- Talkl about how to put gov't on a more sustainable path.
- They advise congress on how to improve performance
- Driven by mandates and requests from congress.
- Fact-based organization-analysis hinges on reviewing, compiling, analyzing data
- Made 380 recommendatin to reduce overlap.
- 108 billion in improper payments (some agencies haven't even reported.
- Health care is particularly worrisome. 5 to 8% of GDP in next few yaers.
- Number of people 65 and older will double in next few years.
- Overpaying people by tens of billions.
Panel on Working HOrizontally" Analytics as a bridget
- Questiona sked about analytics just being repackaged as something we have always done.
- Essentially agreed except that the repackaging simply helps to brand the usefulness of using data in making decisions.
Using Social Data for Public Sector Analytics
- Will mayo and Rebecca Goolsby
- Information moving rapidly.
- Shared example of tweet from San Francisco flight and news story didn't come in for 10 minutes beyond that tweet.
- Used Social Media in public sector in response to Hurrican sandy (e.g. finding where a bunch of trees fell down)
- Weather alerts via twitter where otherwise equipment is unavailable
- Need to use consented data and that available in private sector.
Big Data is not new-no such ting
- Goal is to talk about what is data to your organization
- Mid-2011 was when the google search term exponentially increased
- No such thing as big data, but there is a lot of data out here
- 234 milion e-mails per minute.
- 2.5 quintillionb ytes of data created with 90% of worlds data created in last two years alone.
- Data governance expert peter aiken estimates 80% of the data is not useful.
- Is any of it valuable?
- Big data can be defined as data sets that are too large for you to handle.
- Origin of Big Data
- Some credit John Mashey, chief scientist at silicon graphics in 1990's.
- He was using the label for a range of issues, essentially that the boundaries of computing keep advancing.
- Nate silver labels big data as a fashionable word. It is when we deny our role in the process of data driven predicitons that the odds of failure rise. (signal and the noise book)
- Mark Madsen says big data isn't hype but it is being hyped and says tthe reality is that big data is about new models for data processing.
- Sue Feldman says it is a set of technologies that solve complex information economically and includes volume, variety, and velocity.
- The Hype
- Gardner shows graph of hype cycle. Big Data is two to five years from top of hype cycle.
- Business is about making decisions, data can help
- We gotta do the hard work to figure out the value of business data.
- Need to be willing to experiemnt and willing to be wrong.
- Valuable data is not always big!!!!!!!!!!!!!!!!!!!!
- Analytics can scale in a number of ways.
- BIG DATA INITIATIVES--frighten presenter
- Technology looking for a problem. Cart before the horse.
- Need to first understand the problem before we shotgun some big data initiative.
- Kudzoo????
- Data Vs
- Vacant is a new oen. Available. Availability of data. people not wanting to share data.
- Volume. Big data implies volume. Easiest one from analytical view to solve. 1 tterrabyte to 8 megabytes of useful information. You can shrink that data. Get rid of data rot.
- Velocity
- Variety-biggest challenge. Connecting data that wasn't designed to be conbined. Fuzzy matching.
- Value-different colored bubble on slide. He says its a diferent kind of question.
- Veracity, what is the truth of the data and who says what the truth of that data is?
- Vitality-is the system able to adjust around the data.
- Variability-changing environment around us.
- CRISP-DM process for data mining.
- Goal definition to business understanding to data understanding to data preparation to modeling to evaluation to deployment to knowledge application. Operationalize it.
- Background-at least two components to any analytic architecture.
- data storage (databases, dw, spreadsheets)
- Analytical processing (dashboards, models, metrics, etc.)
- They'd rather have the raw data, vice the aggregate.
- Current standard is to combine storage with in-core analytics. Limited interactionb etween storage and processing
- In-database-effort to improve sall data by pushing analytics into data storage. Avoids data transfer from databse to analytics. (relational databases, teradata, SAS, etc.).
- Computational" 9 million records with 2000 attributes? Big data?
- What if you wanted to test 17 drug interactions on multiple morbidity outcomes?
- It is about the data-treating data as an asset implies systems is designed to support
- An asset is a resource controlled by org-data has a value.
- You may have a lot of data
- Value first through 'test and learn'
- Data governance to maintain flexibility
- Use technology to operationalize.
- It's like an irritating fly buzzing around your head-----big data.
- If they push back and say we need analytics
- He says we should push back and ask what problem do you want to solve?
- New OMB analytics guy said all our stuff neds to be structured.
- A lot of knowledge in tha tbusiness is in the people. Not captured in the data.
- problems are much more important to define.
- Apps
- ENTERPRISE MINER FROM SAS SAVES A LOT OF TIME
- HADOOP goes well for google in text analyzsis I believe.
- Oracle does good things.
- Don't need to be a data scientist to do big data
- Rather have a heart surgeon to do surgeory on my heart.
David Jakubek. Case Study-data to decisions building dta analytics capability in the department of defense.
- Today is international talk like a pirate day. Okay....
- Told a really long joke. Not worth the time imo
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Predictive Analytics in Medicare
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