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Webinar Presentation- Accelerating Growth through Big Data and Analytics

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Webinar Presentation- Accelerating Growth through Big Data and Analytics

  1. 1. Accelerating growth through Big Data and Analytics December 6, 2012 Presented by: 1 /
  2. 2. Key points we want to make today I. Overview on Luminar and Impetus II. Shift from the status quo III. How Big data is helping advance how we market to Latinos IV. The journey, implementation approach V. A new business model supporting clients VI. Lessons Learned 2 /
  3. 3. ● Luminar is an analytics and modeling company focused on helping clients become more efficient in targeting the US Hispanic market ● Company was established Spring 2012 ● Luminar is a business unit of Entravision Communications (NASDAQ: EVC) ● Based in Denver with operations in LA, DC, Buenos Aires, Argentina; and Mexico City ● Key client segments include: Retail, CPG, Financial Services, Media & Entertainment, and Publishing 3 /
  4. 4. Big Data…What it is “Big Data is NOT just analytics. It's NOT just about storage. It's NOT just about anything - it's about everything. It's about tossing out the old way of doing things because those ways simply won't work in the world of BIG.” - Steve Duplessie, founder and senior analyst at ESG 4 /
  5. 5. The value of Big Data varies by company Improve Operational Grow Sales & Empower New Efficiencies Profitability Business Models ● Save Time ● Actionable Customer ● Competitive ● Lower Complexity Insights Differentiation ● Self Service ● Reduce Churn ● Data As a Service ● Predictive Analytics ● Data Science Services ● Improve Customer ● Incubate New Experience Ventures 5 /
  6. 6. Understanding the magnitude of the US Hispanic Population – some basic facts… 6 /
  7. 7. Understanding why Luminar decided to make a Big Data play Four key factors influenced this decision: 1. We wanted to shift from the current marketing paradigm targeting Latinos focused on sample data 2. We recognized that Hispanic consumers are under represented with most marketing approaches 3. Our service offerings are synergistic to our parent company 4. Our model would necessitate ingesting vast amounts of diverse data that required a robust analytics environment 7 /
  8. 8. Underserved market – what we saw in the marketplace ● Brands are making marketing investment decisions on limited information ● Targeting assumptions based mostly on survey or sample methods (i.e. “Latinos over-index on mobile usage”) ● No real insights or true performance of program ● Campaigns mostly based on just ethnically- coded data ● Stereotype approach; they speak Spanish, consume Spanish media, heavy online users…therefore…good target ● Little or no cultural relevancy 8 /
  9. 9. Actionable insights is an evolving process • Testing market • Retains Hispanic • Increased ability • Use analytics to • No in-language agency to capture, prescribe actions experience • Use focus group aggregate and • Effective at sharing analyze data information and • Mass media, single • Multi-channel, often channel not integrated • Use analytics to insights guide actions • Strong ability to • No dedicated • Top Hispanic DMAs • Growing use of capture, aggregate, • Hispanic team • Bilingual experience analyze or share insights to guide • Lack of • Qualitative data use future strategy information understanding how • Strong use of to use analytics insights to guide day-to-day operations 9 /
  10. 10. Big Data brings a high-value offering ● Ability to more precisely support customers across the entire marketing value chain: - Move from a media & communications discussion to a business challenge discussion - Help identify growth opportunity within the Hispanic market - Improve measurement of Hispanic market investments - Demonstrate ROI - Help accelerate growth through empirical data insights ● Transformative in the way we approached business and marketing needs ● Leverage big data environment and 3rd party data sources across business units 10 /
  11. 11. Winning executive buy-in was critical ● It’s was a significant investment and commitment that required CEO vision and support ● Developed detailed roadmap for success: - Prepared comprehensive plan detailing operations, resources, level of investment and implementation path - We weighted the need for big data as new revenue source for EVC - We identified “packaged solutions” for a big data offering - And, we clearly defined how big data fulfilled an underserved market and provided a shift from sample-based research to empirical analytics 11 /
