From Big Data to Actionable Insight: What's Needed on the Back End

Editor's Notes

• #2: https://liaison.com/intranet/learning-sessions/big-data – Presentation from Brad Anderson
• #5: Background on this at http://www.bigdata-startups.com/BigData-startup/mastercard-applies-big-data-to-help-retailers-achieve-better-results/ But for more descriptive background, although he’s talking about Amex and you should be careful to extrapolate, listen to Brad’s presentation (18:09) (Didn’t want to use Amex because Brad chose to keep it blind and I want to respect that as I couldn’t find any details about it online - for the other two case studies I could find independent online info. so felt it was public knowledge we could feel free to use)
• #6: Background on this at http://www.bigdata-startups.com/BigData-startup/mastercard-applies-big-data-to-help-retailers-achieve-better-results/ But for more descriptive background, although he’s talking about Amex and you should be careful to extrapolate, listen to Brad’s presentation (18:09) (Didn’t want to use Amex because Brad chose to keep it blind and I want to respect that as I couldn’t find any details about it online - for the other two case studies I could find independent online info. so felt it was public knowledge we could feel free to use)
• #7: Background on this at http://www.bigdata-startups.com/BigData-startup/mastercard-applies-big-data-to-help-retailers-achieve-better-results/ But for more descriptive background, although he’s talking about Amex and you should be careful to extrapolate, listen to Brad’s presentation (18:09) (Didn’t want to use Amex because Brad chose to keep it blind and I want to respect that as I couldn’t find any details about it online - for the other two case studies I could find independent online info. so felt it was public knowledge we could feel free to use)
• #8: Background on this in Brad’s presentation (20:20)
• #9: Background on this in Brad’s presentation (20:20)
• #12: Volume, variety and velocity of big data make the need for veracity even more acute... some additional background text I found: “Veracity refers to the messiness or trustworthiness of the data. With many forms of big data, quality and accuracy are less controllable (just think of Twitter posts with hash tags, abbreviations, typos and colloquial speech as well as the reliability and accuracy of content)”
• #13: Volume, variety and velocity of big data make the need for veracity even more acute... some additional background text I found: “Veracity refers to the messiness or trustworthiness of the data. With many forms of big data, quality and accuracy are less controllable (just think of Twitter posts with hash tags, abbreviations, typos and colloquial speech as well as the reliability and accuracy of content)”
• #22: Liaison set up a cloud-based collaboration space for data sharing among major pharmaceutical and third-party research partners Space hosts large sets of omics data (genomics, proteomics), pre-clinical, clinical and real world evidence-based data Hosted on our cloud integration platform Our data integration engine delivers transformed data into the repository for access, analysis and visualization from a Web interface Pharma R&D Case Study
• #24: This is the Merck Heron project that we have NOT DONE, only discussed, that Brad told us about on phone call As I understood from the call: We would load in 80 years worth of historical compound data Researchers would be able to leverage this data by running current research models against it before they embarked on costly research or fundraising for costly research We would also store the models for execution against the data Integration/harmonization function is not all that clear to me but I would imagine some types of integration/harmonization would need to take place in order to get the data in the proper format for execution of models against it
• #26: Did this project about a year ago Major pharma wants to understand how often their medication is prescribed/mentioned in handwritten Japanese doctor’s notes In addition: Where it is geographically subscribed What context it is subscribed in What diseases most often associated with in notes We first translate Japanese raw text (pharma notes) and other information into English Then use NLP (natural language processing) to take in surrounding words from form and apply context so that conclusions can be drawn We’re taking unstructured text and turn it into actionable information   More information can be found at 33:19 of Brad’s Liaison Learning session: https://liaison.com/intranet/learning-sessions/big-data
• #28: This is a pharmaceutical but reminder that you wanted it moved to another industry since we’re so pharma-heavy (I had suggested financial) We capture data from 7 analytics providers (logos in PPT) and aggregate and harmonize into a single data model for unified viewing and reporting This dramatically increases speed at which this valuable marketing information is delivered across the business With holistic view at their fingertips, client is able to better understand trends and target potential customers   More information can be found at http://liaison.com/docs/case-studies/liaison---web-analytics-integration-pharmaceutical-case-study.pdf?sfvrsn=6
• #30: This is the one I emailed you Monday that I basically “invented” as there is no background information on it except for diagram (also it seemed very transactionally B2B based and not all that suitable for this talk so I took serious marketing liberties with it). The idea came from the WB (Warner Brothers) diagram in which I noticed that there were feeds coming into our cloud from Flixster and Rovi, two movie viewing platforms I basically fudged the idea that, similar to the previous case study, Liaison is integrating and harmonizing these two feeds (along with Netflix) in order to provide client with a view into how often their movies are being downloaded/viewed from these sites, when, how they’re being rated, etc.
• #32: This is Mohawk Fine Papers as this is the one named use case we’re using Doesn’t have to be blind Liaison is their unified cloud data integration and management platform and acts as the integration backbone and single point of contact for all IT services within Mohawk and with many hundreds of customers, trading partners, and cloud service providers. This is another one very transactionally based and A2A/B2B integration so not sure how you want to spin it except to say that our integration capabilities are far reaching across an entire ecosystem, lots of platforms, document formats, and trading partners’ systems. Mohawk is using our platform for: B2B integration A2A integration MFTaaS (master file transfer-as-a-service) SaaS (Mohawk is consuming a wide range of business applications as services, performed in the cloud) DaaS (process data-based transactions like shipping quotes or credit checks in the cloud)   More information can be found at http://liaison.com/docs/case-studies/liaison---mohawk-fine-papers-case-study---cloud-integration.pdf?sfvrsn=8