John Paredes has over 25 years of experience in data warehousing, business intelligence, and analytics. He specializes in dimensional data modeling for SQL Server Analysis Services cubes and has extensive experience with ETL processes using T-SQL and SSIS. He has expertise in healthcare, telecom, sales/marketing, finance, and manufacturing analytics. His background includes projects designing and building data warehouses, OLAP cubes, reports, and dashboards to support business intelligence and decision making. He has worked extensively with Microsoft SQL Server and Oracle BI tools.

1. 1
John Paredes/US Citizen/OLAPWorld@gmail.com/412-841-0879
Overview – Microsoft Data Warehousing with SQL Server Analysis Services
Decision support/business intelligence has been my passion since my immersion into it at GTE in the early 90’s. I like its
interdisciplinary nature, how it encompasses technology, data analysis, and the opportunity to learn w hat makes a business
tick. As a mathematician, I found dimensional modeling particularly interesting. Over years of experience, I have built up a
considerable expertise w ith dimensional modeling for both Kimball-style relational data w arehouses and multidimensional
databases such as SQL Server Analysis Services cubes and Oracle OLAP. I also have experience in the ETL process w ith
both T-SQL and SSIS. In addition to database/softw are skills, I have a background in analytic methods of statistics and data
mining. I am an experienced trainer and published author in the area.
I take an active interest in the application areas I w ork w ith. My focus over past several years have been healthcare. I also
have significant subject matter expertise in telecom, sales and marketing, finance, and manufacturing data.
Tools
Microsoft: SQL Server 2005/8/12, Analysis Services (SSAS), SQL Server Reporting Services (SSRS), SQL Server
Integration Services (SSIS), Microsoft Office SharePoint Server, Microsoft Performance Point Server, SQL Server Business
Intelligence Development Studio/Data Tools, Parallel data w arehouse (PDW).
Query Languages: T-SQL, MDX, Oracle OLAP DML, DAX
Oracle and other: OBIEE, Oracle 9i/Oracle10g/Oracle11g, Oracle Financial Analyzer, Oracle Analytic Workspace Manager,
Oracle Express Objects, IRI Express OLAP database, SAS JMP, R, Tableau, intermediate conversational Spanish
Education
 M.Phil. Statistics, Yale University, New Haven, Connecticut
 B.S. Electrical Engineering, Rice University, Houston, Texas
Projects
Population Health Data Mart - October 2016 – January 2017
Data mapping and design for a population health data mart in this project. I designed star schema data models for Health
Risk Assessment (HRA), Healthcare Effectiveness Data and Information Set (HEDIS), Adjusted Clinical Groups (ACG),
pharmacy and lab. Data sources included electronic medical records, claims, and encounter records.
I created a mapping document for integrating provider and provider attribute information from a variety of sources including a
population health legacy system, existing data w arehouse, accountable care organization data and data from a special
project.
Enterprise Data Warehouse - August 2015 – September 2016
I w as lead architect for an enterprise data w arehouse (EDW) for the Department of Health Care Finance (DHCF) in
Washington, DC. Activities included design of relational data w arehouse and SSAS cube database. I also designed
dashboards and reports and gave presentations to senior management.
I designed hierarchies and attributes and other dimensional elements for standard healthcare items such as ICD9/10
diagnosis, HCSPC/CPT procedures, DRG, service type, patient demographics, provider type and specialty.
Primary source of data w as Medicaid claim records. I designed this system from the ground up. Modeling w as performed
for a multitude of healthcare data elements such as patient, provider, diagnosis, procedure and location. Star schemas w ere
designed to address a broad spectrum of application area including utilization, fraud, inpatient, ambulatory care, home
health and rate setting.

2. 2
Grateful Patient Medical Fundraising Database - May 2014 – June 2015
Designed and created a “bridge database” application for appeals of charitable contributions that support medical
research. The bridge database integrates data from a variety of sources - including EPIC and Advance - and performs data
cleansing activities such as detecting and resolving multiple constituent entries (“de-duping”), performing exclusions,
reformatting data for external clients, and assigning a unique non-informative identifier code to constituents to support
HIPAA privacy mandates. System includes interfaces for manually entering information, producing data sets per user -
defined characteristics, appending gift capacity ratings and for providing analytics on major gift officer performance. A
combination of SQL Server 2012 and Microsoft Access 2013 w ere used to create this direct marketing application.
