You might expect this to be an easy question to answer. It turns out not to be. Exploring it reveals fundamental issues with how we collect and publish data. We take for granted the value of making data-based decisions and expect we can extend this practice to public policy — but that may not currently be possible.
Deaths would seem to be the easiest possible statistic to measure. Almost all countries record mortality. The United States has a relatively complex multilevel structure of governments, but even so, each state maintains a Department of Vital Statistics that records deaths, issues death certificates, and forwards statistics to the Federal government. The Center for Disease Control’s National Center for Health Statistics publicizes death numbers. But its most recent data brief, Number 355 issued in January 2020, only covers deaths through 2018.
Putting together a polished data set and results requires an average of 15 months. The completed data set has many dimensions useful for research: breakdown by age, cause of death, etc. This level of detail is unnecessary to answer basic questions, so the CDC has an early release program — but that requires submitting an application and institutional review, barriers that are inappropriate for answering basic questions, and still only makes available data 6 months after a year’s end. Since this is still insufficient, CDC offers State and National Provisional Counts. At the time of writing (May 17, 2020), the provisional data covers 2018 and up to March 2019 (14 months behind). And the historical archive covers up to 2015 (where is the data for 2016 and 2017)?
Right now, due to the intense scrutiny on the COVID-19 epidemic, we can actually find some better and more detailed (non-COVID) death reporting from February through May as part of COVID reports). But comparing that data with the prior period in 2019 remains out of reach.
Perhaps counting deaths in the United States is too difficult a problem. Let’s instead turn to a simpler problem — counting deaths in a single state. Since the state issues the death certificates, a State should, you might think, easily be able to answer how many deaths happened in a given timeframe, or at least how many death certificates it issued in that time — a sufficiently close proxy. I live in Texas so I consulted the Texas Department of Health and Human Services, whose Department of Vital Statistics maintains that data in easily accessible web records… for up to 2016.
Resigning myself to ask for the data in the old-fashioned way, I emailed the State Department of Vital Statistics, receiving this reply:
Good Afternoon, Mr. Rostcheck,
Thank you for your request regarding Texas mortality data. At this time we are unable to process your request due to limited resources and COVID19 response. We can either put your request on a waitlist or you are welcome to check back in with us at a later time. Additionally, the most recent year of finalized data we can provide counts by month for is 2017. The most recent year of non-finalized data we can provide these counts for is 2018.
Please let us know if you have any questions.
The Vital Events Data Management Team
Texas Department of State Health Services
Center for Health Statistics
[Note that although they note that the most recent data that Texas’s own authoritative center for health statistics can provide is through 2018, the CDC’s State and National Provisional Counts has Texas’ data through March 2019. Note also that Texas is actually one of the most efficiently run states in the United States.]
#statistics #government #data #data analysis #data analysis
If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.
If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.
In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.
#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition
The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.
This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.
As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).
This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.
#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management
The COVID-19 pandemic disrupted supply chains and brought economies around the world to a standstill. In turn, businesses need access to accurate, timely data more than ever before. As a result, the demand for data analytics is skyrocketing as businesses try to navigate an uncertain future. However, the sudden surge in demand comes with its own set of challenges.
Here is how the COVID-19 pandemic is affecting the data industry and how enterprises can prepare for the data challenges to come in 2021 and beyond.
#big data #data #data analysis #data security #data integration #etl #data warehouse #data breach #elt
CVDC 2020, the Computer Vision conference of the year, is scheduled for 13th and 14th of August to bring together the leading experts on Computer Vision from around the world. Organised by the Association of Data Scientists (ADaSCi), the premier global professional body of data science and machine learning professionals, it is a first-of-its-kind virtual conference on Computer Vision.
The second day of the conference started with quite an informative talk on the current pandemic situation. Speaking of talks, the second session “Application of Data Science Algorithms on 3D Imagery Data” was presented by Ramana M, who is the Principal Data Scientist in Analytics at Cyient Ltd.
Ramana talked about one of the most important assets of organisations, data and how the digital world is moving from using 2D data to 3D data for highly accurate information along with realistic user experiences.
The agenda of the talk included an introduction to 3D data, its applications and case studies, 3D data alignment, 3D data for object detection and two general case studies, which are-
This talk discussed the recent advances in 3D data processing, feature extraction methods, object type detection, object segmentation, and object measurements in different body cross-sections. It also covered the 3D imagery concepts, the various algorithms for faster data processing on the GPU environment, and the application of deep learning techniques for object detection and segmentation.
#developers corner #3d data #3d data alignment #applications of data science on 3d imagery data #computer vision #cvdc 2020 #deep learning techniques for 3d data #mesh data #point cloud data #uav data
Data integration solutions typically advocate that one approach – either ETL or ELT – is better than the other. In reality, both ETL (extract, transform, load) and ELT (extract, load, transform) serve indispensable roles in the data integration space:
Because ETL and ELT present different strengths and weaknesses, many organizations are using a hybrid “ETLT” approach to get the best of both worlds. In this guide, we’ll help you understand the “why, what, and how” of ETLT, so you can determine if it’s right for your use-case.
#data science #data #data security #data integration #etl #data warehouse #data breach #elt #bid data