O slideshow foi denunciado.
Utilizamos seu perfil e dados de atividades no LinkedIn para personalizar e exibir anúncios mais relevantes. Altere suas preferências de anúncios quando desejar.

Enterprise Intelligence

6.656 visualizações

Publicada em

Enterprise Intelligence: Putting the Pieces Together
http://enterpriserelevance.com/kdd2016/keynote.html

These slides are for a keynote presentation delivered at the Workshop on Enterprise Intelligence, held in conjunction with the 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2016).

About the author:

Daniel Tunkelang is a data science and engineering executive who has built and led some of the strongest teams in the software industry. He studied computer science and math at MIT and has a PhD in computer science from CMU. He was a founding employee and chief scientist of Endeca, a search pioneer that Oracle acquired for $1.1B. He led a local search team at Google. He was a director of data science and engineering at LinkedIn, and he established their query understanding team. Daniel is a widely recognized writer and speaker. He is frequently invited to speak at academic and industry conferences, particularly in the areas of information retrieval, web science, and data science. He has written the definitive textbook on faceted search (now a standard for ecommerce sites), established an annual symposium on human-computer interaction and information retrieval, and authored 24 US patents. His social media posts have attracted over a million page views. Daniel advises and consults for companies that can benefit strategically from his expertise. His clients range from early-stage startups to "unicorn" technology companies like Etsy and Pinterest. He helps companies make decisions around algorithms, technology, product strategy, hiring, and organizational structure.

Publicada em: Tecnologia
  • Seja o primeiro a comentar

Enterprise Intelligence

  1. Enterprise Intelligence: Putting the Pieces Together Daniel Tunkelang dtunkelang@gmail.com
  2. Consumer technology is smart and getting smarter.
  3. While most enterprise technology remains dumb.
  4. Why is it so hard to deliver enterprise intelligence? Let’s talk about the challenges.
  5. Challenge 1: Enterprise data lives in silos.
  6. Intelligence comes from joining across data sets. Data connectivity is essential for unlocking value.
  7. Challenge 2: Weak signals.
  8. Compared to the web, no labels or behavioral data. Consumer technology takes these signals for granted.
  9. Challenge 3: Lack of incentives.
  10. Everyone talks the talk about data reuse and knowledge sharing, but most organizations don’t reward it.
  11. But it’s not all gloom and doom. Let’s talk about signs of life for enterprise intelligence.
  12. Sign of life 1: Open source.
  13. Open source not only provides flexibility to tinker, but also dramatically reduces barriers to adoption.
  14. Open source also encourages open standards. Interoperability breaks down silos between applications.
  15. Sign of life 2: Cloud computing.
  16. Cloud computing has dramatically reduced the entry cost for applications requiring large storage or compute resources.
  17. Not just about reduced cost of hardware and administration. Cloud platforms provide most of your stack out of the box.
  18. Sign of life 3: Consumerization of the enterprise.
  19. People increasingly use the same software for both consumer and enterprise applications.
  20. Consumer technology not only raises UI expectations, but also makes people expect greater interoperability.
  21. These signs of life support emerging opportunities to deliver enterprise intelligence—by putting the pieces together.
  22. Opportunity 1: Combine public and enterprise data.
  23. So much of the world’s data is just an API call away. Leverage it!
  24. External data tends to be wide and shallow. Yours is narrow and deep. Combine them!
  25. Opportunity 2: Invest in data standardization.
  26. There’s only so much you can do with strings and other unstructured data. Standardization unlocks value.
  27. At least strive for internal consistency. Even better, use standards with broad support.
  28. Opportunity 3: Create better incentives.
  29. Everyone wants access to everyone else’s data. But no one wants to invest effort. Tragedy of commons.
  30. People need to be rewarded for creating and improving data assets than can be leveraged across organization.
  31. To summarize:
  32. Open source, cloud computing, and consumerization are lowering barriers and raising expectations.
  33. We need to break down silos, strengthen signals, and create better incentives to standardize and share data.
  34. Enterprise intelligence is what we achieve when we put the pieces together.
  35. Thank you! dtunkelang@gmail.com

×