The statistic shows the problems that organizations face when using big data technologies worldwide as of 2017. Around ** percent of respondents stated that inadequate analytical know-how was a major problem that their organization faced when using big data technologies as of 2017.

When data and analytics leaders throughout Europe and the United States were asked what the top challenges were with using data to drive business value at their companies, ** percent indicated that the lack of analytical skills among employees was the top challenge as of 2021. Other challenges with using data included data democratization and organizational silos.

SIAM 2013 Presentation

According to the results of a survey on customer experience (CX) among businesses conducted in the United States in 2021, the main challenge affecting data analysis capability for CX is the lack of reliability and integrity of available data. Data security followed, being chosen by almost ** percent of the respondents.

The dataset used for the experiments in the paper, containing 12,000 molecules with 12 biological effects.

Analysis of the projects proposed by the seven finalists to USDOT's Smart City Challenge, including challenge addressed, proposed project category, and project description. The time reported for the speed profiles are between 2:00PM to 8:00PM in increments of 10 minutes.

This dataset describes the degradation of an aircraft engine. The dataset was used for the prognostics challenge competition at the International Conference on Prognostics and Health Management (PHM08). The challenge is still open for the researchers to develop and compare their efforts against the winners of the challenge in 2008. Data sets consist of multiple multivariate time series. Each data set is further divided into training and test subsets. Each time series is from a different aircraft engine – i.e., the data can be considered to be from a fleet of engines of the same type. Each engine starts with different degrees of initial wear and manufacturing variation which is unknown to the user. This wear and variation is considered normal, i.e., it is not considered a fault condition. There are three operational settings that have a substantial effect on engine performance. These settings are also included in the data. The data are contaminated with sensor noise.

This blog post was posted by Wes Barker on July 27, 2018. It was written by Steven Posnack, M.S., M.H.S., Dustin Charles and Wes Barker.

Background: In Brazil, secondary data for epidemiology are largely available. However, they are insufficiently prepared for use in research, even when it comes to structured data since they were often designed for other purposes. To date, few publications focus on the process of preparing secondary data. The present findings can help in orienting future research projects that are based on secondary data.Objective: Describe the steps in the process of ensuring the adequacy of a secondary data set for a specific use and to identify the challenges of this process.Methods: The present study is qualitative and reports methodological issues about secondary data use. The study material was comprised of 6,059,454 live births and 73,735 infant death records from 2004 to 2013 of children whose mothers resided in the State of São Paulo - Brazil. The challenges and description of the procedures to ensure data adequacy were undertaken in 6 steps: (1) problem understanding, (2) resource planning, (3) data understanding, (4) data preparation, (5) data validation and (6) data distribution. For each step, procedures, and challenges encountered, and the actions to cope with them and partial results were described. To identify the most labor-intensive tasks in this process, the steps were assessed by adding the number of procedures, challenges, and coping actions. The highest values were assumed to indicate the most critical steps.Results: In total, 22 procedures and 23 actions were needed to deal with the 27 challenges encountered along the process of ensuring the adequacy of the study material for the intended use. The final product was an organized database for a historical cohort study suitable for the intended use. Data understanding and data preparation were identified as the most critical steps, accounting for about 70% of the challenges observed for data using.Conclusion: Significant challenges were encountered in the process of ensuring the adequacy of secondary health data for research use, mainly in the data understanding and data preparation steps. The use of the described steps to approach structured secondary data and the knowledge of the potential challenges along the process may contribute to planning health research.

Superstore Sales - The Data Quality Challenge Edition (25K Records)

This dataset is an expanded version of the popular "Sample - Superstore Sales" dataset, commonly used for introductory data analysis and visualization. It contains detailed transactional data for a US-based retail company, covering orders, products, and customer information.

This version is specifically designed for practicing Data Quality (DQ) and Data Wrangling skills, featuring a unique set of real-world "dirty data" problems (like those encountered in tools like SPSS Modeler, Tableau Prep, or Alteryx) that must be cleaned before any analysis or machine learning can begin.

This dataset combines the original Superstore data with 15,000 plausibly generated synthetic records, totaling 25,000 rows of transactional data. It includes 21 columns detailing: - Order Information: Order ID, Order Date, Ship Date, Ship Mode. - Customer Information: Customer ID, Customer Name, Segment. - Geographic Information: Country, City, State, Postal Code, Region. - Product Information: Product ID, Category, Sub-Category, Product Name. - Financial Metrics: Sales, Quantity, Discount, and Profit.

