Demographics Analysis with Consumer Edge Credit & Debit Card Transaction Data

Consumer Edge is a leader in alternative consumer data for public and private investors and corporate clients. CE Transact Signal is an aggregated transaction feed that includes consumer transaction data on 100M+ credit and debit cards, including 14M+ active monthly users. Capturing online, offline, and 3rd-party consumer spending on public and private companies, data covers 12K+ merchants and deep demographic and geographic breakouts. Track detailed consumer behavior patterns, including retention, purchase frequency, and cross shop in addition to total spend, transactions, and dollars per transaction.

Consumer Edge’s consumer transaction datasets offer insights into industries across consumer and discretionary spend such as: • Apparel, Accessories, & Footwear • Automotive • Beauty • Commercial – Hardlines • Convenience / Drug / Diet • Department Stores • Discount / Club • Education • Electronics / Software • Financial Services • Full-Service Restaurants • Grocery • Ground Transportation • Health Products & Services • Home & Garden • Insurance • Leisure & Recreation • Limited-Service Restaurants • Luxury • Miscellaneous Services • Online Retail – Broadlines • Other Specialty Retail • Pet Products & Services • Sporting Goods, Hobby, Toy & Game • Telecom & Media • Travel

This data sample illustrates how Consumer Edge data can be used to compare demographics breakdown (age and income excluded in this free sample view) for one company vs. a competitor for a set period of time (Ex: How do demographics like wealth, ethnicity, children in the household, homeowner status, and political affiliation differ for Walmart vs. Target shopper?).

Inquire about a CE subscription to perform more complex, near real-time demographics analysis functions on public tickers and private brands like: • Analyze a demographic, like age or income, within a state for a company in 2023 • Compare all of a company’s demographics to all of that company’s competitors through most recent history

Consumer Edge offers a variety of datasets covering the US and Europe (UK, Austria, France, Germany, Italy, Spain), with subscription options serving a wide range of business needs.

Use Case: Demographics Analysis

Problem A global retailer wants to understand company performance by age group.

Solution Consumer Edge transaction data can be used to analyze shopper transactions by age group to understand: • Overall sales growth by age group over time • Percentage sales growth by age group over time • Sales by age group vs. competitors

Impact Marketing and Consumer Insights were able to: • Develop weekly reporting KPI's on key demographic drivers of growth for company-wide reporting • Reduce investment in underperforming age groups, both online and offline • Determine retention by age group to refine campaign strategy • Understand how different age groups are performing compared to key competitors

Corporate researchers and consumer insights teams use CE Vision for:

Corporate Strategy Use Cases • Ecommerce vs. brick & mortar trends • Real estate opportunities • Economic spending shifts

Marketing & Consumer Insights • Total addressable market view • Competitive threats & opportunities • Cross-shopping trends for new partnerships • Demo and geo growth drivers • Customer loyalty & retention

Investor Relations • Shareholder perspective on brand vs. competition • Real-time market intelligence • M&A opportunities

Most popular use cases for private equity and venture capital firms include: • Deal Sourcing • Live Diligences • Portfolio Monitoring

Public and private investors can leverage insights from CE’s synthetic data to assess investment opportunities, while consumer insights, marketing, and retailers can gain visibility into transaction data’s potential for competitive analysis, understanding shopper behavior, and capturing market intelligence.

Most popular use cases among public and private investors include: • Track Key KPIs to Company-Reported Figures • Understanding TAM for Focus Industries • Competitive Analysis • Evaluating Public, Private, and Soon-to-be-Public Companies • Ability to Explore Geographic & Regional Differences • Cross-Shop & Loyalty • Drill Down to SKU Level & Full Purchase Details • Customer lifetime value • Earnings predictions • Uncovering macroeconomic trends • Analyzing market share • Performance benchmarking • Understanding share of wallet • Seeing subscription trends

Fields Include: • Day • Merchant • Subindustry • Industry • Spend • Transactions • Spend per Transaction (derivable) • Cardholder State • Cardholder CBSA • Cardholder CSA • Age • Income • Wealth • Ethnicity • Political Affiliation • Children in Household • Adults in Household • Homeowner vs. Renter • Business Owner • Retention by First-Shopped Period ...