Public Transit Analytics - November 2013 – April 2014
Extensive interview s and know ledge engineering for the areas of ridership and on-time performance analysis for a public
transit organization in a major metropolitan area. Performed data modeling for transportation elements such as routes,
stops, trips, drivers, busses, passenger ons and offs, and w orked w ith distance calculations using ray tracing and
trigonometric calculations. Built demonstration dashboards using SSAS cubes to source PerformancePoint displays. Wrote
a detailed requirements document.
Inpatient Healthcare Analysis Platform April 2013 – September 2013
Designed a clinically oriented healthcare analytics platform. This effort encompassed the data modeling for a relational
healthcare data mart, SSAS OLAP/MDX development for advanced analytics, and the design and implementation
dashboards in multiple Microsoft-based technologies including Pow erView , PerformancePoint/SharePoint, and Excel. The
system includes standard medical elements, DRG/MDC hierarchy, ICD9 diagnosis and procedure codes, and medical billing
elements. This system has measures for the analysis of inpatient care w ith measures to support dashboards for, Patient
Flow s, Length of Stay, Case Mix Index, Procedure Counts, Admit and Discharge Status analysis, Readmissions, and Core
Measures. Demoed in YouTube video.
Healthcare Analytics Product Install - October 2012 – March 2013
I assisted in creating and populating the data structures required to supply data to a healthcare analytics product, w ith
financial, operational, quality and staffing dashboards. This w orkentailed integrating data from disparate sources and
considerable data cleansing w hich I implemented using T-SQL procedures and SSIS packages.
Healthcare Data Visits Analysis Modeling - August 2012
I created SSAS cubes based on an existing data w arehouse design. The client company had considerable experience w ith
relational data design but had struggled to transform their data models into high-performance data cubes. I w orked side-by-
side w ith them to not only create the cube but also teach them how to do it. Extensive use w as made of projecting computer
screen so all could see and participate in the process.
Healthcare Revenue Management Data Mart - August 2011 – July 2012
I designed and built an analysis platform to support the revenue integrity objective for medical billing. It consisted of a
relational data w arehouse and an OLAP database. Data from three disparate systems w ere integrated to give the capability
to compare expected payments w ith payments as reported from the financial systems and payments as reported from
explanation of benefit (EOB) data. The relational database follow ed a classic Kimball-style star-schema design.
The system had approximately 70 measures and numerous break-out dimensions including health plan, payer, facility,
patient type, ICD-9 procedure code, MS/APR DRG/MDC groupings, discharge date, and admission source. The data
covered both inpatients and outpatients w ith financial as w ell as clinical data. It included protected health information
(HIPAA). The data integration process accommodated numerous anomalies, such as different medical facilities using
different codes to indicate the same thing, unexpected contradictory lines of data describing an account, and missing values.
These w ere handled w ith both T-SQL procedures and SSIS packages.
Dimensional data w as organized into hierarchies and/or described w ith categorical information giving additional breakout
possibilities. In addition to aggregated source data, numerous derived metrics and derived breakout categories w ere

3. 3
incorporated. Derived data w as numerical (such as collection rates and insurance and patient payment variances) and
categorical (such as a designation as to w hether the case w as likely to be “actionable” or not or w hether the account had
been paid in full or underpaid).
Custom MDX scripts w ere used to create calculated measures and data-driven formatting. SSAS “actions” w ere used to
enable drill-through. For example, through a right-click, it w as possible to drill dow n to the account level to retrieve the
accounts included in the clicked-on cell along w ith comprehensive case descriptions that included facility, payer, financial
payment variances, and lengths-of-stay. End-user reporting w as accomplished via Excel pivot tables making use of an
Analysis Services data source. Ad hoc query of the data mart or staging tables to support analytical projects w as performed
using SSMS and for special studies for the group vice president.
OLAP World, Inc. with Microsoft BI Tools - June 2009 – June 2011
This w orktook place from June 2009 to the June 2011. These consulting projects sometimes overlapped and had
occasional gaps making it cumbersome to give exact time frames. They are presented in the order in w hich they w ere
started, w ith the most recent first.
