Accelerate 2012 chicago - orbitz
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Analytics and Big Data
![[Raghu Kashyap and Jami Timmons], [Orbitz Worldwide]
BIG DATA COMES WITH BIG
CHALLENGES](https://image.slidesharecdn.com/accelerate-2012chicago-orbitz-120629135006-phpapp01/85/Accelerate-2012-chicago-orbitz-1-320.jpg)
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Accelerate 2012 chicago - orbitz
- 1. [Raghu Kashyap and Jami Timmons], [Orbitz Worldwide] BIG DATA COMES WITH BIG CHALLENGES
- 2. ABOUT Raghu Kashyap Technology Director, Web Analytics raghu.kashyap@orbitz.com @ragskashyap http://kashyaps.com @ragskashyap at #ACCELERATE
- 3. TIP #1: Big Data for the right reasons @ragskashyap at #ACCELERATE
- 4. TIP #2: Centralized Decentralization • In God we trust……. But all others bring Data • Break down into smaller chunks of data extraction and focus on wins • 750TB of raw data and still growing @ragskashyap at #ACCELERATE
- 5. TIP #3: Show me the money • EFX – Every Friggin X • PPC bidding efficiencies • MAC vs. PC @ragskashyap at #ACCELERATE
- 6. TIP #4: Unlock the Data Insights • Measure the performance of your feature and fail fast • Challenge now is unlocking the value of this data for non-technical users • Find the right Ambassadors in each of your key business areas. @ragskashyap at #ACCELERATE
- 7. TIP #5: HIPPO is your best friend • Expect organizational resistance from unanticipated directions • You can do wonders in the analytics area if you get buy in. @ragskashyap at #ACCELERATE
- 8. TIP #6: Big Data Model for Success • Do you have the Technology strength to invest and use Big Data? • Big Data mining != analysis • Key Data Warehouse challenges still exist (time, data validity, organizational hurdles) • Analytics using Big Data comes with a price (Technology, Resource, Time, Governance ) @ragskashyap at #ACCELERATE
- 9. ABOUT Jami Timmons Product Lead, Business Intelligence Jami.timmons@orbitz.com @ragskashyap at #ACCELERATE
- 10. TIP #7: Data Governance is like water………….. You can only go so long without it @ragskashyap at #ACCELERATE
- 11. TIP #8: Senior Level Buy in is a MUST • Find a way to illustrate the impact of poor data governance to your Senior Leadership • Their buy makes overall company support possible @ragskashyap at #ACCELERATE
- 12. TIP #9: Acknowledge Winners and Losers • Not everyone will wind up happy in the short term • Long term benefits should be your focus @ragskashyap at #ACCELERATE
- 13. TIP #10: It’s not a Race….its a Journey @ragskashyap at #ACCELERATE
- 14. SUMMARY • #6-Big Data Model for Success • #1-Big Data for the right • #7-Data Governance is reasons like water... You can only • #2-Centralized go so long without it Decentralization • #8-Senior Level Buy in is • #3-Show me the money a MUST • #4-Unlock the Data • #9-Acknowledge Insights Winners and Losers • #5-HIPPO is your best • #10-It’s not a Race….its a journey friend @ragskashyap at #ACCELERATE
Editor's Notes
- Welcome everyone. Its great to put the faces and twitter handles together 3. Betweenme and Jami we would like to talk through our experience and success with Big Data4. Along the lines we would also talk through some of the challenges we came across.
- A while back I met someone at the train station who asked me what I do?I said I head the web analytics team at OWW and I help shape up the strategy and vision of Big Data Analytics and in turn help our business teams to get insights on business performance.So he said, Ah you build reports :-)2. At Orbitz we started using Hadoop in the early days of 2009 and haven’t looked back since then.
- OWW Operates multiple brands across the globe .We have brands both domestic and international.So with so many brands and so much data we had quite a few challenges?We generate hundreds of GB of log data per day. How can we effectively storethis massive data and how can we mine this data and make sense out of it? We realized that with all the challenges we had, we had to innovate and experiment new ways to enable successful analytics at OWW Big data infrastructure with Hadoop has been a huge success at OrbitzWe started of withMachine Learning teamAll of business analytics of web analytics dataStoring and processing production application logsData cache analysis6. So what does Big Data buy us? We can now store data for a long period of time without worrying too much about the space Analysts and developers have access to this data set.Developers and analysts can run adhoc queries to support our business needs. Tying data sources across different mediums (app logs, web logs, MVT logs, Txn data, SM etc…)
- So how do you organizationally structure yourself and Big Data so that you can be effective both in terms of resource utilization and setting the platform for success.This is what we call the Centralized Decentralization. 3. With this approach the core analytics team controls and supports the individual teams when it comes to data extraction and modeling.4. This prevents one team from being the bottle neck with data extraction and analytics 5.Here is an example of how we process our site analytics data today. We FTP the log files into our Hadoop infrastructure daily. The files are LZO compressed for better storage utilization. Developers then write Map reduce jobs against these raw log files to output data into HIVE tables. HIVE is a DW equivalent of Hadoop Non Technical folks, have skillset to run queries against HIVE tables.
- 1. Last year at EMetrics I had interesting tweet exchanges with few folks. In essence we were talking about the importance of visitor level granularity of data and how it will impact personalization.Here I have 3 use cases which is being enabled through Big Data at OWW.2. Our CEO affectionately calls this EFX – We use Hadoop to analyze the attributes from Site Analytics, Internal logs(Consists of multiple application logs and NOT just weblogs), MVT logs. All these in essence will funnel our regression models. One of the key wins from the Machine Learning team was to analyze, build and implement the recommendation engine for out hotel search. The data was from our Site Analytics, and some other internal application logs. was analyzed using MR Hadoop jobs. The results we saw was astonishing. 7% interaction rate37% had a likely chances to continue deep in the funnel2.6% increase in booking path engagement.The beauty of this is the Big Data Analytics is fed into a machine and it learns and changes as time progresses. 3. PPC bidding based on Site Analytics data. EX in turn funneling out PPC channels. The results are very encouraging. Helps us with regression analysis4. The final use case is where we learned that Mac users tend to in general spend more money on our sites
- 1. Big Data team is formed under Business Intelligence team at Orbitz Worldwide.Reflects the importance of big data to the future of the company.Allows the Big Data team to work more closely with the data warehouse and BI teams.A while back we received an email from a user seeking access to Hive. We sent him a detailed email with info on accessing Hive, etc. Received an email back basically saying “you lost me at ssh”. 3. To sellanything you have to be a good story teller. Find the right partners in different parts of your organization who can help you with the story telling.
- 1. We faced organizational resistance to deploying Hadoop.Not from management, but from other technical teams.Required persistence to convince them that we needed to introduce a new hardware spec to support Hadoop.CTO was a big believer and provided technical guidance with Big Data. This helped a lot in making this a success at organization. Its not everyday you have CMO and GVP asking their team members to get data out of Hadoop
- If you have the strength of technology go for it. With the core process of centralized decentralization and being agile how do you succeed? Dimensional modeling is great but like someone wise said 'All models are wrong but some are useful" :-) My point here is data without analysis is like a Ferrari without gas. Big Data needs heavy investment from time and resource perspective . It comes with challenges such as Data Governance With that nowIwould like to invite Jami to talk through the challenges and importance of Data Governance.