This data release includes tables and plots of results for pesticide compounds (pesticides and degradates) analyzed in groundwater samples collected by the USGS National Water-Quality Assessment Project during water years 2013-18 and in associated quality-control samples that are used to assess the quality of the reported pesticide results. All samples were analyzed by the USGS National Water Quality Laboratory (NWQL) using laboratory schedule 2437. The table of groundwater data includes pesticide results as reported by the laboratory, along with results that represent the application of censoring levels at the 90-percent upper confidence limit of the 95th percentile of laboratory blank concentrations determined by water year. The other seven tables included in this data release contain pesticide results for the following types of quality-control samples: field blanks, matrix spikes, and replicates collected at field sites; laboratory blanks and reagent spikes prepared by the NWQL; and third-party blind blanks and blind spikes prepared by the USGS Quality Systems Branch. The table of pesticide results for field matrix spikes includes the paired groundwater results and other fields needed to calculate spike recovery as described in the data processing steps of the metadata file. The table of pesticide results for field replicates includes the paired groundwater results and other fields needed to calculate variability in detection and (or) concentration as described in the data processing steps of the metadata file. Results included in this data release for laboratory reagent spikes are for water year 2018 only; results for laboratory reagent spikes analyzed in water years 2013-15 are available in Shoda and others (2017) and in water years 2016-17 are available in Wieben (2019). Useful graphical representations of data in the tables are provided in various plots that compare detections and concentrations for groundwater and blank samples, compare recovery results for the different spike types, and illustrate variability in replicate-sample results across concentration ranges. Shoda, M.E., Nowell, L.H., Bexfield, L.M., Sandstrom, M.W., Stone, W.W., 2017, Recovery data for surface water, groundwater and lab reagent samples analyzed by the USGS National Water Quality Laboratory schedule 2437, water years 2013-15: U.S. Geological Survey data release, https://doi.org/10.5066/F7QZ28G4. Wieben, C.M., 2019, Pesticide recovery data for surface-water and lab reagent samples analyzed by the USGS National Water Quality Laboratory schedule 2437, water years 2016-17: U.S. Geological Survey data release, https://doi.org/10.5066/P93MWMVF. There are 8 tables included in this data release: Table1_GroundwaterData2013_2018.xlsx -- Pesticide results for groundwater samples collected by the National Water-Quality Assessment Project, 2013-18. This table includes pesticide results as reported by the laboratory, along with results that represent the application of censoring levels at the 90-percent upper confidence limit of the 95th percentile of laboratory blank concentrations determined by water year. Results that were rejected for data analysis for reasons described in the metadata document and in the associated Scientific Investigations Report are flagged. Table2_FieldBlankData2013_2018.xlsx -- Pesticide results for field blanks collected at groundwater sites by the National Water-Quality Assessment Project, 2013-18. Results that were rejected for data analysis for reasons described in the metadata document and in the associated Scientific Investigations Report are flagged. Table3_FieldSpikeData2013_2018.xlsx -- Pesticide results for field matrix spikes collected at groundwater sites by the National Water-Quality Assessment Project, 2013-18. Results of paired groundwater samples are included. Results that were rejected for data analysis for reasons described in the metadata document and in the associated Scientific Investigations Report are flagged. Fields needed to calculate spike recovery as described in the data processing steps of the metadata file are included. Table4_FieldRepData2013_2018.xlsx -- Pesticide results for field replicates collected at groundwater sites by the National Water-Quality Assessment Project, 2013-18. Results of paired groundwater samples are included. Results that were rejected for data analysis for reasons described in the metadata document and in the associated Scientific Investigations Report are flagged. Fields needed to calculate variability in detection and (or) concentration as described in the data processing steps of the metadata file are included. Table5_LabBlankData2013_2018.xlsx -- Pesticide results for laboratory blanks prepared by the National Water Quality Laboratory, 2013-18. Results that were rejected for data analysis for reasons described in the metadata document and in the associated Scientific Investigations Report are flagged. Table6_LabReagentSpikeData2018.xlsx -- Pesticide results for laboratory reagent spikes prepared by the National Water Quality Laboratory, 2018. Table7_QSBBlindBlankData_2018.xlsx -- Pesticide results for third-party blind blanks prepared by the Quality Systems Branch, 2018. Table8_QSBBlindSpikeData2013_2018.xlsx -- Pesticide results for third-party blind spikes prepared by the Quality Systems Branch, 2013-18. Results that were rejected for data analysis for reasons described in the metadata document and in the associated Scientific Investigations Report are flagged. There are 5 sets of graphical representations of the data. Detailed descriptions of the plots included in this data release are provided in the associated Scientific Investigations Report: PlotGroup1_TimeSeries.pdf – Plots of reported detections and concentrations in groundwater samples (Table 1), field blanks (Table 2), and laboratory blanks (Table 5) for individual compounds by analysis date, showing the frequency, timing, and magnitude of detections among these sample types. Nondetections are plotted as open circles at the standard laboratory reporting level in effect at the time of analysis (identified on each graph) or, if applicable, at the raised reporting level specified for an individual sample. PlotGroup2_EDFsByWY.pdf – Empirical distribution functions illustrating upper percentiles of concentrations for groundwater samples (Table 1) relative to field blanks (Table 2) and laboratory blanks (Table 5) for selected pesticides and water years. Plots are provided for compounds and water years with at least one groundwater detection and a quantifiable (detected) 99th percentile of concentration for laboratory blanks. PlotGroup3_SpikeTimeSeries.pdf – Plots of recoveries for laboratory reagent spikes (Table 6), field matrix spikes (Table 3), and third-party blind spikes (Table 8) for individual pesticides by analysis date, illustrating the range of typical recoveries. Lowess (locally weighted scatterplot smoothing) curves are included to illustrate general changes in recovery through time. Results for laboratory reagent spikes analyzed in water years 2013-15 are available in Shoda and others (2017) and in water years 2016-17 are available in Wieben (2019). PlotGroup4_LabFieldSpikes.pdf – Box plots comparing recoveries for laboratory reagent spikes (Table 6) and field matrix spikes (Table 3). Results for laboratory reagent spikes analyzed in water years 2013-15 are available in Shoda and others (2017) and in water years 2016-17 are available in Wieben (2019). PlotGroup5_FieldRepVar.pdf – Plots of standard deviation and relative standard deviation against mean concentration of field replicate samples (Table 4) for selected pesticides, including assigned boundaries between the lower concentration range where standard deviation generally is more uniform and the upper concentration range where relative standard deviation generally is more uniform. Plots are provided for pesticides that had 10 or more replicate pairs with detections in both samples of the pair.

Global Spend Analysis with Consumer Edge Credit & Debit Card Transaction Data

Consumer Edge is a leader in alternative consumer data for public and private investors and corporate clients. CE Vision EUR is an aggregated transaction feed that includes consumer transaction data on 6.7M+ Europe-domiciled payment accounts, including 5.3M+ active monthly users. Capturing online, offline, and 3rd-party consumer spending on public and private companies, data covers 4.4K+ brands and 620 symbols including 490 public tickers. Track detailed consumer behavior patterns, including retention, purchase frequency, and cross shop in addition to total spend, transactions, and dollars per transaction.

Consumer Edge’s consumer transaction datasets offer insights into industries across consumer and discretionary spend such as: • Apparel, Accessories, & Footwear • Automotive • Beauty • Commercial – Hardlines • Convenience / Drug / Diet • Department Stores • Discount / Club • Education • Electronics / Software • Financial Services • Full-Service Restaurants • Grocery • Ground Transportation • Health Products & Services • Home & Garden • Insurance • Leisure & Recreation • Limited-Service Restaurants • Luxury • Miscellaneous Services • Online Retail – Broadlines • Other Specialty Retail • Pet Products & Services • Sporting Goods, Hobby, Toy & Game • Telecom & Media • Travel

This data sample illustrates how Consumer Edge data can be used to understand a company’s growth by country for a specific time period (Ex: What was McDonald’s year-over-year growth by country from 2019-2020?)

