Dallas Data Brewery Topics
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TopicDetailsSuggested byVotesScheduled
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1Perception of Data QualityWhat is "quality data"? How business side sees it? How data engineers see it? Any stories of misunderstandings?Stefan UrbanekJune 2013
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2Troubles of Getting DataWhat are the issues of getting the data to begin with? Existence, structure, security, laws, automation, digitization, scraping, quality, volume, speed ... How do you deal with it? What tricks and tools to use and why?Stefan Urbanek
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3Creating Big Information from Big Data with defining result from strategies.Information from data putting the fragments into the appropriate order by organizing your hypothesis and end result. Doing this with out fooling yourself or lying. Creating Big Information from Big Data with defining result from strategies.
John Gostomski
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4Data Projects and Audience
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5BI and MarketingExisting Data, big and small, can be used by the business that owns it to further promote and market their services. This is a fact that many companies may be aware of it, but are not doing anything for their own services. What are some good approaches? What are some good frame of mind to even begin to think about this?Will Gunadi@StiiviJuly 2013
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6Open DataWhat is Open Data and Open Government Data? Presentation of existing open-data projects: goals and principles.
Discussion topics: How can business benefit from Open Data? Why and how to integrate Open Data into business? Should businesses open their data? Why and how?
Stefan Urbanek
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7Analytical CRMCustomer (or Constituent) Relationship Management from the analytical perspective. Why to do it? What we can learn from ACRM data and how we can use them in day-to-day business? Examples of ACRM projects (I can show some from Mobile Telco industry). ==> LOOKING FOR: more ACRM examples.Stefan Urbanek
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8Error Management ModelHow to manage errors (incorrect addresses, names, and other personal information for example) - How to measure the "quality" (efficiency, effectiveness) of your process. A look at an error management model that can be used to rate a process that identifies, prioritizes (for resolution), manages, and monitors/communicates information about errors. I can present background on the I constructed model and then

1) open for general discussion/enhancement,
2) split up attendees for discussion/enhancement in groups regarding identification, prioritization, management or monitoring, or
3) however you think would be the best way to proceed.

Is the model complete? Too granular, not granular enough? Do others have similar models/methods for comparison? How could it be applied?
Eric Noack@Noack, @StiiviAugust 2013
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9Business RulesHow business rules are used? What are the processes around business rules? Examples of rules helping to solve a problem, examples of rules gone bad. ==> LOOKING FOR: storiesStefan Urbanek
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10Hight Volume Transactions and Real Time DWHHow do you deal with high volume transactions? What is "real time" datawarehouse? Examples of "realtime" and reasons for "real time" granularity. Blurring boundary between OLTP and OLAP with new solutions. ==> LOOKING FOR: storiesStefan Urbanek
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11Star Schema (traditional datawarehouse)What is star and snowflake schema? From basics to many-to-many and hierarchical dimensions.Stefan Urbanek
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12NLP QueryNatural language processing – demontration of queries on top of DB. Requirements for data modelling.Stefan Urbanek
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13Data Security and PrivacyHigh-level discussion of data security and privacy in the age of information sharing. Subtopics: data governance, regulatory domains/requirements, strategies for compliance, real costs of non-compliance/data breaches, data anonymization techniques, privacy policies and considerations, best practices definition, application and data security approaches (firewalls, compartmentalization, design for failure, logging/detection, encryption (transmission, at-rest, key handling)), data owner consent, etc. As applied to domains like healthcare/disease surveillance, government data, business data analytics/operational data, non-profits.Jay Gates
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