. ● Lots of headroom among the BI audience ● BI audience using traditional BI technologies to satisfy operational analytical applications and near-real-time information delivery.

Hinweis der Redaktion

  1. While big data is getting a lot of hype, users still rely on the data warehouse for near real-time data
  2. What’s not clear is whether most of the respondents are citing ETL batch-load rates or trickle-feed rates. Given the preponderance of data warehousing implementers, it’s probably the former since trickle feeding, even in a data warehousing environment, is still fairly rare.
  3. From Report: “The fact that the top two choices here define a modern BI architecture in which a BI tool accesses an analytical database makes me wonder how many respondents confuse operational analytics with faster queries. Operational analytics is not about faster queries – it’s about applying queries to fast-moving data.”
  4. These results show there is a clear need for Vitria to enhance their market presence among these buyers. Since this market is still in its infancy, Vitria should focus their messaging around enhancing audience awareness in this space.
  5. For 2nd bullet:Interesting to note because you might expect a real-time system processing thousands of records to pose scalability and performance issues for most users