Inquire about a CE subscription to perform more complex, near real-time global spend analysis functions on public tickers and private brands like: • Analyze year-over-year spend growth for a company for a subindustry by country • Analyze spend growth for a company vs. its competitors by country through most recent time

Consumer Edge offers a variety of datasets covering the US and Europe (UK, Austria, France, Germany, Italy, Spain), with subscription options serving a wide range of business needs.

Use Case: Global Spend Analysis

Problem A global retailer wants to understand company performance by geography to identify growth and expansion opportunities.

Solution Consumer Edge transaction data can be used to analyze shopper behavior across geographies and track: • Growth trends by country vs. competitors • Brand performance vs. subindustry by country • Opportunities for product and location expansion

Impact Marketing and Consumer Insights were able to: • Develop weekly reporting KPI's on key growth drivers by geography for company-wide reporting • Refine strategy in underperforming geographies, both online and offline • Identify areas for investment and expansion by country • Understand how different cohorts are performing compared to key competitors

Corporate researchers and consumer insights teams use CE Vision for:

Corporate Strategy Use Cases • Ecommerce vs. brick & mortar trends • Real estate opportunities • Economic spending shifts

Marketing & Consumer Insights • Total addressable market view • Competitive threats & opportunities • Cross-shopping trends for new partnerships • Demo and geo growth drivers • Customer loyalty & retention

Investor Relations • Shareholder perspective on brand vs. competition • Real-time market intelligence • M&A opportunities

Most popular use cases for private equity and venture capital firms include: • Deal Sourcing • Live Diligences • Portfolio Monitoring

Public and private investors can leverage insights from CE’s synthetic data to assess investment opportunities, while consumer insights, marketing, and retailers can gain visibility into transaction data’s potential for competitive analysis, understanding shopper behavior, and capturing market intelligence.

Most popular use cases among public and private investors include: • Track Key KPIs to Company-Reported Figures • Understanding TAM for Focus Industries • Competitive Analysis • Evaluating Public, Private, and Soon-to-be-Public Companies • Ability to Explore Geographic & Regional Differences • Cross-Shop & Loyalty • Drill Down to SKU Level & Full Purchase Details • Customer lifetime value • Earnings predictions • Uncovering macroeconomic trends • Analyzing market share • Performance benchmarking • Understanding share of wallet • Seeing subscription trends

Fields Include: • Day • Merchant • Subindustry • Industry • Spend • Transactions • Spend per Transaction (derivable) • Cardholder State • Cardholder CBSA • Cardholder CSA • Age • Income • Wealth • Ethnicity • Political Affiliation • Children in Household • Adults in Household • Homeowner vs. Renter • Business Owner • Retention by First-Shopped Period • Churn • Cross-Shop • Average Ticket Buckets

According to Cognitive Market Research, The global third-party risk management market size is USD 5.5 billion in 2023 and will expand at a compound annual growth rate (CAGR) of 17.20% from 2023 to 2030.

The demand for third party risk managements is rising due to Resource optimization to protect the interests of millions of digital financial service consumers.
Demand for cloud remains higher in the third party risk management market.
The BFSI category held the highest third party risk management market revenue share in 2023.
North American third party risk management will continue to lead, whereas the European third party risk management market will experience the most substantial growth until 2030.

Rising Instances of Cyber-attacks and Frauds in Digital Financial Services to Provide Viable Market Output

With greater internet penetration, the deployment of smart technology has enhanced the appeal of digital financial services such as mobile banking and digital payments. Because of the growth of digital services, businesses must adapt and incorporate sophisticated technologies into their offerings. However, as the use of digital payment systems in the BFSI sector has grown, so have the risks of cyber-attacks and fraud. BFSI stakeholders are investing heavily to protect their clients from such disasters. The market for third-party risk management will develop as resources are optimized to protect the interests of millions of users of digital financial services.

Growing digitization of Businesses to Propel Market Growth

Industry automation and digitization have exacerbated data privacy and security breaches. With growing digitization, various stakeholders become involved, heightening safety issues. This spike in third-party involvement is propelling the third-party risk management market, raising associated hazards. As industries increasingly rely on external partners and vendors, the need for robust risk management solutions to protect against potential vulnerabilities and ensure the integrity of sensitive data becomes critical in the midst of an evolving landscape of technological advancements and increased interconnectivity.

Market Dynamics of

Third Party Risk Management Market

Key Drivers of

Third Party Risk Management Market

Increasing Regulatory Compliance Demands : Organizations are encountering heightened regulatory pressures to ensure that third parties adhere to legal and compliance standards, particularly in sectors such as finance, healthcare, and technology. Regulations like GDPR, HIPAA, and SOX require comprehensive risk assessments and ongoing monitoring. As the consequences of non-compliance become more severe, businesses are allocating resources to third-party risk management platforms to protect their operations and ensure regulatory compliance.

Escalating Outsourcing and Supply Chain Complexity : As organizations expand their global reach and outsource essential services, the intricacy of managing third-party vendors, suppliers, and partners significantly increases. This escalation results in greater exposure to cybersecurity threats, operational interruptions, and data breaches. The demand for real-time visibility, thorough due diligence, and risk profiling across multi-tier vendor ecosystems is a key factor driving the need for effective TPRM solutions.

Increase in Cybersecurity Threats from Third Parties : Third-party vendors frequently represent the most vulnerable aspect of an organization’s cybersecurity framework. Notable breaches associated with third-party failures have raised awareness regarding vendor-related cyber risks. Companies are now pursuing comprehensive tools to continuously monitor vendor activities, implement security measures, and proactively address vulnerabilities, leading to substantial growth in the market for third-party risk management software and services.

