SlideShare ist ein Scribd-Unternehmen logo
1 von 18
Downloaden Sie, um offline zu lesen
Business Intelligence Software
    For Inquisitive Minds
Typical Scientific Data Workflow




           Acquisition                    Exploration                 Analysis
                                                                         Analysis


1. Data Acquisition                                               3.Data Analysis
•   Excel, Access, homebrews                                      •    SAS, R
•   (Electronic?!) forms, notes                                   •    Spotfire
•   LIMS & instruments output                                     •    Tableau
•   Labmatrix forms & records                                     •    Statisticians
                                      2. Data Exploration
•   Other enterprise resources                                    •    etc…
•   etc…
                                  •   Easy, graphical queries
                                  •   ETL & data cleaning tools
                                  •   Formulas & calculations
                                  •   Visualize charts & graphs

                                                                        2
Once you have:


   1. Collected… ()
   2. Standardized… (Not yet? Use built-in data cleaning tools)
   3. Normalized… (Not yet? Use built-in formula calculation tools)


…some, or all of your project data,


                       how do you best make use of them?
The Problem: subject matter experts having to go through a
(limited) pipeline of IT expertise to answer complex questions
about their domain-specific data.



     DB        DB            IT


DB        DB

                        Programmers
     DB




Piles of project data                 Domain experts with many
from various sources                  complex data questions
Clashing of Expertise

Domain Experts / Researchers                       IT / Programmers

                     DNA!                                        Primary key!
                     Biomarkers!                                 Data type!
                     Transcription!                              Object model!




• Can’t access data by myself             • Too many throw-away or one-off
• My data inquiries are taking              project requests
  too long to process                     • They keep changing their minds
• I have many more inquiries                about how to cut the data
  but afraid to ask                       • Nothing is standardized
• IT misinterprets my inquiries           • No prioritization: using brute
• Changed my mind about                     force approach to grind through
  inquiries in process already              all data instead of critical path
• Data result doesn’t look right          • Could use more domain
• Didn’t IT know I need to relate           expertise when processing piles
  A with B in this specific way?            of complex data
• …                                       • …
1. Common workspace
The Solution:
                 2. Shared “language”

                                            IT / Programmers


       DB        DB
                                        All raw & prepared data
                                        can be centralized here.
                                        The data processes and
                                        data queries are shown
  DB        DB        centralize        graphically, so they are
                                        easily understood by both
                                        IT and domain experts.


       DB




                                            Domain Experts
Symbiotic Expertise

Domain Experts / Researchers                      IT / Programmers




• Can explore data by myself           • Centralized environment to
• Get results from complex questions     prepare and present data sets
  in minutes instead of weeks          • Built-in import, data cleaning,
• Gain actionable insights even from     standardization & ontology tools
  rough or messy data (within          • Centrally manage data access and
  institutional guidelines)              audit all changes and activities
• Visually share interesting data      • Prepare and fix data issues with
  queries with colleagues                guided priority from end-users
• Visually share data workflows and    • Develop & reuse code for projects
  issues with IT personnel               via programmatic interface
• Help IT identify data issues and     • Self-serve model allows IT to work
  prioritize fixes                       on other things
• …                                    • …
Symbiotic Expertise = smarter & less IT efforts,
           faster & better data access for domain experts




                       SEA OF DATA



  With the ability to explore data easily, domain experts can quickly
identify relevant data, gain actionable insights, and better drive efforts
How does            work?
Step 1. Drag & drop a set of data     Step 3. Expand the scope and detail of
on top of another.                    your question with additional data sets,
                                      filter conditions, calculations, or other
                                      kinds of transformations as necessary.
       Patients             Meds



                                                                           Combine




Step 2. Data sets are intelligently                                Pivot
and automatically connected to                            Result                Result
each other.                                               Set 1                 Set 2


                                                 Filter
                           Patients
      Patients
                           on Meds
                                      Each “node” is live, so you can retrieve
                  Filter              and review the results from each step
       Meds
                                      as you build a complex query.
                                      You are now trained in using Qiagram.
Current Client Application Areas:
• Clinical & Translational Research
• Biomarker Discovery
• Healthcare Data Utilization/Consumption
• In silico Clinical Trial Feasibility
• Consortium Collaborations
• Cheminformatics Research
• …
Case Study: Common Problem in Translational Research
                                                       Cryptic DB you’ll never
                                                        have easy access to
Qiagram: our award-winning “draw-your-question”
The Solution   interface - SQL or programming training NOT required!




