Data design thinking: data cleaning improvements using tableau prep - Felker, Christopher (Dryad, 2018)
Tableau Prep automatically shows errors and outliers in data and employs
fuzzy clustering to help you with the common, repetitive tasks like fixing
spellings errors or reconciling entities across data sources. Project
Maestro shares the same calc language and governance structure as the rest
of Tableau, so you can get started easily. And with a streamlined sharing
experience to Tableau Desktop, Tableau Server and Tableau Online, a user
can experience data prep and analysis in one continuous flow.  In this
first phase of the Enterprise implementation of Tableau at UCSD Health, we
have focused on getting data from five vendors (Epic, Experian and Bank of
America / Healthlogic) ready for Tableau work. The data sets we've
created in Hyper have a zero date of 2013 10 01. They are also made to
conform to  SDMX standards whenever possible. SDMX stands for Statistical
Data and Metadata eXchange and is an international initiative that aims at
standardising and modernising (“industrialising”) the mechanisms and
processes for the exchange of statistical data and metadata among
international organisations and their member countries. Current operations
depend on a data file loosely termed an 'accounts trial
balance'. This data is actually a blend of account level information,
and a 'bucket' which is a software generated construct. The
bucket information relies on affinity analysis - a data analysis and data
mining technique that discovers co-occurrence relationships among
activities performed by (or recorded about) specific individuals or
groups. In the case of UCSD Health billing accounts, the affinity is based
on the patient, their insurance coverges and the split of fiscal
responsibility. SDMX is sponsored by seven international organisations
including the Bank for International Settlements (BIS), the European
Central Bank (ECB), Eurostat (Statistical Office of the European Union),
the International Monetary Fund (IMF), the Organisation for Economic
Cooperation and Development (OECD), the United Nations Statistical
Division (UNSD), and the World Bank. These organisations are the main
players at world and regional levels in the collection of official
statistics in a large variety of domains (agriculture statistics, health
care, economic and financial statistics, social statistics, environment
statistics etc.). We've published a sequence of Adobe Spark guides
that can be useful for providing UCSD employees recognition for learning
that happens anywhere. A digital badge is an online representation of a
data analysis skill we are promoting and sharing with our sister UC
medical centers. References Tableau 2018. Tableau Prep.
<https://tabsoft.co/2vbfBG1> last accessed 2018 04 24. SDMX
2018. Learning about sdmx basics. <http://bit.ly/2qvze5J>
last accessed 2018 04 13.
The operational data has been defined in data structure definition
specifications. Reference copies that have been anonymised have been
provided here and in Adobe Spark publications.
dsd/043 dimension sdmx data structure definition exposure type dsd/045
dimension sdmx data structure definition valuation method universal
resource locator url <http://bit.ly/2wFtGw8> dataset
<CBD2 – consolidated banking data> data structure definition
<statistics on consolidated banking data> ECB_CBD2 agency
<ECB>  download SDMX 2.1 schema of the ECB_CBD2 DSD
<http://bit.ly/2ImA7p3> uc health / ucsd health dataset
<CCD1 - consolidated claim data> data structure
definition(s) <dsd/047 attribute proprietary data structure
definition claim 05280602 epic systems> UCH_CCD1 agency
<0000 0001 2107 4242 ucsd health> access to CCD1 is through
the ucsd tableau server Metrics based on this standard are developed by
persons listed in this resource d/416 2018 19 131 master organisation
chart ucsd health patient financial services 0000 0001 2107 4242 ucsd
health Discovery metrics Beta metrics CCD Bm 0.0 Alpha metrics CCD Am 0.0
Production metrics CCD 0.0