Key Restraints in

Third Party Risk Management Market

High Implementation and Operational Costs : Implementing a successful Third-Party Risk Management (TPRM) program often necessitates a significant initial investment in software, training, and resources. For small to medium-sized enterprises, these expenses can be overwhelming. Beyond the initial setup, continuous risk monitoring and compliance audits further elevate operational costs, which can deter adoption among organizations with limited budgets or those lack...

McGRAW’s US B2B Data: Accurate, Reliable, and Market-Ready

Our B2B database delivers over 80 million verified contacts with 95%+ accuracy. Supported by in-house call centers, social media validation, and market research teams, we ensure that every record is fresh, reliable, and optimized for B2B outreach, lead generation, and advanced market insights.

Our B2B database is one of the most accurate and extensive datasets available, covering over 91 million business executives with a 95%+ accuracy guarantee. Designed for businesses that require the highest quality data, this database provides detailed, validated, and continuously updated information on decision-makers and industry influencers worldwide.

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Key Data Attributes:

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• Technology Insights: Information on hardware and software usage across organizations
• Social Media Connections: LinkedIn, Facebook, and direct dial contacts
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Unlike many providers that rely solely on third-party vendor files, McGRAW takes a hands-on approach to data validation. Our dedicated nearshore and offshore call centers engage directly with data before each delivery to ensure every record meets our high standards of accuracy and relevance.

In addition, our teams of social media validators, market researchers, and digital marketing specialists continuously refine and update records to maintain data freshness. Each dataset undergoes multiple verification checks using internal validation processes and third-party tools such as Fresh Address, BriteVerify, and Impressionwise to guarantee the highest data quality.

Additional Data Solutions and Services

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Many B2B data providers rely on vendor-contributed files without conducting the rigorous validation necessary to ensure accuracy. This often results in outdated and unreliable data that fails to meet the demands of a fast-moving business environment.

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Through continuous validation, social media verification, and real-time updates, McGRAW provides a high-quality, dependable database for businesses that prioritize data integrity and performance. Our Global Business Executives database is the ideal solution for companies that need accurate, relevant, and market-ready data to fuel their strategies.

From October 2017 through September 2022, the National Water Quality Network (NWQN) monitored 110 surface-water river and stream sites and more than 1,800 groundwater wells for a large number of water-quality analytes, for which associated quality-control data and corresponding statistical summaries are included in this data release. The quality-control data—for samples that were collected in the field (at all 110 surface-water sites, 350 groundwater wells, and 16 quality-control-only sites), prepared in the laboratory, or prepared by a third party—can be used to assess the quality of environmental data collected by the NWQN through the estimation of bias and variability in reported results. The general analyte groups that were monitored at NWQN surface-water and (or) groundwater sites and have associated quality-control data in this data release include major ions, nutrients, trace elements, pesticides, volatile organic compounds, hormones, pharmaceuticals, radionuclides, microbial indicators, sediment, and environmental tracers. For each analyte group, the data tables contain results for one or more of the following types of quality-control samples, where relevant: blanks, matrix spikes, and replicates collected at field sites; laboratory blanks, reagent spikes, and matrix spikes prepared by the USGS National Water Quality Laboratory (NWQL) (quality-control samples prepared by other analyzing laboratories are not included in the current data release); and third-party blanks, spikes, and reference samples prepared by the USGS Quality Systems Branch (QSB). For each relevant analyte, tables of summary statistics characterize the frequency and concentrations of blank detections, the typical magnitude of and variability in spike and reference-sample recoveries, and the typical variability between replicate concentrations. Tables included in this data release: Table1_SiteList.txt: Information about National Water Quality Network sites that have associated quality-control data. Table2_AnalyteList.txt: Information about National Water Quality Network analytes that have associated quality-control data, including available aquatic-life and (or) human-health benchmarks and selected information regarding analytical methods. Table3_BlankData.txt: For all relevant analytes, results for blanks collected at field sites, prepared in the laboratory, or prepared by a third party. Table4_SpikeData.txt: For all relevant analytes, results for matrix spikes prepared in the field, matrix spikes prepared in the laboratory, reagent spikes prepared in the laboratory, or reagent spikes prepared by a third party. For matrix spikes, results of paired environmental samples are included. Table5_ReplicateData.txt: For all relevant analytes, results for field replicates and paired environmental samples. Table 6_ReferenceData.txt: For all relevant analytes, results for third-party reference samples. Table7_BlankStats.txt: For all relevant analytes, summary statistics for each type of available blank sample. Table8_SpikeStats.txt: For all relevant analytes, summary statistics for each type of available spike sample. Table9_ReplicateStats.txt: For all relevant analytes, summary statistics for field replicates. Table10_ReferenceStats.txt: For all relevant analytes, summary statistics for reference samples.

In 2024, financial media networks (FMNs) accounted for 0.1 percent of digital advertising spending in the United States. The share is expected to quadruple by 2026. FMNs are defined as financial institutions with their own ad networks using their own first-party data to target their customers with third-party ads. Examples include Chase Bank, PayPal, or Klarna.

San Francisco Campaign and Governmental Conduct Code ("S.F. C&GC Code") sections 1.143(c), 1.152(a)(3), 1.161(b), 1.161.5, and 1.160.5 require persons who make any independent expenditure, electioneering communication, or member communication that clearly identifies a candidate for City elective office or who authorizes, administers or pays for a persuasion poll to file disclosure statements with the Ethics Commission. For detailed instructions, please see Third Party Disclosure Form Regarding Candidates.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

In 2000, the Department for Environment, Food and Rural Affairs (DEFRA) and the National Assembly for Wales commissioned Halcrow to develop Futurecoast, a completely new way of predicting shoreline evolution. The analysis it contains gives a better understanding of coastal systems and their characteristics, and it is now being used to create the next generation of Shoreline Management Plans. The main format is oblique aerial photos taken from helicopter of the entire GB coastline, linked to the Ordnance Survey map. They were produced by DEFRA in conjunction with Halcro, expressly to assist in the shoreline management planning process. A 'behavioural systems' approach, such as was adopted by the FUTURECOAST project, involves the identification of the different elements that make up the coastal structure and developing an understanding of how these elements interact on a range of both temporal and spatial scales. In this exercise it is the interaction between the units that is central to determining the behaviour. Feedback invariably plays an important role and changes in energy/sediment inputs that affect one unit can in turn affect other units, which themselves give rise to a change in the level of energy/sediment input. Whilst the starting point for a behavioural system is the energy and sediment pathways, it is important to identify the causative mechanism as a basis for building a robust means of predicting the response to change. This must take account of variations in sediment supply and forcing parameters, such as tide and wave energy. However, it is also important to look for situations where the system response is to switch to a different state, for example, the catastrophic failure of a spit, or the switching of channels as a consequence of episodic storm events.