                          Just drag & drop, and run your query!
Qiagram: a visual data query tool
Example 1: “reporting & operational statistics” data query
Qiagram: a visual data query tool
Example 2: hypothesis-driven data exploration
Qiagram: a better BI tool for translational research (TR)

                                Traditional BI              TR Informatics
Budget                     $$$                         $
Purpose                    Operational                 Exploratory
Questions                  Simple                      Complex
Data Cleaning &            Precursor to                Parallel to meaningful
Standardization            meaningful queries          queries
Data Sources               Well understood             Ever-changing
Data organization          Hierarchical                Ad hoc
Perspective                Static                      Individualized
Collaboration              Limited                     Extensive
... the exploratory & discovery nature of TR requires tools specifically designed
for TR endeavors, instead of shoe-horning traditional BI technologies.

                                                                                    15
Many ways to get data into the system:
                                               Large Flat             DB       DB
  An enterprise, scalable solution that           Files      DB
                                                                      Federation
  communicates with all data sources                                    Engine



                                                       SQL Scripts
      DB                       tab-delimited
                                    text
                    Data          SOAP
                                                  ETL
                 Transformer                   Framework

                               Web Forms,
                                Data Files
                                  HTTP           WEB UI
       .TXT
                                                                       Qiagram
                                                                       Core API
                                Java Objects
                 Enterprise        RMI           RMI API
                  System
      DB                                                              Qiagram
                                   XML
                 Enterprise       SOAP
                                               Custom Web
                                                                     Framework
                  System                         Services
KEY FRAMEWORK FEATURES

Centralize Data: web-accessible system enables immediate data staging, multi-
site collaboration, data/site management, data QA/review/reports, and instant data
querying results; scalable enterprise deployment

Clean & Standardize: improve data quality via built-in data cleaning and
standardization tools; establish or import vocabularies & standardized data models

Enforce User Roles & Permissions: flexible configurations of how
different users/groups/TAs can access specific data sets in collaborative settings

Maintain Security & Compliance: transmit data securely, facilitate
regulatory compliance, and track all data changes via detailed audit logs in this
HIPAA/PHI-compliant system; customizable data backup & recovery plans

Integration & Interoperability: multiple interfaces to communicate with
other data systems in your IT infrastructure; vocabulary & ontology definitions
Qiagram: accolades




     Proprietary & Confidential

Weitere ähnliche Inhalte

Andere mochten auch

Translational Research Intelligence - Beyond Traditional Bi
Translational Research Intelligence - Beyond Traditional BiTranslational Research Intelligence - Beyond Traditional Bi
Translational Research Intelligence - Beyond Traditional Bishc66columbia
 
How To Make A Facebook Business (Fan) Page
How To Make A Facebook Business (Fan) PageHow To Make A Facebook Business (Fan) Page
How To Make A Facebook Business (Fan) PageMovement Mortgage
 
алтанзул цахим тест
алтанзул цахим тесталтанзул цахим тест
алтанзул цахим тестmarlaashka
 

Andere mochten auch (9)

Brochure 2013
Brochure 2013Brochure 2013
Brochure 2013
 
Company profile
Company profileCompany profile
Company profile
 
зөв
зөвзөв
зөв
 
Translational Research Intelligence - Beyond Traditional Bi
Translational Research Intelligence - Beyond Traditional BiTranslational Research Intelligence - Beyond Traditional Bi
Translational Research Intelligence - Beyond Traditional Bi
 
