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Data Exploration and Analytics for the Modern Business


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Every day, your business generates enormous quantities of data. How can you unlock its value? How can you build self-service exploration experiences that empower frontline decision-makers? …

Every day, your business generates enormous quantities of data. How can you unlock its value? How can you build self-service exploration experiences that empower frontline decision-makers?

This webinar features Greg Jones from Smartling and Scott Hoover from Looker. Smartling is a powerful software platform for managing translation and localization of digital content. Looker is a data exploration platform that operates in the database. Together, Greg and Scott will introduce you to a modern approach to managing analytics in today’s fast-growing, web-centric business environments.

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  • 1. Data Exploration Analytics for the Modern Business Scott Hoover, Data Scientist at Looker May 29, 2014
  • 2. Looker is a data exploration solution that operates in the database to enable organizations to explore data in all its detail. What Is Looker?
  • 3. Some of Looker’s Customers
  • 4. Greg Jones Lead Developer at Smartling Introducing…
  • 5. Data Exploration at Smartling Greg Jones, Lead Engineer
  • 6. About Smartling • Translation management platform - cloud based • Simplify and accelerate the time to reach a global market • Quickly translate websites, mobile apps, and documents
  • 7. • Collect content automatically via API or connector • Facilitate professional, human translation • Deliver the approved translations back to the same place How It Works
  • 8. • Optional; further accelerates the translation process • The fastest way to translate and deploy websites • No internationalization/recod ing required Global Delivery Network
  • 9. • Visualize and analyze every aspect of the translation workflow • Segment data by customer, project, language, and more • Give business users control; drastically reduce developer involvement • Single View of Data Business Needs
  • 10. • Aggregate transactional data across 3 disparate MySQL instances • Quickly process large volumes of data -3B page views/mo; 84k metrics monitored • Have a simple API to expose data and a centralized view of it Technical Needs
  • 11. • Adopted Amazon Redshift as our high- performance data warehouse • Replicating all transactional data to Redshift on a nightly basis • Implemented Looker as our data exploration platform Solution
  • 12. • Strong alignment with Smartling’s development principles • Easy to integrate, continuously being improved, excellent support • Ease of use: even our CTO built a report! Why Looker?
  • 13. • Analyze velocity through the translation workflow; identify bottlenecks • Monitor vendor SLAs and recoup money for our customers when not met • Show how our software is improving the speed of translations Results - Translation Velocity
  • 14. • Understand how customers grow once they start using the product • Analyze how well our software is providing results • Show how our software is reducing our customers’ translation costs Results - Leveraging Translation Memory
  • 15. • Words Translated = 2,807,127 • Cost (assuming no Matches) = $421,069 • Cost w/ Matches = $321,314 • Savings over 18 Months = $99,755 • Savings per Month = $5,541 Results - Leveraging Translation Memory
  • 16. • Identify at-risk customers based on login frequency/translation activity • Financial analysis • Cut down development time for reporting • One single view of the data • Support business and technical planning through a full set of KPIs Results
  • 17. • Utilized by Customer Support, Account Managers, Sales Engineers, Product Managers, Developers, CTO, CEO • Interesting data findings included in weekly release notes • Integrating into Wallboard Adoption
  • 18. • Create even more views of our data • Expose data to customers • Allow customers to explore their data • Use data to show the value of Smartling Plan for the Future
  • 19. Thank you for your time, and now back to Scott
  • 20. How Looker Works Web application that connects to any relational database management system. Intermediate modeling layer (i.e.,LookML) that abstracts and simplifies SQL generation. LookML allows developers to manage: — Relations between tables — Introduction of intrinsic attributes — Creation of custom metrics User-friendly, web-based UI for exploring data.
  • 21. Looker Demo Explore section — building queries Dashboards —visualizing queries LookML and embedded SQL — lightweight development
  • 22. Q & A
  • 23. More Questions?