🚨 Introduced Data Quality Challenges (The Dirty Data)

This dataset is intentionally corrupted to provide a robust practice environment for data cleaning. Challenges include: Missing/Inconsistent Values: Deliberate gaps in Profit and Discount, and multiple inconsistent entries (-- or blank) in the Region column.

• Data Type Mismatches: Order Date and Ship Date are stored as text strings, and the Profit column is polluted with comma-formatted strings (e.g., "1,234.56"), forcing the entire column to be read as an object (string) type.

• Categorical Inconsistencies: The Category field contains variations and typos like "Tech", "technologies", "Furni", and "OfficeSupply" that require standardization.

• Outliers and Invalid Data: Extreme outliers have been added to the Sales and Profit fields, alongside a subset of transactions with an invalid Sales value of 0.

• Duplicate Records: Over 200 rows are duplicated (with slight financial variations) to test your deduplication logic.

❓ Suggested Analysis and Modeling Tasks

This dataset is ideal for:

Data Wrangling/Cleaning (Primary Focus): Fix all the intentional data quality issues before proceeding.

Exploratory Data Analysis (EDA): Analyze sales distribution by region, segment, and category.

Regression: Predict the Profit based on Sales, Discount, and product features.

Classification: Build an RFM Model (Recency, Frequency, Monetary) and create a target variable (HighValueCustomer = 1 if total sales are* $>$ $1000$*) to be predicted by logistical regression or decision trees.

Time Series Analysis: Aggregate sales by month/year to perform forecasting.

Acknowledgements

This dataset is an expanded and corrupted derivative of the original Sample Superstore dataset, credited to Tableau and widely shared for educational purposes. All synthetic records were generated to follow the plausible distribution of the original data.

Data sets for Reproducibility Challenge 2022 [Re] FOCUS: Flexible Optimizable Counterfactual Explanations for Tree Ensembles. The paper can be found at OpenReview.net.

The United States is today the global leader in networking and information technology NIT. That leadership is essential to U.S. economic prosperity, security, and quality of life. The Nation?s leadership position is the product of its entire NIT ecosystem, including its market position, commercialization system, and higher education and research system...

This dataset contains a diverse range of imaging biological data and models. The data is sourced and curated by a team of experts at CZI and is made available as part of these datasets only when it is not publicly accessible or requires transformations to support model training.

In 2020, ** percent of healthcare providers and ** percent of healthcare payers surveyed in the United States indicated that lack of technical interoperability was the biggest challenge around health data sharing. Among ** percent of providers, noted that timeliness of data that is shared was a challenge, in comparison only ** percent of payers shared the same concern.

Data for the SPHERE Challenge that will take place in conjunction with ECML-PKDD 2016. Please cite: Niall Twomey, Tom Diethe, Meelis Kull, Hao Song, Massimo Camplani, Sion Hannuna, Xenofon Fafoutis, Ni Zhu, Pete Woznowski, Peter Flach, Ian Craddock: “The SPHERE Challenge: Activity Recognition with Multimodal Sensor Data”, 2016;arXiv:1603.00797. BibTeX record: @article{twomey2016sphere, title={The SPHERE Challenge: Activity Recognition with Multimodal Sensor Data}, author={Twomey, Niall and Diethe, Tom and Kull, Meelis and Song, Hao and Camplani, Massimo and Hannuna, Sion and Fafoutis, Xenofon and Zhu, Ni and Woznowski, Pete and Flach, Peter and others}, journal={arXiv preprint arXiv:1603.00797}, year={2016} } http://arxiv.org/abs/1603.00797v2 Complete download (zip, 41.4 MiB)

This dataset contains a transcriptomics biological data and models. The models embed transcriptomic data and facilitate transcriptomic analysis. The data is sourced and curated by a team of experts at CZI and is made available as part of these datasets only when it is not publicly accessible or requires transformations to support model training.

On June 4-6, 2019, the NSTC NITRD Program, in collaboration with the NSTC's MLAI Subcommittee, held a workshop to assess the research challenges and opportunities at the intersection of cybersecurity and artificial intelligence. The workshop brought together senior members of the government, academic, and industrial communities to discuss the current state of the art and future research needs, and to identify key research gaps. This report is a summary of those discussions, framed around research questions and possible topics for future research directions. More information is available at https://www.nitrd.gov/nitrdgroups/index.php?title=AI-CYBER-2019.

