COVID Data Challenges - Updated 2021
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The document outlines the challenges and inefficiencies in data governance and quality during the COVID-19 pandemic, particularly in the aviation sector's response involving major airports like JFK and Newark. It highlights the lack of standardization and trust in data reported by various states, complicating public health decision-making. The proposal includes implementing robust data governance strategies and advanced analytics to improve data quality and enhance public health outcomes.
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"In reviewing the analysis obtained by NPR, Panchadsaram says the local
and hospital-level data HHS is collecting would be very useful to
researchers and health leaders. "That stuff isn't easy to find at a national
level," he says. "There's no one place [publicly] you can go to get all that
data."
October 30, 2020](https://image.slidesharecdn.com/datamasters-covid-19-210206024157/85/COVID-Data-Challenges-Updated-2021-11-320.jpg)
COVID Data Challenges - Updated 2021
- 1. COVID-19 - Building Trust in Data To Save Lives
- 2. Paul Balas 25+ Years Experience Leading Digital Transformations Multiple MDM Implementations, Data Governance, and Data Warehouse Initiatives q Digital Transformation Consultant q IT Executive q Enterprise Architect q Developer paul.balas@revisioninc.com
- 3. THE REVISION ANALYTICS DIFFERENCE Founded in 1998, REVISION is a management consulting firm that marries domain and technology expertise to achieve amazing results for our clients through digital transformations that deliver value. § REVISION Senior Consultants Depth Of Experience – “We’ve Walked The Walk.” § 20+ Years C-level Advisory Roles In Performance Excellence § 20+ Years IT Operations & Security Experience § Airport and Transportation Experience § Culture & Operations State, Local & Federal § DoD Secret & Top Secret Cleared Staff Data Strategy Data Ops Data Governance MDM Advanced Analytics Assessment & Implementation
- 4. 4 REVISION – LEADING IN RESPONSE TO THE COVID PANDEMIC We built a system to enable John F. Kennedy International (JFK), Newark Liberty International (EWR) and LaGuardia Airport (LGA) to aid in response to the COVID pandemic “… The Aviation Strategy Unit, created a custom dashboard that estimates and predicts how many passengers will arrive from states with travel advisories. The new tool combines API data and information within the Port Authority’s data warehouse with updates from the COVID Tracking Project…”
- 5. This is a presentation to uncover the systemic data challenges in our Government’s response to The COVID Pandemic And a proposal to improve data quality and build trust
- 6. Agenda 6 What has happened since we started this journey? Why is the Data Wrong? Why the New HHS System Isn’t Working How To Fix It - a POC
- 8. Dr. Deborah Birx said “there is nothing from the CDC that I can trust” in a White House coronavirus task force meeting 8 May 10, 2020
- 9. TRUST I’m going to show you four news articles that speak to four different types of challenges with achieving good data governance and delivering trusted data. Do you notice any governance challenges we encounter in our own companies? 9
- 10. 10 Medical examiner offices had struggled to keep up with a recent spike in COVID-19 deaths, with more than 2,300 deaths reported statewide in just the past two weeks, according to the Florida Department of Health.
- 11. 11 "In reviewing the analysis obtained by NPR, Panchadsaram says the local and hospital-level data HHS is collecting would be very useful to researchers and health leaders. "That stuff isn't easy to find at a national level," he says. "There's no one place [publicly] you can go to get all that data." October 30, 2020
- 12. 12 “During a news conference Saturday morning, Perna explained that he had not taken into consideration the time it would take for completed vaccine to go through the full Food and Drug Administration quality control process, which can take 48 hours.” Dec. 19, 2020
- 13. 13 ”I saw the President presenting graphs that I never made. I know that someone out there or someone inside was creating a parallel set of data and graphics that were shown to the President.” January 25, 2021
- 14. Increasing volumes and process inefficiencies Data silos and protectionism Poor data governance and unclear definitions Dueling dashboards 14 Common Challenges in Data Governance
- 15. 1: What are your top Data Governance Challenges? 15 a) Increasing Volumes b) Data Silos c) Unclear Definitions d) Dueling Dashboards e) Lack of Executive Alignment
- 16. Why is COVID-19 Data Wrong? No published standards on how to collect and present the information The CDC had an aging system which wasn’t agile to change The virus is spreading quickly – huge volumes of new data to process We weren’t capturing needed data 16
- 17. How Do We Need to Use The Data? PPE, Hospital Capacity, Testing Supplies Ensure we have enough doctors, nurses, and other healthcare professionals Issuing protective orders to stop the spread of the virus (Shelter-in-place, Social Distancing, Shuttering Business) 17
- 18. We’ve been asking ourselves, ”Could we have saved more lives?” Without trusted data, no decision will be driven with conviction 18
- 19. 19
- 20. The Data is Inconsistent - States Are Reporting to Differing Standards https://www.beckershospitalreview.com/data-analytics/states-are-reporting-inconsistent-incomplete-covid-19-data-analysis- finds.html#:~:text=The%20federal%20government%20has%20left,former%20CDC%20Director%20Tom%20Friedan. 20
- 21. The Data Isn’t Standardized - How Many Tests Have Been Given? 21
- 22. 22 There are literally 1,000’s of examples of incorrectly reported data
- 23. The HHS System isn’t solving the problem, and It’s a Big Problem CDC has 1,200 Users and about 950 State CDC Partners There are over 6,000 Hospitals in the US About 2,000 Hospitals provide Covid data directly to HH Protect TeleTracking provides data from about 1,100 additional Hospitals 23
