Logitech Accelerates Cloud Analytics Using Data Virtualization by Avinash Deshpande
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Logitech is leveraging cloud analytics and big data to enhance its business operations and product offerings, focusing on creating a decentralized self-service analytics environment for improved business reporting and decision-making. By utilizing advanced analytics such as predictive and prescriptive analytics, the company aims to optimize consumer insights and operational performance. The implementation of data virtualization technologies is expected to enhance data governance and integration capabilities, supporting scalability and efficiency in Logitech's data management processes.
Logitech Accelerates Cloud Analytics Using Data Virtualization by Avinash Deshpande
- 1. LOGITECH ACCELERATES CLOUD ANALYTICS USING BIG DATA FABRIC Avinash Deshpande Chief Software Architect, Big data and Advanced Analytics adeshpande2@logitech.com
- 2. Logitech designs products that have an everyday place in people's lives, connecting them to the digital experiences they care about. Over 30 years ago, Logitech started connecting people through computers; now it’s designing products that bring people together through music, gaming, video and computing. In 1981, Logitech was founded in the village of Apples, Switzerland. The start-up was based in a farm building – the Swiss equivalent of a Silicon Valley garage. At the heart of Logitech’s success lies its ability to design product experiences that tap into genuine consumer needs. Under a number of different brands, the company offers PC peripherals; cases and keyboards for tablets; equipment for gamers; mobile speakers and earphones for music and sports enthusiasts; devices to make video collaboration simple in the workplace; and entertainment and control products for the home. THE LOGITECH STORY
- 3. LOGITECH – PRODUCT PORTFOLIO • Mice + Keyboards • Mobile • Smart Home • Gaming • Speakers • Video
- 4. LOGITECH CONFIDENTIAL: NOT FOR DISTRIBUTION ANALYTICS AT SCALE SUPPORTING OUR GROWING BUSINESS
- 5. LOGITECH DATA USE CASES Structured Semi-Structured Unstructured Batch Data Velocity Real-Time Social Media Sentiment Predictive Analytics Demand Forecasting Price violations on Retail sites Data Warehousing Text Mining Security Video Analysis Retail Data scrapping Machine Learning ioT Multi site ERP Marketing Funnel Sales Channel Mgmt Smart Home Natural Language Processing (NLP) VR Gaming Device Events
- 6. ANALYTICS AT SCALE - SUMMARY • Create a decentralised self-service analytics environment for traditional business reporting and analytics (Descriptive and Diagnostic Analytics). Becomes a purely EXPLICIT experience. • Allowing for a centralised, cross-functional shared advanced analytics service tasked to deliver Predictive and Prescriptive analytics to the organisation. • A minimal investment, with leveraged return.
- 7. • Logitech and Competitors Products Consumer Reviews § Core capabilities to scrape retail websites for consumer reviews § Raw and structured data available for advanced analysis § Petabyte data volumes support and performance § Sentiment analysis for business decision § BGs data analysis for consumer complaints and issues and negative reviews • Logitech Product Pricing § Core capabilities to scrape retail websites for pricing of Logitech products § Amazon buy button analysis § Amazon.com and marketplace price analysis § Price violation analysis DATA VOLUME – COMPLEXITY (RETAIL USE CASE)
- 9. LOGITECH CONFIDENTIAL: NOT FOR DISTRIBUTION NEW TECHNOLOGY A VISION
- 10. LOGITECH CONFIDENTIAL: NOT FOR DISTRIBUTION AUTOMATED INSIGHTS WORDSMITH for TABLEAU
- 12. TEXT ANALYTICS
- 15. LOGITECH CONFIDENTIAL: NOT FOR DISTRIBUTION REAL-TIME ON DEMAND delivery to your PHONE and DESKTOP and DASHBOARD • Executive Summaries • Customer by Product • Product by Customer • Demand / Supply updates • Market Analytics / Market Share • Marketing Reports • Competitive Analysis • Sentiment • ... NLP is a scalable self-service environment, meaning we can open it to business users (self-service) and allow them to improve and drive business impact and adoption. It is language agnostic, meaning we can publish reports in the language they are written.