A Gray Legacy: Genesis
A Gray Legacy: GenesisA Gray Legacy: Genesis
A Gray Legacy: Genesis
 
3 р анги
3 р анги3 р анги
3 р анги
 
How To Make A Facebook Business (Fan) Page
How To Make A Facebook Business (Fan) PageHow To Make A Facebook Business (Fan) Page
How To Make A Facebook Business (Fan) Page
 
Labmatrix
LabmatrixLabmatrix
Labmatrix
 
алтанзул цахим тест
алтанзул цахим тесталтанзул цахим тест
алтанзул цахим тест
 

Ähnlich wie Qiagram

Qiagram
QiagramQiagram
Qiagramjwppz
 
Big Data Analytics: Applications and Opportunities in On-line Predictive Mode...
Big Data Analytics: Applications and Opportunities in On-line Predictive Mode...Big Data Analytics: Applications and Opportunities in On-line Predictive Mode...
Big Data Analytics: Applications and Opportunities in On-line Predictive Mode...BigMine
 
Software architecture & design patterns for MS CRM Developers
Software architecture & design patterns for MS CRM  Developers Software architecture & design patterns for MS CRM  Developers
Software architecture & design patterns for MS CRM Developers sebedatalabs
 
THE 3V's OF BIG DATA: VARIETY, VELOCITY, AND VOLUME from Structure:Data 2012
THE 3V's OF BIG DATA: VARIETY, VELOCITY, AND VOLUME from Structure:Data 2012THE 3V's OF BIG DATA: VARIETY, VELOCITY, AND VOLUME from Structure:Data 2012
THE 3V's OF BIG DATA: VARIETY, VELOCITY, AND VOLUME from Structure:Data 2012Gigaom
 
THE 3V’S OF BIG DATA: VARIETY, VELOCITY, and VOLUME
THE 3V’S OF BIG DATA: VARIETY, VELOCITY, and VOLUMETHE 3V’S OF BIG DATA: VARIETY, VELOCITY, and VOLUME
THE 3V’S OF BIG DATA: VARIETY, VELOCITY, and VOLUMEGigaom
 
Enabling Flexible Governance for All Data Sources
Enabling Flexible Governance for All Data SourcesEnabling Flexible Governance for All Data Sources
Enabling Flexible Governance for All Data SourcesInside Analysis
 
Denodo DataFest 2016: Comparing and Contrasting Data Virtualization With Data...
Denodo DataFest 2016: Comparing and Contrasting Data Virtualization With Data...Denodo DataFest 2016: Comparing and Contrasting Data Virtualization With Data...
Denodo DataFest 2016: Comparing and Contrasting Data Virtualization With Data...Denodo
 
Introduction to Data Science.pptx
Introduction to Data Science.pptxIntroduction to Data Science.pptx
Introduction to Data Science.pptxAnusuya123
 
Data mining - GDi Techno Solutions
Data mining - GDi Techno SolutionsData mining - GDi Techno Solutions
Data mining - GDi Techno SolutionsGDi Techno Solutions
 
Li101 enterprise class_systems
Li101 enterprise class_systemsLi101 enterprise class_systems
Li101 enterprise class_systemsjleecbd
 
Agile Data Rationalization for Operational Intelligence
Agile Data Rationalization for Operational IntelligenceAgile Data Rationalization for Operational Intelligence
Agile Data Rationalization for Operational IntelligenceInside Analysis
 
Pragmatics Driven Issues in Data and Process Integrity in Enterprises
Pragmatics Driven Issues in Data and Process Integrity in EnterprisesPragmatics Driven Issues in Data and Process Integrity in Enterprises
Pragmatics Driven Issues in Data and Process Integrity in EnterprisesAmit Sheth
 