Summary

This package contains data and processing tools for replicating the research presented in the paper "Statistical Test of Distance–Duality Relation with Type Ia Supernovae and Baryon Acoustic Oscillations" (2018, ApJ, DOI: 10.3847/1538-4357/aac88f, arXiv:1604.04631).

The compressed archive file "ddmc-nosample-v3.1.tar.xz" contains only the compressed SNIa data, the BAO measurements, and 3rd-party data files used in this work. The random samples can be re-created by the tools included in the package. This is the file suitable for low-speed download.

The file "ddmc-v3.1.tar.xz" contains the full set of random sample output files and analysis results in addition to those in the "ddmc-nosample-v3.1.tar.xz" file. This is the archive containing all the data and figure files used directly in the paper.

To uncompress the files, the XZ Utils software package is required.

The file "CHECKSUM.asc" is a GPG-clearsigned text file containing the SHA-512 checksum values for file integrity verification. The text file itself is signed with the GPG key 0xE977A6E990102402 available from keyservers.

Please read the README files in each package for more details and instructions.

Release notes for version 3.1

Version 3.1 is a minor revision with the addition of some alternative input parameter distributions.

Release notes for version 3

This is the 3rd version representing a re-written analysis of the distance-duality test. This new version updated and renamed the complementary parameter (CP) sets to match the ones used in the paper. New results concerning the interpretation of results as a diagnostics of distance measurement systematics are presented. Also included are updated utility scripts, new tests for Gaussian approximation to the results, and new data-visualization scripts.

Earlier versions

Earlier versions are available from Zenodo. Links: v1, v2.

Water quality data is held in the WISKI Kisters Water Quality Module (KiWQM). The Water Quality Archive provides a central repository for all data relating to water quality measurements that have since the 1960's been collected from over 22,000 sampling points, with 2,500 active today. Samples are taken from coastal or estuarine waters, rivers, lakes, ponds, canals or groundwater. Additional data has been provided by third parties as public record. The data measures a wide range of water quality parameters, such as nutrients, metals and physio-chemical determinants for both archived and processed data and these are used to assess discharge monitoring against discharge permits, investigation of pollution incidents or environmental monitoring. Whilst this data is used to meet the requirements of The Water Framework Directive and Bathing Water Quality assessments, the system is currently unable to record anything about compliance.

In 2024, advertising spending on financial media networks (FMNs) was estimated at *** million U.S. dollars in the United States. The value is expected to double in 2025, and then to double again in 2026. FMNs are defined as financial institutions with their own ad networks using their own first-party data to target their customers with third-party ads. Examples include Chase Bank, PayPal, or Klarna.

Demographic and PII data (including emails, phone numbers, and addresses) for the US Millennial and Gen Z population segments. Fully opt-in and CCPA compliant (direct submission from the individuals). 30 million+ population.

High success and conversion rates for direct marketing, targeted ads, identity verification, and demographic research.

This data can be merged into the BIGDBM Consumer dataset or have specific data fields appended from the BIGDBM Consumer dataset.

BIGDBM Privacy Policy: https://bigdbm.com/privacy.html

Abstract

Rates of overweight, obesity, and chronic diseases such as cardiovascular diseases, hypertension, type 2 diabetes and certain cancers (bowel, lung, prostate and uterine) are on the rise in most sub-saharan Africa (SSA) countries like kenya. These increases can be largely attributed to the shift toward unhealthy diet patterns and increased access to processed foods that are high in fat, sugar, and sodium. The influx of supermarkets in east africa and the replacement of traditional foods for processed foods places this region in a vulnerable position for greater increases in chronic disease rates. Consumer purchasing history from supermarkets can provide valuable insight to food intake over time and the present and future effects on chronic diseases. Purchasing data from supermarkets is available yet underutilized in SSA.

The study aimed to harmonize and increase accessibility to grocery data, use statistical methods to explore purcharing patterns and predict the effects of nutrition on chronic diseases, and inform policy on the various influences on consumer purchases.

Geographic coverage

County coverage: Nairobi and Kiambu.

Analysis unit

Individuals and supermarket transaction records.

Universe

The survey covers transaction records of individuals who made purchases in supermarkets.

Sampling procedure

The study is a cross-sectional exploratory study with a phased approach employing quantitative secondary data collection from a third-party information management solution provider. The third party provider employs an open integrated point of sale and store information retail system that connects retail touch points and sales channels in several counties in Kenya.

Sampling was conducted after a census of all supermarkets subscribed to the third party system was done. Only those counties with supermarkets subscribed to the platform were sampled. A sample of large, medium sized and small supermarkets were selected to participate in the study. The supermarket sizes were determined as follows; large supermarkets ( supermarkets with a cumulative total of more than 8 branch networks). Medium size supermarkets will be those with 3-8 branch networks in the counties and smaller supermarkets are those with 1-2 branch networks.

Grocery data was received from a supermarket chain with 3 branches.

Sampling deviation

Not Applicable

Mode of data collection

Other [oth]

Research instrument

A standardized form was developed to guide in extration of information from 3rd party information provider for supermarket purchase data. Variables of interest includes supermarket name, supermarket branch, location of supermarket, invoice id, customer id, customer demographics (gender, age), date and time of purchase, product name purchased, unit price per item, number of items purchased, payment method used by customer for purchase etc.

Secondary data collected will not be identifiable as it will be anonymized at the supermarket and client level.

