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?

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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Data Exploration and Analytics for the Modern Business

  1. 1. Data Exploration Analytics for the Modern Business Scott Hoover, Data Scientist at Looker May 29, 2014
  2. 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. 3. Some of Looker’s Customers
  4. 4. Greg Jones Lead Developer at Smartling Introducing…
  5. 5. Data Exploration at Smartling Greg Jones, Lead Engineer
  6. 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. 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. 8. • Optional; further accelerates the translation process • The fastest way to translate and deploy websites • No internationalization/recod ing required Global Delivery Network
  9. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 19. Thank you for your time, and now back to Scott
  20. 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. 21. Looker Demo Explore section — building queries Dashboards —visualizing queries LookML and embedded SQL — lightweight development
  22. 22. Q & A
  23. 23. More Questions?