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These slides summarize a survey of BI professionals on operational analytics.

These slides summarize a survey of BI professionals on operational analytics.

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  • While big data is getting a lot of hype, users still rely on the data warehouse for near real-time data
  • 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.
  • 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.”
  • 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.
  • 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

Transcript

  • 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.