- 17. SOLUTION ARCHITECTURE Amazon Web Services AWS GlacierAWS S3 AWS Redshift Pentaho DI Pentaho Operations Mart Cloudwatch SNSIAM Cloudtrail EMR SPARK Python / R AWS RDS Denodo Data Virtualization Tableau Pentaho BA Data Interfaces Web ServicesOBIEE CUBES Snowflake Text Analytics
- 18. JOURNEY TO CLOUD Cloud empowers IT organizations to redefine the way Data services are produced and delivered Scalable • Infrastructure scaled up - down on the fly (Elastic) • Focus on simplicity, security, robustness, and scalability Efficient • Infrastructure costs are pay as use Reliable • AWS managed services Managed & Governed • Transparency on usage patterns • Breadth of services offered, pricing, performance and flexibility
- 19. IMPACT OF DATA VIRTUALIZATION PLATFORMS By 2018, organizations with data virtualization capabilities will spend 40% less on building and managing data integration processes for connecting distributed data assets.” -Gartner
- 20. NEED FOR DATA VIRTUALIZATION Abstract access to disparate data sources A single semantic repository Optimized data availability in real-time to consumers Centralized, governed and secured data layer
- 21. MANAGING BIG DATA WITH DATA LAKES Ø Organizations are exploring data lakes as consolidated repositories of massive volumes of raw, detailed data of various types and formats to overcome Big Data challenges. Ø But creating a physical data lake presents its own hurdles, one of which is the need to store the data twice which can lead to governance challenges with regard to data access and quality. Ø Data Virtualization technologies can improve an organization’s ability to govern and extract more value from its data lakes by extending them as logical data lakes. - Ventana Research
- 22. BIG DATA FABRIC Denodo Data Virtualization
- 23. • Logical model can be predefined for the data • Eliminates load processes and the need to update the data • Uses the security and governance system already in place • Collects and maintains statistics and determines optimal query execution • Avails Cache mechanism and pushdown for optimal performance • Array of connection options from structured to unstructured data • Business Layer, enabling data Consistency through single object, multiple consumers • Rapid prototyping • Data Audits • Sandbox for data science VIRTUALIZATION BENEFITS
- 24. • Catalog exploration o Graphical representation of data model o Data lineage o Integrated catalog search • Data Discovery o User friendly query wizards with drill down capabilities o Export to CSV, Excel and Tableau Data Extracts • Secure o Leverages Denodo’s security model and access control o Available vis SSL/TLS GOVERNANCE - DENODO INFORMATION SELF SERVICE
- 25. CLOUD AND DV BENEFITS • Proactive – IT has embraced cloud as a model for achieving innovation through increased efficiency, reliability and agility • Reusability and template development • Rapid innovation within governance structure, balanced costs, risks and service levels • Greater efficiency and reliability, enabling broader audience to consume IT services via self-service
- 26. LESSONS LEARNT • Reduced Spend • Live migration • Flexible and cost effective • Better business continuity • Speed to deliver • Easier to manage • More efficient IT operations • low hardware costs • No or reduced Software costs Cons • Possible learning curve • Accountability • Getting all vendors to gel well Pros
- 27. REFERENCE ARCHITECTURE Metadata Management, Data Governance, Data Security Cost and Usage Pattern Sensor Data Machine Data Logs Social Data Clickstream Data Internet Data Image and Video Cloud Applications Enterprise Applications Data Sources Data Insights Self-Service / Data Discovery Reporting Predictive Analytics Statistical Analytics Sentimental Analytics Text Analytics Data Mining Data Virtualization Data Collection Real-Time Data Access (On-Demand / Streaming) C D C E T L EDW ODS Cloud DW NoSQL Data Warehouse File Storage (S3) Batch DW Spark SQL NoSQLSearch Search Big Data In-Memory Analytical Appliances Real-Time Decision Support Alerts Scorecards/ Dashboards
- 28. DATA VIRTUALIZATION IN IOT ECOSYSTEM Other RDBMS (apps, CRM, SAP) Other Sources (SaaS, SFDC, etc.) Ingestion Streaming analytics Big Data Storage Batch analytics, Machine learning Streaming data Traditional batch processing (ETL to EDW) Semantic Model Secure + Combine + Enrich Meta Base
- 29. INTEGRATION WITH IOT - STREAMING Ingestion buffers Streaming Analytics Storage Read temp window buffers SQL based enrichment Secure + Combine + Enrich
- 30. BENEFITS • Adds context to device data – Enrichment and augments with other sources (internal or external) – No replication: enables Virtual Data Lake • Simplifies publication of useful results – End-user oriented semantic model – Reports and dashboards in SQL – Data as a service (REST, OData) • Improves governance – Security (AD integration, data restrictions, masking, etc.) – End-to-end lineage – Abstraction of source technologies