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Splunk Business Analytics

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Splunk Business Analytics Application

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Splunk Business Analytics

  1. 1. Business Analy,cs Paradigm Change Dmitry Anoshin
  2. 2. Target Market Trends • “Feeding transac,onal data into a tradi,onal data warehouse no longer represents the extent of capabili,es necessary for BI.” • “The simple idea of building a tradi,onal data warehouse to support a BI plaEorm is no longer sufficient.” • “….require new informa,on management capabili,es to integrate informa,on from disparate, external and unstructured informa,on sources.”
  3. 3. Tradi,onal Analy,cs Types: • Business Intelligence • Data mining • OLAP • Plain Analy,cs Uses: • Get beNer sense of their opera,ons • Cut costs • Improve decision making • Iden,fy inefficient processes, which can lead to iden,fy new business opportuni,es and reengineering their processes Challenges: • Raw informa,on lives are usually decoupled or spread across distributed systems • Difficult to consolidate • Involves an effort going through the typical SDLC, which takes lots of ,me
  4. 4. Typical Process for Structured Data Applica,on Applica,on Applica,on Connector Data base ETL Data Warehouse Analy,cs Tool Direct Insert Early Structure Binding • Decide what ques,ons to ask • Design the data schema • Normalize the data • Write database inser,on code • Create the queries • Feed the results into an analy,cs tool
  5. 5. Business Analy,cs –Before Splunk IT/Business Challenges • Most organiza,ons only rely on structured data for business analy,cs – not sufficient today! • New data sources such as machine increasingly cri,cal sources of insight – not leveraged by organiza,ons • Inability to scale / handle data volume of new sources as data con,nues to grow Inability to deliver real-­‐,me insights to the business. • Most today rely on ETL causing latency in analy,cs Exis,ng solu,ons unable to do data mash-­‐up across structured and machine data Business Consequence • Inability to gain real-­‐,me business insights from new data sources • Business users across func,ons (sales ops, product managers, marke,ng, and customer support users cannot leverage new data sources for analy,cs • Compe,,ve disadvantage as other companies increasingly leverage machine data for business insights • Unable to get insights from new data sources with their tradi,onal structured analy,cs tools
  6. 6. Business Analy,cs – A]er Splunk IT/Business Vision • Deliver real-­‐,me business insight from machine data • Enrich machine data with structured data to provide business context • Complement exis,ng BI technologies for insight into a new class of data • Leverage search, interac,ve dashboards in Splunk or other 3rd party visualiza,on tools • Rapid ,me to value in gaining business insights from machine data Business Benefits • Applica,on Analy,cs – to understand how customers are interac,ng with various online applica,ons. • Content & Search Analy,cs – to understand how customers are accessing and searching for content served up over CDNs • Real-­‐,me Sales Analy,cs – to gain real-­‐,me visibility into products and services that customers are purchasing. • Service Cost Analy,cs – to gain insight (for example) into call detail records and cost associated with comple,ng each call. • Online Mone,za,on Analy,cs – an example of this is online gaming companies where they are introducing virtual goods and charging for them. • Marke,ng Analy,cs – understanding customer click-­‐ through for ads helps improve placement, pricing and click through rates.
  7. 7. Splunk Delivers Value Across IT and the Business Business Analy,cs Digital Intelligence Security and Compliance IT Opera,ons App Manageme nt Industrial Data Developer PlaEorm (REST API, SDKs) >SPLUNK Small Data. Big Data. Huge Data.
  8. 8. Splunk Turns Machine Data into Opera,onal Intelligence Customer Facing Data Outside the Datacenter ApplicaDons " Web logs " Log4J, JMS, JMX " .NET events " Code and scripts Networking " Configura,ons " syslog " SNMP " neElow Databases " Configura,ons " Audit/query logs " Tables " Schemas VirtualizaDon & Cloud " Hypervisor " Guest OS, Apps " Cloud Linux/Unix " Configura,ons " syslog " File system " ps, iostat, top Windows " Registry " Event logs " File system " sysinternals Logfiles Configs Messages Traps Alerts Metrics Scripts Changes Tickets " Click-­‐stream data " Shopping cart data " Online transac,on data " Manufacturing, logis,cs… " CDRs & IPDRs " Power consump,on " RFID data " GPS data