Doing Analytics Right - Building the Analytics Environment
Doing Analytics Right - Building the Analytics EnvironmentDoing Analytics Right - Building the Analytics Environment
Doing Analytics Right - Building the Analytics EnvironmentTasktop
 
Zakipoint Introduction
Zakipoint IntroductionZakipoint Introduction
Zakipoint Introductionrameshkbudhani
 
Labmatrix Slides 2011 05
Labmatrix Slides 2011 05Labmatrix Slides 2011 05
Labmatrix Slides 2011 05bhughes26
 
Introducing the Big Data Ecosystem with Caserta Concepts & Talend
Introducing the Big Data Ecosystem with Caserta Concepts & TalendIntroducing the Big Data Ecosystem with Caserta Concepts & Talend
Introducing the Big Data Ecosystem with Caserta Concepts & TalendCaserta
 
Self Service Analytics enabled by Data Virtualization from Denodo
Self Service Analytics enabled by Data Virtualization from DenodoSelf Service Analytics enabled by Data Virtualization from Denodo
Self Service Analytics enabled by Data Virtualization from DenodoDenodo
 
Research Data Management: What is it and why is the Library & Archives Servic...
Research Data Management: What is it and why is the Library & Archives Servic...Research Data Management: What is it and why is the Library & Archives Servic...
Research Data Management: What is it and why is the Library & Archives Servic...GarethKnight
 
Hadoop meets Agile! - An Agile Big Data Model
Hadoop meets Agile! - An Agile Big Data ModelHadoop meets Agile! - An Agile Big Data Model
Hadoop meets Agile! - An Agile Big Data ModelUwe Printz
 

Ähnlich wie Qiagram (20)

Qiagram
QiagramQiagram
Qiagram
 
Big Data Analytics: Applications and Opportunities in On-line Predictive Mode...
Big Data Analytics: Applications and Opportunities in On-line Predictive Mode...Big Data Analytics: Applications and Opportunities in On-line Predictive Mode...
Big Data Analytics: Applications and Opportunities in On-line Predictive Mode...
 
Software architecture & design patterns for MS CRM Developers
Software architecture & design patterns for MS CRM  Developers Software architecture & design patterns for MS CRM  Developers
Software architecture & design patterns for MS CRM Developers
 
Big data ppt
Big data pptBig data ppt
Big data ppt
 
THE 3V's OF BIG DATA: VARIETY, VELOCITY, AND VOLUME from Structure:Data 2012
THE 3V's OF BIG DATA: VARIETY, VELOCITY, AND VOLUME from Structure:Data 2012THE 3V's OF BIG DATA: VARIETY, VELOCITY, AND VOLUME from Structure:Data 2012
THE 3V's OF BIG DATA: VARIETY, VELOCITY, AND VOLUME from Structure:Data 2012
 
THE 3V’S OF BIG DATA: VARIETY, VELOCITY, and VOLUME
THE 3V’S OF BIG DATA: VARIETY, VELOCITY, and VOLUMETHE 3V’S OF BIG DATA: VARIETY, VELOCITY, and VOLUME
THE 3V’S OF BIG DATA: VARIETY, VELOCITY, and VOLUME
 
Enabling Flexible Governance for All Data Sources
Enabling Flexible Governance for All Data SourcesEnabling Flexible Governance for All Data Sources
Enabling Flexible Governance for All Data Sources
 
Denodo DataFest 2016: Comparing and Contrasting Data Virtualization With Data...
Denodo DataFest 2016: Comparing and Contrasting Data Virtualization With Data...Denodo DataFest 2016: Comparing and Contrasting Data Virtualization With Data...
Denodo DataFest 2016: Comparing and Contrasting Data Virtualization With Data...
 