Process Optimization
Development of an Analysis Services database to support near-real time analytics for manufacturing processes. This
database w as at the heart of a cloud-based predictive analytics product being developed by a Pittsburgh technology
company. The technology focus of my w orkw as SSAS although the project included some SQL development as w ell.
This w orkentailed the design of the cubes: designing the measures, dimensions, and hierarchies. I w rote MDX scripts to
effect custom aggregation methods, convert betw een different types of units (e.g., converting numbers of units produced to
equivalent time), and generate summary calculations (e.g., percentages and averages).
The objects I developed supported standard tabular reports, drillable bar charts, Pareto diagrams, and pie charts. The
product under development primarily used Adobe Flex to render the user interface, but I did do some development of charts
and drill-through actions for SSRS as w ell.
My w orksupplied the data behind a range of analyses offered w ithin the application including: dow ntime reason analysis,
operating equipment efficiency (OEE), real time process monitoring, and financial reporting. Dimensions I developed
included plant/production line, equipment, product, dow ntime reasons, OEE time usage categories, and time.
Retail Sales Analytics using OLAP and Datamining
This project entailed SSAS OLAP data cubes and SSAS data mining structures w ith SQL Server 2008. In addition to
softw are development, the assignment relied on my abilities as a data scientist/statistician to create meaningful analytical
models. For both the OLAP and data mining efforts, I designed the relational database that provided source data to the
SSAS database The SSAS OLAP database w as used for slice-and-dice reporting and analysis. It consisted of four
dimensions (store, customer, item, day) each w ith its ow n multilevel hierarchy. I created MDX scripts to create calculated
measures such as percent of parent and year-to-date, quarter-to-date, and month-to-date figures. The data w as accessed
by the end user via Excel pivot tables. Some of this effort supported an initiative to retire the use of Congos and take full
advantage of the functionality that w as already included in the SQL Server license cost. I created reports that matched those
familiar to the Cognos end users.
For the data mining segment, I developed a relational database to provide the data sources. This database consisted of a
“case table” that contained a unique identifier for each case (in this application, a shopping cart of items) and a “nested
table” that provided the list of items contained in the shopping cart. In addition to identifying each case, the case table
contained attributes of the case (e.g., store type or customer demographics), and the nested table contained attributes of the
items (e.g., product category or packaging). This w as so that an analysis could be conducted in terms of the factors (e.g.,
customer demographics or product characteristics) that w ere believed to be the business drivers rather than the detail data
(i.e., specific items purchased) that w as too specific to reveal meaningful patterns and w hich w ould drive a need for very
large training sets.
Next, I created mining structures for Microsoft Association Rules (in SSAS) to create an analytical environment that w ould
be used to quantify the kinds of products that tend to be bought together (an analysis know n as market basket analysis).
This design step entailed assigning prediction fields and setting algorithm parameters. Once configured, the model is run to
score sets of cases. The results produced are perused by the user w ith one of the three applicable mining model view ers.
Output consisted of rules, item sets, and dependency netw orks.
Various metrics w ere generated to ensure results that w ere valid and meaningful: assertion of information content in the
attributes (predictions changed w hen values of input attributes changed), predictive pow er (gain in information by using
predictors w as big enough to make the relationships discovered interesting), and the results had statistical validity (the
sample size w as large enough so that the results w ere not merely a reflection of natural random variation). These metrics
w ere explained to the client and documented in a w ritten report.

4. 4
SetFocus Mentoring and Training Program
To make the transition from the Oracle’s business intelligence platform to Microsoft’s, I participated in SetFocuses Business
Intelligence Masters program. This is a hands-on training program w ith mentorship by some of best in the business. This
w as a full-time, intensive thirteen w eekprogram of training and hands-on projects engaging each of the components of the
Microsoft BI technology suite including SSIS, SSRS, SSAS and Performance Point services. Accomplishments w er e:
 Implemented numerous modules as part of an end-to-end implementation of business intelligence solutions
including data mart design, ETL, designing and loading OLAP cubes, MDX and SQL queries, reports using SQL
Server Reporting Services (SSRS), SharePoint, Performance Point, and Excel pivot tables.
 Developed ETL system for populating a star schema data mart. SQL Server Integration Services (SSIS)
transformation modules w ere used to create packages for data cleansing, loading, package execution automation,
error logging, create database maintenance plans, and send automated emails.