- 24. HHS Challenges ● “Use of data across programs… remains a challenge.” ● “Data are often housed in … data silos” 24
- 25. 25 NNDSS is focused on speed, data format standardization, and validation They need to focus on improving Data Governance and Collaboration
- 26. 26 TeleTracking, is just a data entry app that is driven by procedural rules The main problem in achieving Data Quality is PEOPLE
- 27. 27 Definitions translate into procedural business logic to ensure quality data. But does this approach work? The Fallacy of Standardized Data Entry Solutions
- 28. 28 If you’re one of those managers who believes that a standardized data entry screen fixes your organizations data quality problem... You haven’t done much data analysis (go speak with your data team)
- 29. A Novel Way to Solve The Data Governance Challenge 29
- 30. Imagine This is Your Problem to Solve You are the CIO of the CDC tasked to improve our Nation’s ability to better manage this and the next pandemic through the use of data. Your first goal is to understand the key issues in the current system (“as-is”), and develop a roadmap to address them. The stakes are high. 30
- 31. Architectural Principles Build trust in the data Identify which issues to fix first The system should be agile to change Efficient collaboration 31
- 32. Two Paths Standardize data entry systems Identify and address systemic governance challenges 32
- 33. Identify and Address Systemic Governance Challenges Master Data Management Taxonomy Management Data Quality Cloud Data Integration Data Transformation Orchestration Natural Language / Feature Extraction Data Lake Compute and Storage
- 34. Understand Systemic Data Challenges You choose to look at issues around ‘Testing’ as you believe you can get immediate benefits for public health if you can build confidence in the test data What problems are states having in processing Test Data? Is Test Data being reported consistently, accurately? 34
- 35. Compare 3 Websites Standards on Test Data 35
- 36. Completely Different Standards (For The Same Data) 36
- 37. Three Weeks Later 37 Integrate and process the self-reported data quality issues from your three test sites Create data quality taxonomies to report on the issue types Extract entities from the quality issues and classify them using our taxonomies
- 39. Virtual Standardization Meetings You use your findings to review issues and publish recommendations to conform to new standards for each of the data providers. You use the platform to manage quality on an on-going basis. You provide the DataOps platform to data providers to self-manage their quality issues. 39
- 40. Can We Do More? You can now see the systemic data quality issues and communicate with stakeholders effectively to get alignment. Next, classify news by Public Influencers, Events, and Locations because you want to see if what our leaders say has an impact on testing and infection rates. 40
- 41. Public Influencers - New Jersey
- 42. Public Influencers - New York
- 43. 2. Do you have confidence in the COVID data? 43 a. High Confidence b. Neutral c. Low Confidence
- 44. How Was It Built? 44
- 45. Google Cloud Platform InfoWorks Logical workflow Internet data sources Data orchestration Tableau TAMR Big Query VM instances Google cloud storage Natural language Python Twitter API News API State Health Department Web Pages JHU Github
- 46. I Had To Extract Meaning From Text
- 48. Data Experts - Spend More Time Analyzing/Strategizing Before: Experts spend too much Time manually fixing data Today: ML can do 80% of data mastering lift... …. Enabling experts to put final touches on the last 20%. 48
- 49. The TAMR Agile Approach to Data Mastering Mastered data OLD WAY Rules-based Source data Mastered data Time Quality Months to years 60%–80% Accuracy Modify rules, create exceptions Months 1–4 Months 5–12+ Iterate Machine-driven NEW WAY Days to weeks 90%+ accuracy Source data Weeks 1–12 Iterate with human- guided machine learning Identify developers Get business input Write rules Review with business Unified data Rules
- 50. Taxonomies: Before vs. After Tamr TAMR enabled us to create standardized taxonomies that can be managed by a networked group of hospitals, labs, health officials. These taxonomies are critical to having good quality and conformed data across a widely distributed data network. There is an efficient mechanism for building consensus across experts at the same time as fixing the data. There is no solution like it in the market.
- 51. MDM - People Mastering
- 53. Mastering People: 530K to 9K in a Few Days Using TAMR, I was able to take a corpus of over 500k entity records identified by Google Natural Language across 60,000 news articles, hundreds of web pages, thousands of tweets in a few weeks, reducing it to a about 10k Golden Master People Records I estimate the system can be maintained in one to two hours a week at scale, decreasing to minutes a week as the model learns 53
- 54. Events - Before and After TAMR
- 56. 7 TAMR Projects Built in a Few Weeks
- 57. Event Classification Events are hard to identify in text I first created a taxonomy of events tagged by Google Natural Language to limit those of interest. I loaded those into TAMR And then classified them in a few hours 57
- 58. 3: Which of these capabilities are you most interested in learning more about? 58 Agile Data Governance Google Cloud Platform Google Natural Language Processing API TAMR InfoWorks DataOps
- 59. Conclusion The COVID Pandemic data challenges are a macro-view of the same challenges we all face in our own companies as we use data as information to improve outcomes The hardest problem to solve is injecting Subject Matter Expertise into a flexible data processing system that can help us align SME perspectives, and respond to changing needs with agility 59
- 60. The Team! 60 Mingo Sanchez Keith Worfolk Elizabeth Michel Katie Everett Kamal Maheshwari