© TechTarget

Operational Analytics Benchmark Report Results

December 2013

2© TechTarget

BI Framework 2020

Analytics Intelligence

Continuous IntelligenceCont

ent I

ntel

ligen

ce

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-Driven Alerts and Dashboards

Ad hoc SQL

Dashboard Alerts

Event detection and correlation

CEP, Streams

Analytic Sandboxes

Design Framework

Architecture

Reporting &

Analysis

Excel, Access, OLAP, Data mining, visual exploration

Keyw

ord

sear

ch, B

I too

ls,

Xque

ry, H

ive,

Java

, etc

.

Map

Redu

ce, X

ML

sche

ma,

Ke

y-va

lue

pairs

, gra

ph

nota

tion,

etc

.H

DFS,

NoS

QL

data

bses

Business Intelligence

3© TechTarget

Two Worlds of Operational Analytics

Batch-loaded Data

WarehouseMini-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© TechTarget

Definition

● 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.

© TechTarget 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%)

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 de-partment

21%

I'm in a business

department not related

to IT28%

© TechTarget 6

Operational analytics adoption

Key Takeaways

• One-third of respondents have either “fully” or “partially” deployed operational analytics

What is the status of operational analytics at your organization?

No plans

Under consideration

Under development

Partially deployed

Fully deployed

26%

25%

18%

22%

10%

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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, companies build operational analytics system but there is a shift to buy full-fledged systems. This 17% will rise.

Did you build or buy your operational analytical platform?

Built

Bought

Both

27%

17%

57%

© TechTarget 8

Scope of operational analytics deployments

Key Takeaways

• Most operational analytics implementations are guided by the corporate IT department and integrate data from applications

Which best describes the scope of your operational analytic deployment(s)?

Enterprise

Business unit

Departmental

Inter-enterprise

60%

21%

13%

5%

© TechTarget 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, Finance and Sales

What functional areas use operational analytics software?

Operations

Finance

Sales

Marketing

IT

Service

Supply chain

Risk

Product management

E-commerce

Logistics

Manufacturing

Other

65%

51%

50%

46%

42%

39%

24%

23%

21%

21%

21%

17%

6%

© TechTarget 10

Operational analytics primary users

Key Takeaways

• Operational analytic users are equally split between analysts and casual users

Who are the primary users of your operational analytics software?

Business analysts

Casual users

Application developers

Statisticians

45%

44%

6%

5%

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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.

What are your future plans for the deployment of operational analytical tools?

Expand deployment

Maintain, but not expand

Decrease deployment

Other

73%

23%

1%

4%

© TechTarget 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

Which types of data feed your operational analytic engine?

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

59%

45%

42%

39%

34%

34%

27%

27%

24%

24%

20%

19%

17%

15%

11%

© TechTarget 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.

How many sources of data does your operational analytic engine combine in your primary application?

0

1

2

3

4

5

6-10

0%

8%

9%

16%

10%

8%

50%

© TechTarget 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

What is the data throughput rate on average?

< 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

5%

13%

13%

27%

18%

14%

10%

© TechTarget 15

Operational analytics engine rule creation

Key Takeaways

• Not surprisingly, business analysts and business users create the rules for governing how the operational analytical engine manipulates data

Who creates the rules that govern how the operational analytical engine manipulates data?

Business analysts

Business users

Application developers

Data scientists

IT administrators

Statisticians

Other

53%

50%

37%

24%

24%

16%

5%

© TechTarget 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%)

What is the output of the operational analytics engine?

Real-time dashboard

Alerts via Web, email or pager

Database updates

Workflow

Triggers (scripts)

New queries

Recommendations/offers

Trouble ticket

Other

74%

53%

46%

37%

27%

24%

22%

20%

4%

© TechTarget 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.

What technologies do you use in conjunction with operational analytics?

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

76%

47%

43%

42%

39%

33%

32%

27%

17%

11%

10%

9%

© TechTarget 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

Which best describes the rules that govern your operational analytical applications?

Boolean

Statistical

Both above

20%

21%

68%

© TechTarget 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.

Which vendors supply you with operational analytics software?

Oracle

IBM

SAS

Open Source (Flume, Storm, Kafka)

Informatica

SQLStreams

Sybase (SAP)

Splunk

HP

Tibco

Streamworks

InfoChimps

ZoomData

Splice

Vitria

Tervelo

Other

35%

27%

22%

21%

17%

11%

11%

11%

7%

7%

6%

2%

2%

1%

1%

0%

42%

© TechTarget 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

To what degree does your operational analytical engine deliver the following benefits?

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

50%

44%

43%

41%

40%

35%

34%

31%

30%

25%

© TechTarget 21

Operational analytics engine challenges

Key Takeaways

• Respondents cited “sourcing data” and “defining rules for analysis and actions” as the top challenges.

• Surprisingly, “scalability” and “performance” were the least cited challenges

What challenges have you faced implementing operational analytics?

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

42%

42%

25%

26%

36%

34%

32%

© TechTarget 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.

What prevents you from deploying operational analytics software?

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

34%

28%

17%

14%

13%

12%

10%

23© TechTarget

Summary

● 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.