  12. 12. Result – Luminar was created as a new Entravision business unit New business unit was created dedicated to serving Hispanic-focused analytics and insights 12 /
  13. 13. 13 /
  14. 14. Luminar Big Data would need to support these needs ● Analytics-as-a-Service platform ● Aggregate multiple sources of data from diverse sources - Licensed data - EVC data - Unstructured social data - Client data ● Offer an advanced and unique focused analytics service - Provide insights into Hispanic consumer behavior - Targeting customers in retail, financial services, insurance and auto segments ● Future offerings - Platform as a Service - White Label Services 14 /
  15. 15. Importance of aligning our vision with the right technology partner ● Proven track record – vendor had to have a demonstrable experience in the implementation of big data solutions ● Technology agnostic – We needed a technology partner that could help plan and deploy a solution architecture that was not married to any one vendor ● Experience with multiple technology providers/suppliers – We needed a partner that could understand the big data landscape now, in 6 moths and 18 months from today ● Blended team approach – Our ideal partner had to clearly understand that they would be operating in a blended client/vendor team environment 15 /
  16. 16. Deployment Objectives ● Build a best-of-breed model based on Luminar requirements - Take a vendor neutral approach - Lowest Total Cost of Ownership - No requirement to integrate with any legacy systems but SQL data migration ● Cloud based architecture ● Maximize “re-use” of vendor experience in Big Data ● Scalability for future data requirements ● Data security requirements ● Visualization ● Start with a “shoestring” approach 16 /
  17. 17. Build the right foundation for growth ● Impetus lead solution architecture and vendor selection process ● We established a solution framework that delivers four client offerings ● We architected a solution that defined all major technology Key Performance Indicators (KPIs) and SPOF 17 /
  18. 18. Advisors Experience. Architects Expertise. Applications Excellence. 18 /
  19. 19. Use Case Discovery and Implementations Verticals Big Data Analytics Value Use-Case Drivers and Patterns Functional areas 19 /
  20. 20. Solution Creation Approach - Steps • Understand Data, ETL and Analytical/Reporting & 1: Initial roadmap requirements Phase • Prepare comprehensive/ long list of candidates • Finalize assessment criteria and weightage factors 2: Finalize • Compare and recommend short list of POC candidates after detailed evaluation Candidates including vendor meetings • Implement, execute and benchmark critical use cases 3: POC • Execute POC candidates in parallel if possible 4: Final • Assessment report Phase • Recommend best solution fit 20 /
  21. 21. Vendor selection based on weighted scoring We created a custom-scoring matrix used for evaluating vendors pros and cons, defining requirements, and weighting against Luminar’s objectives 21 /
  22. 22. Technology – Hybrid architecture Data Sources Amazon AWS BI Display Outputs ETL / ELT Security Module BI Tools/ Data Licensed & Compliance Software Services Data Web-based Internal Data Workstation Talend Tableau Client Data Data Tablet Stream Statistical Processing Modeling Unstructured (LADAP) Data Mobile Other Data Sources Notebook Revolution /Hive Luminar Data Store Cluster (HDFS on EBS + P+G + 53 + RDS + Hdbase) Horton Works 22 /
  23. 23. Implemented solution overview ● Hadoop Cluster provisioned on Amazon EC2 in under four hours ● Original data sets imported from MySQL to HDFS/Hive using Sqoop and Talend ● Existing R scripts were modified to work with Hive for data analysis. Minimal code modification required ● Tableau work books modified to connect to Hive via Hortonworks ODBC driver 23 /
  24. 24. Our value proposition formula 24 /
  25. 25. How we do It Data Sources Data Management & Staging Insight Solutions Customer Visualization Application Luminar Luminar Data Insight App Customer Decision Engine Client Data Files Real-Time Cloud Insights 3rd Party Data Unstructured Data Big Data Analytics 25 /
  26. 26. Luminar Business Insights /
  27. 27. Closing remarks…Four key takeaways 1 Make a strategic connection to Big Data… In Luminar’s case, it provided a clear strategic path to a new marketing approach 2 Big Data initiative requires holistic approach bringing business and IT together to stitch all the parts 3 While IT and business need to work together, the business must own the initiatives 4 Have a flexible approach to your roll-out strategy 27 /
  28. 28. Accelerating Growth through Big Data and Analytics Q&A December 6, 2012 Presented by: 28 /

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