The standardized form is provided as external resources data. V1-V18 the questions are found in the “Study abstraction tool”

Cleaning operations

Not Applicable

Response rate

Not Applicable

Sampling error estimates

Not Applicable

Data Clean Room Market Outlook

According to our latest research, the global data clean room market size in 2024 stood at USD 1.27 billion, reflecting the growing adoption of privacy-centric data collaboration solutions worldwide. The market is witnessing robust expansion, registering a compound annual growth rate (CAGR) of 19.6% from 2025 to 2033. By the end of 2033, the data clean room market is projected to reach a substantial valuation of USD 6.14 billion. This impressive growth is being driven by increasing regulatory pressure for data privacy, the phasing out of third-party cookies, and the urgent need for secure data collaboration in the digital advertising and analytics ecosystems.

The primary growth factor for the data clean room market is the escalating demand for privacy-compliant data sharing and analytics. As organizations face heightened scrutiny over data privacy, especially with the enforcement of regulations such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), there is a clear shift towards solutions that enable secure, privacy-preserving data collaboration. Data clean rooms allow multiple parties to analyze shared data sets without exposing personally identifiable information (PII), thereby maintaining compliance and trust. This feature is especially vital for industries such as advertising, where brands, publishers, and platforms require granular insights without breaching privacy laws.

Another significant driver is the rapid transformation of the digital advertising landscape. With major browsers phasing out third-party cookies, advertisers and marketers are seeking alternative methods to measure campaign effectiveness and audience insights. Data clean rooms provide a secure environment for brands and publishers to match and analyze first-party data, unlocking new opportunities for targeted advertising and advanced measurement. In addition, the rise of walled gardens—large digital platforms that control vast amounts of user data—has further accelerated the adoption of data clean rooms, as these platforms offer clean room solutions to enable privacy-safe data collaboration with advertisers.

Technological advancements and the integration of artificial intelligence (AI) and machine learning (ML) into data clean rooms are also fueling market growth. Modern data clean room platforms are leveraging AI/ML to enhance data matching, automate compliance checks, and provide deeper analytics while ensuring privacy. This not only streamlines operations for enterprises but also unlocks new value from data sets that were previously inaccessible due to privacy concerns. As a result, organizations across sectors such as BFSI, healthcare, retail, and media are increasingly investing in data clean rooms to gain competitive advantage and drive innovation.

From a regional perspective, North America continues to dominate the data clean room market, accounting for the largest share in 2024 due to the presence of leading technology providers, early regulatory adoption, and a mature digital advertising ecosystem. However, Europe and the Asia Pacific regions are rapidly catching up, driven by stringent data privacy regulations and the digital transformation of key industries. Emerging markets in Latin America and the Middle East & Africa are also witnessing increased adoption, albeit at a slower pace, as enterprises in these regions begin to recognize the importance of secure data collaboration in the evolving digital economy.

Component Analysis

The data clean room market is segmented by component into software and services, each playing a distinct yet complementary role in the ecosystem. The software segment encompasses the core platforms and solutions that facilitate secure data collaboration, analytics, and privacy management. These platforms are designed to integrate seamlessly with existing enterp

According to Cognitive Market Research, the global Data Preparation Tools market size will be USD XX million in 2025. It will expand at a compound annual growth rate (CAGR) of XX% from 2025 to 2031.

North America held the major market share for more than XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Europe accounted for a market share of over XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Asia Pacific held a market share of around XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Latin America had a market share of more than XX% of the global revenue with a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. Middle East and Africa had a market share of around XX% of the global revenue and was estimated at a market size of USD XX million in 2025 and will grow at a CAGR of XX% from 2025 to 2031. KEY DRIVERS

Increasing Volume of Data and Growing Adoption of Business Intelligence (BI) and Analytics Driving the Data Preparation Tools Market

As organizations grow more data-driven, the integration of data preparation tools with Business Intelligence (BI) and advanced analytics platforms is becoming a critical driver of market growth. Clean, well-structured data is the foundation for accurate analysis, predictive modeling, and data visualization. Without proper preparation, even the most advanced BI tools may deliver misleading or incomplete insights. Businesses are now realizing that to fully capitalize on the capabilities of BI solutions such as Power BI, Qlik, or Looker, their data must first be meticulously prepared. Data preparation tools bridge this gap by transforming disparate raw data sources into harmonized, analysis-ready datasets. In the financial services sector, for example, firms use data preparation tools to consolidate customer financial records, transaction logs, and third-party market feeds to generate real-time risk assessments and portfolio analyses. The seamless integration of these tools with analytics platforms enhances organizational decision-making and contributes to the widespread adoption of such solutions. The integration of advanced technologies such as artificial intelligence (AI) and machine learning (ML) into data preparation tools has significantly improved their efficiency and functionality. These technologies automate complex tasks like anomaly detection, data profiling, semantic enrichment, and even the suggestion of optimal transformation paths based on patterns in historical data. AI-driven data preparation not only speeds up workflows but also reduces errors and human bias. In May 2022, Alteryx introduced AiDIN, a generative AI engine embedded into its analytics cloud platform. This innovation allows users to automate insights generation and produce dynamic documentation of business processes, revolutionizing how businesses interpret and share data. Similarly, platforms like DataRobot integrate ML models into the data preparation stage to improve the quality of predictions and outcomes. These innovations are positioning data preparation tools as not just utilities but as integral components of the broader AI ecosystem, thereby driving further market expansion. Data preparation tools address these needs by offering robust solutions for data cleaning, transformation, and integration, enabling telecom and IT firms to derive real-time insights. For example, Bharti Airtel, one of India’s largest telecom providers, implemented AI-based data preparation tools to streamline customer data and automate insights generation, thereby improving customer support and reducing operational costs. As major market players continue to expand and evolve their services, the demand for advanced data analytics powered by efficient data preparation tools will only intensify, propelling market growth. The exponential growth in global data generation is another major catalyst for the rise in demand for data preparation tools. As organizations adopt digital technologies and connected devices proliferate, the volume of data produced has surged beyond what traditional tools can handle. This deluge of information necessitates modern solutions capable of preparing vast and complex datasets efficiently. According to a report by the Lin...