 Created data cubes, dimensions and KPIs using SQL Server Analysis Services. System included hierarchical and
custom-grouping aggregations as w ell as MDX queries to implement calculated measures.
 Developed detail and summary reports as w ell as line and pie charts, trend analysis reports, and sub-reports
according to business requirements using SSRS.
 Implemented business intelligence dashboards using MOSS 2007 Report Center and Excel Services. Produced
different summary results based on user view and role membership.
 Created score cards w ith executive summary dashboards using MS Office Performance Point Server 2007
Dashboard Designer that display performance monitoring measures for sales, sales trends, and product return
from a SQL Server 2005 OLAP data source and deployed them to SharePoint sites. The dashboards also
contained drill-dow n capabilities to view sales by category and customers through summary reports as w ellas
sales trend charts.
 Key product areas utilized also include data modeling, SSRS report subscriptions and data driven subscriptions,
SSAS analysis services processing tasks, cube processing options and roles
OLAP World, Inc. with Oracle BI Tools April 1998–June 2009
Below is a highly summarized account of more than 10 years of consulting w orkw ith Oracle OLAP. Tools used included
Oracle Analytic Workspace Manager, BI Beans, Express, Express Objects, and Financial Analyzer (OFA).
 Redesigned large Oracle OLAP marketing analysis system w ith more than a terabyte of data. Activities included
interfacing w ith analysts to gather requirements, formulating system enhancements, designing the data
w arehouse, defining the cube loading process, and extensive OLAP DML programming to pre-calculate advanced
analytics.
 Designed a system to analyze call set-up data for roaming in large cellular telephone netw orks. My primary role
w as OLAP database developer, and I w rote the majority of the OLAP code. This system performed an incremental
load several times hourly importing millions of records, 24/7. This system is the heart of a commercially available
system offered by a major telecom company. (ViseWise)
 Assisted in the development and data loading of an Oracle Financial Analyzer system. Activities included
developing the load procedure, resolving data discrepancies, and developing an incremental update process.
 Designed and developed an executive information system (EIS) for sales tracking and business analysis at a major
bank. Work included both database and user interface design.
 Developed a feature-rich system for analyzing manufacturing cost data. The system created, displayed, and
costed bills of materials (BOMs). It could separate material and transformation costs and compare data from
different suppliers. The end user could sw itch back and forth betw een flat and hierarchical displays of BOM data.
 Developed a methodology for setting the values of numerical parameters used in the execution of an advanced
data mining algorithm developed to monitor netw orktraffic and signal alerts w hen anomalies occurred. SAS JMP
w as used for data analysis.

5. 5
 Assisted in requirements gathering and end-user training for a pharmaceutical clinical trials analysis system using
OFA.
 Developed a sales analysis system and, a few years later, a media usage analytics system for a major beverage
company.
 Designed and taught a one-w eek training course in OLAP programming for a client company.
 Taught data w arehousing and OLAP database design for Oracle Education.
 Assisted in the preparation of data forms for an Oracle Hyperion Planning application.
 Author of 300+ page book capturing my specialized Oracle OLAP expertise, The Multidimensional Data Modeling
Toolkit: Making Your Applications Smart with Oracle OLAP, ISBN 978-0981775302
Syniverse Technologies, Inc. (formerly Verizon, GTE) February 1992–March1998
Senior Manager, Business Intelligence
Entry into decision support/business intelligence w orkafter nearly a decade as an aerospace engineer doing signal
processing, artificial intelligence, and expert systems w ork. Experience at Syniverse included four day training in
dimensional data design taught personally by Ralph Kimball in a small in-house class and extensive mentorship in
multidimensional database design from senior Information Resources, Inc. consultant.
 Provided strategic direction in the development of decision support systems using Oracle OLAP, data mining, and
artificial intelligence technology in my role as head of the Business Operations Research Group.
 Developed methodologies for analyzing customer migrations, analyzing advertisers purchasing patterns, and
developing a product price elasticity model.
 Developed system for predicting customer service cancellations, assessing customer financial value, and creating
a risk-based model, as an integral part of a customer relationship management system for the mobile phone
industry.
 Developed algorithms and softw are for detecting fraudulent cellular telephone activity.