Introduction to Data Science.pptx
Introduction to Data Science.pptxIntroduction to Data Science.pptx
Introduction to Data Science.pptx
 
Data mining - GDi Techno Solutions
Data mining - GDi Techno SolutionsData mining - GDi Techno Solutions
Data mining - GDi Techno Solutions
 
Li101 enterprise class_systems
Li101 enterprise class_systemsLi101 enterprise class_systems
Li101 enterprise class_systems
 
Agile Data Rationalization for Operational Intelligence
Agile Data Rationalization for Operational IntelligenceAgile Data Rationalization for Operational Intelligence
Agile Data Rationalization for Operational Intelligence
 
Pragmatics Driven Issues in Data and Process Integrity in Enterprises
Pragmatics Driven Issues in Data and Process Integrity in EnterprisesPragmatics Driven Issues in Data and Process Integrity in Enterprises
Pragmatics Driven Issues in Data and Process Integrity in Enterprises
 
Doing Analytics Right - Building the Analytics Environment
Doing Analytics Right - Building the Analytics EnvironmentDoing Analytics Right - Building the Analytics Environment
Doing Analytics Right - Building the Analytics Environment
 
Zakipoint Introduction
Zakipoint IntroductionZakipoint Introduction
Zakipoint Introduction
 
Labmatrix Slides 2011 05
Labmatrix Slides 2011 05Labmatrix Slides 2011 05
Labmatrix Slides 2011 05
 
Introducing the Big Data Ecosystem with Caserta Concepts & Talend
Introducing the Big Data Ecosystem with Caserta Concepts & TalendIntroducing the Big Data Ecosystem with Caserta Concepts & Talend
Introducing the Big Data Ecosystem with Caserta Concepts & Talend
 
Self Service Analytics enabled by Data Virtualization from Denodo
Self Service Analytics enabled by Data Virtualization from DenodoSelf Service Analytics enabled by Data Virtualization from Denodo
Self Service Analytics enabled by Data Virtualization from Denodo
 
Research Data Management: What is it and why is the Library & Archives Servic...
Research Data Management: What is it and why is the Library & Archives Servic...Research Data Management: What is it and why is the Library & Archives Servic...
Research Data Management: What is it and why is the Library & Archives Servic...
 
Hadoop meets Agile! - An Agile Big Data Model
Hadoop meets Agile! - An Agile Big Data ModelHadoop meets Agile! - An Agile Big Data Model
Hadoop meets Agile! - An Agile Big Data Model
 