Report Overview

The global Privacy Management Software market has become a vital sector in the technology landscape. With increasingly sophisticated cyber threats, organizations are investing heavily in advanced solutions. In 2023, the market value stood at USD 3.0 billion, and it is projected to soar to USD 83.7 billion by 2033, growing at an impressive CAGR of 39.50% between 2024 and 2033. This surge is fueled by the rapid adoption of digital transformation strategies, growing reliance on cloud infrastructure, and the ever-increasing risk of cyberattacks.

AI and ML are playing a pivotal role in automating privacy management processes. These technologies enable real-time data monitoring, identify compliance risks, and offer predictive insights to mitigate potential breaches. For instance, AI-based solutions can now detect anomalies in large data sets, improving compliance efficiency. By 2024, over 40% of privacy management tools will incorporate AI-driven analytics.

With regulations such as GDPR, CCPA, and China's Personal Information Protection Law (PIPL), companies are prioritizing consumer rights like data portability, the right to be forgotten, and opt-out preferences. Privacy management solutions are increasingly equipped with features to address these rights efficiently. For example, the demand for data subject access request (DSAR) management tools has surged by nearly 35% annually.

Privacy management software is being integrated with broader cybersecurity platforms to create unified solutions. This integration helps companies streamline compliance while protecting data from unauthorized access. Gartner predicts that by 2025, 60% of the privacy management software market will be bundled with cybersecurity suites to address overlapping challenges.

Industries like healthcare, finance, and e-commerce are seeing tailored privacy management solutions that cater to specific compliance needs. For example, healthcare providers are adopting tools to meet HIPAA compliance, while financial institutions are leveraging software that ensures data security in line with GDPR and PSD2 regulations.

Organizations are increasingly concerned about the data shared with third-party vendors. Privacy management tools now include third-party risk assessment capabilities to evaluate vendor compliance with privacy standards. According to a recent survey, 55% of organizations implemented third-party risk management in 2023, a figure expected to grow significantly in 2024.

As businesses migrate to cloud environments, cloud-based privacy management software is becoming a preferred choice due to its scalability and ease of integration. Currently, 67% of businesses prefer cloud-based solutions, a number anticipated to grow as remote work and digital transformation expand.

Governments worldwide are enforcing data localization rules, requiring businesses to store user data within specific geographic boundaries. Privacy management tools now offer features to ensure compliance with such laws, enabling organizations to align with region-specific data storage requirements.

To meet growing consumer expectations, organizations are deploying privacy dashboards that allow users to view, manage, and delete their data. These dashboards are becoming a standard feature, with 30% of companies globally adopting them in 2023 to improve transparency.

Organizatio...

Programmatic Audio Advertising Market Outlook

According to our latest research, the global Programmatic Audio Advertising market size reached USD 7.2 billion in 2024, reflecting robust momentum in digital advertising innovation. The market is projected to expand at a CAGR of 13.7% from 2025 to 2033, reaching an estimated USD 22.4 billion by 2033. This remarkable growth is primarily driven by the increasing adoption of digital audio platforms, the proliferation of connected devices, and the rising demand for data-driven, targeted advertising strategies.

The primary growth factor propelling the programmatic audio advertising market is the explosive rise in digital audio consumption. Over the past few years, consumers have rapidly shifted toward streaming music, podcasts, and radio through platforms such as Spotify, Apple Music, and Pandora. This shift has created a fertile environment for advertisers to engage audiences through dynamic, personalized audio ads. The integration of advanced data analytics and artificial intelligence has further enhanced advertisers’ ability to target specific demographics, behaviors, and preferences, making programmatic audio advertising a highly efficient channel for reaching on-the-go consumers. The seamless, non-intrusive nature of audio advertising also contributes to higher engagement rates compared to traditional display or video ads, fueling sustained market demand.

Another significant driver is the evolution of ad formats and the expansion of device ecosystems. The emergence of smart speakers, voice assistants, and connected cars has introduced new touchpoints for programmatic audio ads, broadening the scope beyond traditional mobile and desktop channels. Advertisers are increasingly leveraging innovative ad formats such as voice-activated ads and interactive audio spots to capture listener attention and drive measurable actions. These advancements have enabled marketers to deliver contextually relevant messages in real time, maximizing the impact of their campaigns while optimizing ad spend. Furthermore, the scalability and automation offered by programmatic platforms reduce manual intervention, streamline operations, and enhance ROI for brands across various industries.

The programmatic audio advertising market also benefits from the growing emphasis on privacy and brand safety. With tightening regulations around data usage and third-party cookies, advertisers are seeking alternative channels that offer both compliance and effectiveness. Audio platforms typically rely on first-party data and contextual targeting, minimizing privacy risks while maintaining high levels of personalization. This has made programmatic audio an attractive option for brands looking to future-proof their advertising strategies. Additionally, the ability to measure campaign performance in real time and adjust parameters dynamically ensures that advertisers can achieve their marketing objectives without compromising user trust or regulatory compliance.

Regionally, North America continues to dominate the programmatic audio advertising landscape, accounting for the largest market share in 2024. The region’s advanced digital infrastructure, widespread adoption of streaming services, and strong presence of leading audio tech companies have established it as a hub for programmatic innovation. Europe and Asia Pacific are also witnessing rapid growth, driven by increasing smartphone penetration, expanding internet access, and rising investments in digital advertising technologies. The Middle East & Africa and Latin America, while still emerging, present significant untapped potential as local advertisers embrace digital transformation and global platforms expand their reach into these regions.