  9. 9. Early vs. Late Binding Schema Early Structure Binding -­‐ Tradi,onal SELECT customers.* FROM customers WHERE customers.customer_id NOT IN(SELECT customer_id FROM Orders WHERE year(orders.order_date) = 2004) Structure Data • Schema – created at design ,me • Homogeneous– must fit into tables or be converted to fit into tables • Queries – understood at design ,me for maximum performance • Must exactly match constraints
  10. 10. Early vs. Late Binding Schema Late Structure Binding -­‐ Splunk Structure Data • Schema-­‐less • Heterogeneous– can come from any textual source • Created at search ,me • Constantly changing • Queries/searches can be ad-­‐hoc • No conversion required, no constraints
  11. 11. Analy,cs Early Structure Binding Late Binding Schema Decide the ques,on(s) you want to ask Design the Schema Normalize the data and write DB inser,on code Create SQL & Feed into Analy,cs Tool Write data (or events) to log files Collect the log files Create searches, graphs, and reports using Splunk (Days, Weeks or Months & Destruc,ve) (Minutes & Non-­‐ Destruc,ve)
  12. 12. Example: Business Visibility From Machine Data Machine Data (from customer interacDon) Product InformaDon Geo locaDon Data Customer interacts with service online or from any device Ac,on Product User session User browser informa,on 66.57.19.112 ..[05/Dec/2011 07:05:22:152]”GET /card.do? action=addtocart&itemid=EST-17& product_id=K9- BD-01&JSESSIONID.SD7SLSFF8ADFF8HTTP 1.1” 200 3923 AppleWebKit/535.2 (KHTML.like Gecko) Chrome/15.0.874.121 Safari535.2 Product_id=K9-BD-01 Product Name=2 TB Portable Drive Manufacturer=iomega Real-­‐Time Business Insights from Machine Data Geo location data Correlated with product informa,on from database Loca,on data based on where the customer purchased / interacted with service – What products are popular in what region? – Which product are customers leaving in cart? – What are interac,on paths by devices? – How can we improve customer experience?
  13. 13. Gepng Structured Data In Splunk CSV lookup Splunk Connector • Access data at scale • In real-­‐,me • Easy set-­‐up & maintenance Log files Structured databases Applica,ons Web Servers Other systems
  14. 14. DB Connect: Business Context to Machine Data Structured Data >Machine Data >Business AnalyDcs Rate plans, customer profile, geo loca,on Customer profile, Service subscrip,on Product descrip,ons, Customer profile Device ac,va,on, Radius, applica,on logs Applica,on, server and network logs Applica,on logs, authen,ca,on logs Sales Analy,cs Customer Analy,cs Product Analy,cs
  15. 15. Gepng Business Insights from Splunk User Interface: Splunk User Interface: Third Party Dashboards Searches Pivot Schedule SDK/APIs ODBC
  16. 16. Posi,oning Splunk for Business Analy,cs >New class of data for business analy,cs >Enrich machine data with structured data >Real-­‐,me business insights >Complement tradi,onal BI Tools
  17. 17. Splunk Complements Exis,ng BI Tools Features Splunk Leading BI Tools Focus PlaEorm for real-­‐,me opera,onal intelligence Data visualiza,on and business intelligence so]ware Value Collect, index, search, monitor, report on, analyze massive streams of machine data Analyze, visualize and share structured data Users IT, Opera,ons, Security, Developers, Analysts, Business Users (as consumers) Business Users and Analysts (already using data discovery tool) Use Cases IT Ops, App Management, Security, Digital Intelligence, Business Analy,cs from machine data, Internet of Things Marke,ng, HR, Sales Repor,ng, Supply Chain Analysis
  18. 18. Scales to TBs/day and Thousands of Users " Automa,c load balancing linearly scales indexing " Distributed search and MapReduce linearly scales search and repor,ng
  19. 19. Summary > Real Time Architecture > Universal Machine Data PlaWorm > Schema on the Fly > Agile ReporDng and AnalyDcs > Scales from Desktop to Enterprise > Fast Time to Value > Passionate and Vibrant Community

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