Objective(s): Momentum for open access to research is growing. Funding agencies and publishers are increasingly requiring researchers make their data and research outputs open and publicly available. However, this introduces many challenges, especially when managing confidential clinical data. The aim of this 1 hr virtual workshop is to provide participants with knowledge about what synthetic data is, methods to create synthetic data, and the 2023 Pediatric Sepsis Data Challenge. Workshop Agenda: 1. Introduction - Speaker: Mark Ansermino, Director, Centre for International Child Health 2. "Leveraging Synthetic Data for an International Data Challenge" - Speaker: Charly Huxford, Research Assistant, Centre for International Child Health 3. "Methods in Synthetic Data Generation." - Speaker: Vuong Nguyen, Biostatistician, Centre for International Child Health and The HIPpy Lab This workshop draws on work supported by the Digital Research Alliance of Canada. Data Description: Presentation slides, Workshop Video, and Workshop Communication Charly Huxford: Leveraging Synthetic Data for an International Data Challenge presentation and accompanying PowerPoint slides. Vuong Nguyen: Methods in Synthetic Data Generation presentation and accompanying Powerpoint slides. This workshop was developed as part of Dr. Ansermino's Data Champions Pilot Project supported by the Digital Research Alliance of Canada. NOTE for restricted files: If you are not yet a CoLab member, please complete our membership application survey to gain access to restricted files within 2 business days. Some files may remain restricted to CoLab members. These files are deemed more sensitive by the file owner and are meant to be shared on a case-by-case basis. Please contact the CoLab coordinator on this page under "collaborate with the pediatric sepsis colab."

The big data – huge amount of data – era has begun and is redefining how organizations deal with information. While the business sector has been using and developing big data applications for nearly a decade, only recently the public sector has begun to adopt this technology to gather information and use it as a decision support tool. Few organizations have so many advantages to harness the potential of the big data as the public service agencies, because of a large amount of data they have access to. However, due to the current theme, there is still a long way to go. Some papers have presented ways in which governments are using big data to better serve their citizens. Nevertheless, there is still much uncertainty about the real possibility of improving government operations through this technology. By analyzing the literature related to the topic, this paper aims to present the areas of public administration that can take advantage of the data analysis. In addition, raising the challe...

The global Healthcare Data Collection and Labeling market is experiencing robust expansion, projected to reach an estimated $12,500 million by 2025 and steadily grow at a Compound Annual Growth Rate (CAGR) of 18% through 2033. This significant growth is primarily fueled by the escalating demand for high-quality, annotated healthcare data to power advancements in Artificial Intelligence (AI) and Machine Learning (ML) applications within the sector. Key drivers include the increasing adoption of AI in medical imaging analysis, drug discovery, personalized medicine, and predictive diagnostics. The burgeoning volume of healthcare data generated from electronic health records (EHRs), wearable devices, and genomic sequencing further necessitates sophisticated data collection and labeling services to unlock its full potential. Several critical trends are shaping the market landscape. The rise of federated learning and privacy-preserving techniques is addressing data security and compliance concerns, enabling collaborative model training without direct data sharing. Furthermore, the demand for specialized labeling for diverse data types such as audio (for voice-enabled diagnostic tools) and images (for radiology and pathology) is intensifying. While the market presents immense opportunities, restraints such as stringent data privacy regulations (e.g., HIPAA, GDPR) and the high cost associated with acquiring and labeling vast datasets present ongoing challenges. However, the continuous innovation in AI-powered labeling tools and the growing awareness of the ROI from accurate data are expected to mitigate these challenges, propelling the market forward. Major companies like Alegion, Ango AI, Appen Limited, and Snorkel AI are at the forefront, offering advanced solutions to meet these evolving needs across segments like Biotech, Dentistry, and Diagnostic Centers. This comprehensive report delves into the rapidly evolving landscape of Healthcare Data Collection and Labeling, a critical enabler for advancements in artificial intelligence (AI) and machine learning (ML) within the healthcare industry. The study spans the historical period of 2019-2024, with a base year of 2025 and extends through an estimated forecast period of 2025-2033, offering deep insights into market dynamics. The global market for healthcare data collection and labeling is projected to witness significant growth, with the estimated market size reaching USD 5,700 million by 2025 and expected to climb to over USD 15,800 million by 2033, exhibiting a robust CAGR. This growth is fueled by the increasing demand for high-quality, accurately labeled datasets across various healthcare applications, from drug discovery to diagnostic imaging and personalized medicine. The report provides an in-depth analysis of market trends, key players, regional dominance, product insights, and the driving forces and challenges shaping this vital sector.