Qiagram

  • 1. Business Intelligence Software For Inquisitive Minds
  • 2. Typical Scientific Data Workflow Acquisition Exploration Analysis Analysis 1. Data Acquisition 3.Data Analysis • Excel, Access, homebrews • SAS, R • (Electronic?!) forms, notes • Spotfire • LIMS & instruments output • Tableau • Labmatrix forms & records • Statisticians 2. Data Exploration • Other enterprise resources • etc… • etc… • Easy, graphical queries • ETL & data cleaning tools • Formulas & calculations • Visualize charts & graphs 2
  • 3. Once you have: 1. Collected… () 2. Standardized… (Not yet? Use built-in data cleaning tools) 3. Normalized… (Not yet? Use built-in formula calculation tools) …some, or all of your project data, how do you best make use of them?
  • 4. The Problem: subject matter experts having to go through a (limited) pipeline of IT expertise to answer complex questions about their domain-specific data. DB DB IT DB DB Programmers DB Piles of project data Domain experts with many from various sources complex data questions
  • 5. Clashing of Expertise Domain Experts / Researchers IT / Programmers DNA! Primary key! Biomarkers! Data type! Transcription! Object model! • Can’t access data by myself • Too many throw-away or one-off • My data inquiries are taking project requests too long to process • They keep changing their minds • I have many more inquiries about how to cut the data but afraid to ask • Nothing is standardized • IT misinterprets my inquiries • No prioritization: using brute • Changed my mind about force approach to grind through inquiries in process already all data instead of critical path • Data result doesn’t look right • Could use more domain • Didn’t IT know I need to relate expertise when processing piles A with B in this specific way? of complex data • … • …
  • 6. 1. Common workspace The Solution: 2. Shared “language” IT / Programmers DB DB All raw & prepared data can be centralized here. The data processes and data queries are shown DB DB centralize graphically, so they are easily understood by both IT and domain experts. DB Domain Experts
  • 7. Symbiotic Expertise Domain Experts / Researchers IT / Programmers • Can explore data by myself • Centralized environment to • Get results from complex questions prepare and present data sets in minutes instead of weeks • Built-in import, data cleaning, • Gain actionable insights even from standardization & ontology tools rough or messy data (within • Centrally manage data access and institutional guidelines) audit all changes and activities • Visually share interesting data • Prepare and fix data issues with queries with colleagues guided priority from end-users • Visually share data workflows and • Develop & reuse code for projects issues with IT personnel via programmatic interface • Help IT identify data issues and • Self-serve model allows IT to work prioritize fixes on other things • … • …
  • 8. Symbiotic Expertise = smarter & less IT efforts, faster & better data access for domain experts SEA OF DATA With the ability to explore data easily, domain experts can quickly identify relevant data, gain actionable insights, and better drive efforts
  • 9. How does work? Step 1. Drag & drop a set of data Step 3. Expand the scope and detail of on top of another. your question with additional data sets, filter conditions, calculations, or other kinds of transformations as necessary. Patients Meds Combine Step 2. Data sets are intelligently Pivot and automatically connected to Result Result each other. Set 1 Set 2 Filter Patients Patients on Meds Each “node” is live, so you can retrieve Filter and review the results from each step Meds as you build a complex query. You are now trained in using Qiagram.
  • 10. Current Client Application Areas: • Clinical & Translational Research • Biomarker Discovery • Healthcare Data Utilization/Consumption • In silico Clinical Trial Feasibility • Consortium Collaborations • Cheminformatics Research • …
  • 11. Case Study: Common Problem in Translational Research Cryptic DB you’ll never have easy access to
  • 12. Qiagram: our award-winning “draw-your-question” The Solution interface - SQL or programming training NOT required! Just drag & drop, and run your query!
  • 13. Qiagram: a visual data query tool Example 1: “reporting & operational statistics” data query
  • 14. Qiagram: a visual data query tool Example 2: hypothesis-driven data exploration
  • 15. Qiagram: a better BI tool for translational research (TR) Traditional BI TR Informatics Budget $$$ $ Purpose Operational Exploratory Questions Simple Complex Data Cleaning & Precursor to Parallel to meaningful Standardization meaningful queries queries Data Sources Well understood Ever-changing Data organization Hierarchical Ad hoc Perspective Static Individualized Collaboration Limited Extensive ... the exploratory & discovery nature of TR requires tools specifically designed for TR endeavors, instead of shoe-horning traditional BI technologies. 15
  • 16. Many ways to get data into the system: Large Flat DB DB An enterprise, scalable solution that Files DB Federation communicates with all data sources Engine SQL Scripts DB tab-delimited text Data SOAP ETL Transformer Framework Web Forms, Data Files HTTP WEB UI .TXT Qiagram Core API Java Objects Enterprise RMI RMI API System DB Qiagram XML Enterprise SOAP Custom Web Framework System Services
  • 17. KEY FRAMEWORK FEATURES Centralize Data: web-accessible system enables immediate data staging, multi- site collaboration, data/site management, data QA/review/reports, and instant data querying results; scalable enterprise deployment Clean & Standardize: improve data quality via built-in data cleaning and standardization tools; establish or import vocabularies & standardized data models Enforce User Roles & Permissions: flexible configurations of how different users/groups/TAs can access specific data sets in collaborative settings Maintain Security & Compliance: transmit data securely, facilitate regulatory compliance, and track all data changes via detailed audit logs in this HIPAA/PHI-compliant system; customizable data backup & recovery plans Integration & Interoperability: multiple interfaces to communicate with other data systems in your IT infrastructure; vocabulary & ontology definitions
  • 18. Qiagram: accolades Proprietary & Confidential