Ad Format Analysis

The ad format segment of the programmatic audio advertising market is highly diverse, reflecting the rapidly evolving preferences of digital audiences. In-stream ads remain the most dominant format, leveraging the popularity of music streamin

Data Management Platform (DMP) Market Outlook

According to our latest research, the global Data Management Platform (DMP) market size reached USD 3.72 billion in 2024. The market is demonstrating robust expansion, driven by escalating data-driven marketing initiatives and increasing digital transformation across industries. The DMP market is forecasted to grow at a CAGR of 13.8% from 2025 to 2033, reaching a projected value of USD 11.14 billion by 2033. This remarkable growth is primarily fueled by the rising demand for actionable customer insights, the proliferation of digital channels, and the continuous evolution of regulatory landscapes impacting data privacy and management.

The primary growth factor propelling the Data Management Platform (DMP) market is the surge in data generation from various digital touchpoints. Organizations across sectors are increasingly leveraging DMP solutions to aggregate, segment, and analyze vast datasets derived from web, mobile, CRM, and offline sources. This enables them to gain a 360-degree view of customer behavior, preferences, and engagement patterns. The heightened focus on delivering personalized customer experiences, optimizing advertising spend, and enhancing campaign effectiveness has led to a widespread adoption of DMPs. Moreover, the integration of artificial intelligence and machine learning within DMPs has further amplified their capability to deliver predictive analytics and real-time insights, positioning them as an indispensable tool in the modern marketing and data analytics ecosystem.

Another significant driver for the Data Management Platform market is the increasing emphasis on compliance with data privacy regulations such as GDPR, CCPA, and other regional frameworks. As organizations navigate complex regulatory environments, the need for robust data governance, consent management, and transparent data processing has never been more critical. DMPs are evolving to incorporate advanced privacy controls, consent management modules, and secure data handling practices, enabling businesses to maintain customer trust while ensuring regulatory adherence. This evolution is not only fostering market growth but also redefining the competitive landscape, with vendors differentiating themselves through compliance-centric features and secure data architectures.

The rapid adoption of cloud-based DMP solutions is another key element catalyzing market expansion. Cloud deployment offers unparalleled scalability, flexibility, and cost-effectiveness, making DMPs accessible to a broader spectrum of organizations, including small and medium enterprises. The proliferation of cloud infrastructure, coupled with advancements in API integrations and interoperability, has significantly reduced the time-to-market for DMP implementations. Additionally, the growing trend of omnichannel marketing and the convergence of first-party, second-party, and third-party data sources are compelling organizations to invest in sophisticated DMPs that can seamlessly manage and unify disparate data streams. As a result, the DMP market is witnessing accelerated adoption across various industry verticals, including retail, BFSI, healthcare, and media & entertainment.

Regionally, North America continues to dominate the global Data Management Platform (DMP) market, accounting for the largest share in 2024. The region’s leadership is attributed to the presence of major technology vendors, advanced digital infrastructure, and a mature advertising ecosystem. However, the Asia Pacific region is emerging as the fastest-growing market, driven by rapid digitalization, increasing internet penetration, and the burgeoning e-commerce sector. Europe also holds a significant share, bolstered by stringent data privacy regulations and high adoption rates among enterprises. Latin America and the Middle East & Africa are witnessing steady growth, supported by increasing investments in digital transformation and data-driven marketing initiatives.

Component Analysis

The Data Mana

According to Cognitive Market Research, The Global market for Information Services was USD 140.9 billion in 2022 and will grow at a 7.80% CAGR from 2023 to 2030. Market Dynamics of

Information Services Market

Key Drivers for

Information Services Market

Data generation is expanding exponentially: The digital transformation across industries has produced massive quantities of structured and unstructured data, which has increased the need for data processing and analytics services. Information services are essential for organizations to extract practical knowledge from huge datasets. Cloud computing supports real-time analysis and scalable data storage. Risk management and regulatory compliance needs: Businesses are now compelled to use specialized information services due to increased data privacy legislation (GDPR, CCPA) and financial reporting standards. Demand for compliance is driven by industries such as healthcare, finance, and the law. Third-party providers are knowledgeable about how regulations are changing. Integration of AI and automation: The speed and correctness of information services are increased by the integration of sophisticated analytics, machine learning, and natural language processing. Automated data curation and predictive modeling lessen manual labor while enhancing decision-making.

Key Restraints for

Information Services Market

Worries about data security and privacy: High-profile breaches and misuse of personal data undermine consumer trust in information service companies. High operational costs result from stringent cybersecurity safeguards and encryption protocols. Cross-border data transfer limitations make it harder to provide services globally. Market fragmentation and strong competition: Low entry barriers for simple data services result in oversaturation in some areas. As suppliers compete on price rather than value-added features, differentiation becomes more difficult. Reliance on third-party data sources: The dependability of services is impacted by the inconsistent data quality from outside vendors. Proprietary datasets' licensing fees lower the profit margins of information service companies

Key Trends for

Information Services Market

Specific industry-specific solutions: Targeted niche information services for sectors like healthcare (clinical trial data) or supply chain (IoT sensor analytics) are gaining popularity. A higher-value knowledge is produced by combining domain expertise with data science. Real-time data delivery: switch from static reports to dynamic dashboards and streaming analytics. Edge computing allows for quicker processing for time-sensitive applications like financial trading or fraud detection. Ethical AI and open data sourcing: Increasingly, socially conscious firms are asking for auditable algorithms and unbiased datasets. Providers are implementing fair data acquisition strategies and explainable AI frameworks Introduction of Information Services

Information systems are a collection of interconnected components that are used to capture, process, save, and disseminate various sorts of data for people to view and utilize. Businesses and consumers can choose from a variety of services offered by the information services market. These services might range from analytics tools and cloud-based storage to data management services and cybersecurity solutions. The market is being driven by an increase in the demand for these services as businesses search for fresh ways to use technology to spur development and innovation.

For instance, Amazon Web Services (AWS) offers a variety of cloud-based services, such as data storage and analysis tools. AWS provides a number of storage solutions, such as object storage, block storage, and file storage, as well as data analysis and machine learning capabilities. These services enable businesses to store and analyze massive volumes of data in the cloud, making it more accessible and usable for a wide range of applications.

(Source: docs.aws.amazon.com/whitepapers/latest/aws-overview/storage-services.html)