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Business Analytics Paradigm Change

Business Analytics Paradigm Change

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Business Analytics Paradigm Change

  1. 1. Business Analytics Paradigm Change Dmitry Anoshin
  2. 2. Target Market Trends • “Feeding transactional data into a traditional data warehouse no longer represents the extent of capabilities necessary for BI.” • “The simple idea of building a traditional data warehouse to support a BI platform is no longer sufficient.” • “….require new information management capabilities to integrate information from disparate, external and unstructured information sources.”
  3. 3. Traditional Analytics Types: • Business Intelligence • Data mining • OLAP • Plain Analytics Uses: • Get better sense of their operations • Cut costs • Improve decision making • Identify inefficient processes, which can lead to identify new business opportunities and reengineering their processes Challenges: • Raw information lives are usually decoupled or spread across distributed systems • Difficult to consolidate • Involves an effort going through the typical SDLC, which takes lots of time
  4. 4. Typical Process for Structured Data Application Application Data base ETL Data Application Connector Warehouse Analytics Tool Early Structure Binding • Decide what questions to ask • Design the data schema • Normalize the data • Write database insertion code • Create the queries • Feed the results into an analytics tool
  5. 5. Business Analytics –Before Splunk IT/Business Challenges • Most organizations only rely on structured data for business analytics – not sufficient today! • New data sources such as machine increasingly critical sources of insight – not leveraged by organizations • Inability to scale / handle data volume of new sources as data continues to grow Inability to deliver real-time insights to the business. • Most today rely on ETL causing latency in analytics Existing solutions unable to do data mash-up across structured and machine data Business Consequence • Inability to gain real-time business insights from new data sources • Business users across functions (sales ops, product managers, marketing, and customer support users cannot leverage new data sources for analytics • Competitive disadvantage as other companies increasingly leverage machine data for business insights • Unable to get insights from new data sources with their traditional structured analytics tools
  6. 6. Business Analytics – After Splunk IT/Business Vision • Deliver real-time business insight from machine data • Enrich machine data with structured data to provide business context • Complement existing BI technologies for insight into a new class of data • Leverage search, interactive dashboards in Splunk or other 3rd party visualization tools • Rapid time to value in gaining business insights from machine data Business Benefits • Application Analytics – to understand how customers are interacting with various online applications. • Content & Search Analytics – to understand how customers are accessing and searching for content served up over CDNs • Real-time Sales Analytics – to gain real-time visibility into products and services that customers are purchasing. • Service Cost Analytics – to gain insight (for example) into call detail records and cost associated with completing each call. • Online Monetization Analytics – an example of this is online gaming companies where they are introducing virtual goods and charging for them. • Marketing Analytics – 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 Analytics Digital Intelligence Security and Compliance IT Operations App Manageme nt Industrial Data Developer Platform (REST API, SDKs) >SPLUNK Small Data. Big Data. Huge Data.
  8. 8. Splunk Turns Machine Data into Operational Intelligence Customer Facing Data Outside the Datacenter Applications Web logs Log4J, JMS, JMX .NET events Code and scripts Networking Configurations syslog SNMP netflow Databases Configurations Audit/query logs Tables Schemas Virtualization & Cloud Hypervisor Guest OS, Apps Cloud Linux/Unix Configuration s 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 transaction data Manufacturing, logistics… CDRs & IPDRs Power consumption RFID data GPS data
  9. 9. Early vs. Late Binding Schema Early Structure Binding - Traditional 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 time • Homogeneous– must fit into tables or be converted to fit into tables • Queries – understood at design time 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 time • Constantly changing • Queries/searches can be ad-hoc • No conversion required, no constraints
  11. 11. Analytics Early Structure Binding Late Binding Schema Decide the question(s) you want to ask Design the Schema Normalize the data and write DB insertion code Create SQL & Feed into Analytics Tool Write data (or events) to log files Collect the log files Create searches, graphs, and reports using Splunk (Days, Weeks or Months & Destructive) (Minutes & Non- Destructive)
  12. 12. Example: Business Visibility From Machine Data Machine Data (from customer interaction) Product Information Geo location Data Customer interacts with service online or from any device User browser information Action Product User session 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 information from database Location 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 interaction paths by devices? – How can we improve customer experience?
  13. 13. Getting Structured Data In Splunk Log files CSV lookup Splunk Connector • Access data at scale • In real-time • Easy set-up & maintenance Structured databases Applications Web Servers Other systems
  14. 14. DB Connect: Business Context to Machine Data Structured Data >Machine Data >Business Analytics Rate plans, customer profile, geo location Customer profile, Service subscription Product descriptions, Customer profile Device activation, Radius, application logs Application, server and network logs Application logs, authentication logs Sales Analytics Customer Analytics Product Analytics
  15. 15. Getting Business Insights from Splunk User Interface: Splunk User Interface: Third Party Dashboards Searches Pivot Schedule SDK/APIs ODBC
  16. 16. Positioning Splunk for Business Analytics >New class of data for business analytics >Enrich machine data with structured data >Real-time business insights >Complement traditional BI Tools
  17. 17. Splunk Complements Existing BI Tools Features Splunk Leading BI Tools Focus Platform for real-time operational intelligence Data visualization and business intelligence software Value Collect, index, search, monitor, report on, analyze massive streams of machine data Analyze, visualize and share structured data Users IT, Operations, 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 Analytics from machine data, Internet of Things Marketing, HR, Sales Reporting, Supply Chain Analysis
  18. 18. Scales to TBs/day and Thousands of Users Automatic load balancing linearly scales indexing Distributed search and MapReduce linearly scales search and reporting
  19. 19. Summary > Real Time Architecture > Universal Machine Data Platform > Schema on the Fly > Agile Reporting and Analytics > Scales from Desktop to Enterprise > Fast Time to Value > Passionate and Vibrant Community

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