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Qiagram

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Qiagram

Qiagram is a collaborative visual data exploration environment that enables investigator-initiated, hypothesis-driven data exploration, allowing business users as well as IT professionals to easily ask complex questions against complex data sets.

Qiagram is a collaborative visual data exploration environment that enables investigator-initiated, hypothesis-driven data exploration, allowing business users as well as IT professionals to easily ask complex questions against complex data sets.

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Qiagram

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

Notas do Editor

  • Data is the lifeblood of the scientific process. Data is used to prove or disprove hypotheses and to guide decision-making. Its lifespan starts with data generation and ends with achieving concrete insights. There is a step in the middle, however, that is often overlooked: data exploration - the stage of hypothesis generation and decision-making during which the researcher takes a high-level, exploratory view of the available data. Data acquisition --&gt; Data exploration --&gt; Data analysis This data exploration stage is often used to determine the availability of &amp;quot;interesting data&amp;quot; that can be used in further analysis.
  • By removing the barrier of specialized database languages, we enable researchers to directly investigate their data by simply drawing a diagram. This frees up IT to better focus on infrastructure and support operations.

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