Quadrant provides Insightful, accurate, and reliable mobile location data.

Our privacy-first mobile location data unveils hidden patterns and opportunities, provides actionable insights, and fuels data-driven decision-making at the world's biggest companies.

These companies rely on our privacy-first Mobile Location and Points-of-Interest Data to unveil hidden patterns and opportunities, provide actionable insights, and fuel data-driven decision-making. They build better AI models, uncover business insights, and enable location-based services using our robust and reliable real-world data.

We conduct stringent evaluations on data providers to ensure authenticity and quality. Our proprietary algorithms detect, and cleanse corrupted and duplicated data points – allowing you to leverage our datasets rapidly with minimal processing or cleaning. During the ingestion process, our proprietary Data Filtering Algorithms remove events based on a number of both qualitative factors, as well as latency and other integrity variables to provide more efficient data delivery. The deduplicating algorithm focuses on a combination of four important attributes: Device ID, Latitude, Longitude, and Timestamp. This algorithm scours our data and identifies rows that contain the same combination of these four attributes. Post-identification, it retains a single copy and eliminates duplicate values to ensure our customers only receive complete and unique datasets.

We actively identify overlapping values at the provider level to determine the value each offers. Our data science team has developed a sophisticated overlap analysis model that helps us maintain a high-quality data feed by qualifying providers based on unique data values rather than volumes alone – measures that provide significant benefit to our end-use partners.

Quadrant mobility data contains all standard attributes such as Device ID, Latitude, Longitude, Timestamp, Horizontal Accuracy, and IP Address, and non-standard attributes such as Geohash and H3. In addition, we have historical data available back through 2022.

Through our in-house data science team, we offer sophisticated technical documentation, location data algorithms, and queries that help data buyers get a head start on their analyses. Our goal is to provide you with data that is “fit for purpose”.

The global market for Document Duplication Detection Software is experiencing robust growth, driven by the increasing need for efficient data management and enhanced security across various industries. The rising volume of digital documents, coupled with stricter regulatory compliance requirements (like GDPR and CCPA), is fueling the demand for solutions that can quickly and accurately identify duplicate files. This reduces storage costs, improves data quality, and minimizes the risk of data breaches. The market's expansion is further propelled by advancements in artificial intelligence (AI) and machine learning (ML) technologies, which enable more sophisticated and accurate duplicate detection. We estimate the current market size to be around $800 million in 2025, with a Compound Annual Growth Rate (CAGR) of 15% projected through 2033. This growth is expected across various segments, including cloud-based and on-premise solutions, catering to diverse industry verticals such as legal, finance, healthcare, and government. Major players like Microsoft, IBM, and Oracle are contributing to market growth through their established enterprise solutions. However, the market also features several specialized players, like Hyper Labs and Auslogics, offering niche solutions catering to specific needs. While the increasing adoption of cloud-based solutions is a key trend, potential restraints include the initial investment costs for software implementation and the need for ongoing training and support. The integration challenges with existing systems and the potential for false positives can also impede wider adoption. The market's regional distribution is expected to see a significant contribution from North America and Europe, while the Asia-Pacific region is projected to exhibit substantial growth potential driven by increasing digitalization. The forecast period (2025-2033) presents significant opportunities for market expansion, driven by technological innovation and the growing awareness of data management best practices.

Data Deduplication Software Market Outlook

The global data deduplication software market size was valued at approximately USD 2.5 billion in 2023 and is expected to reach USD 6.8 billion by 2032, growing at a compound annual growth rate (CAGR) of 11.5% during the forecast period. One of the primary growth factors driving this market is the increasing volume of data generated across various industry verticals, necessitating efficient data management solutions to reduce storage costs and enhance data processing efficiency.

The phenomenal growth in data generation is primarily attributed to the proliferation of digital technologies and the surge in internet usage. Organizations are producing massive volumes of data from diverse sources such as social media, IoT devices, transaction records, and more. This exponential data growth demands robust data management and storage solutions, making data deduplication software indispensable. By eliminating redundant data, these software solutions significantly optimize storage requirements, thereby reducing costs and improving overall data management efficiency.

Another significant growth factor is the increasing adoption of cloud computing. Organizations are increasingly migrating their data storage and processing needs to cloud platforms due to their scalability, flexibility, and cost-effectiveness. Data deduplication is particularly crucial in cloud environments as it helps in minimizing storage requirements and optimizing bandwidth usage, leading to cost savings and enhanced performance. As businesses continue to leverage cloud technologies, the demand for efficient data deduplication solutions is expected to rise correspondingly.

The rising importance of data privacy and security is also fueling the demand for data deduplication software. With stringent data protection regulations such as GDPR and CCPA coming into play, organizations are required to manage and secure their data more rigorously. Data deduplication helps in maintaining clean, non-redundant data sets, which simplifies data governance and compliance management. Additionally, deduplicated data is easier to encrypt and monitor, thereby enhancing overall data security.

In the realm of data management, Big Data Replication Software plays a pivotal role in ensuring data consistency and availability across multiple platforms. As organizations increasingly rely on vast amounts of data for decision-making and operational efficiency, the ability to replicate data accurately becomes crucial. This software facilitates seamless data replication, allowing businesses to maintain up-to-date copies of their data across different locations. By doing so, it not only enhances data reliability but also supports disaster recovery and business continuity efforts. The integration of Big Data Replication Software with existing data management systems can significantly streamline data operations, providing organizations with the agility needed to respond to dynamic market conditions.

Regionally, North America holds a significant share in the data deduplication software market, owing to the early adoption of advanced technologies and the presence of major cloud service providers. However, the Asia Pacific region is anticipated to exhibit the highest growth rate during the forecast period. This can be attributed to the rapid digital transformation, increasing adoption of cloud services, and the growing number of small and medium enterprises in the region.

Component Analysis

The data deduplication software market is segmented into software and services. The software segment dominates the market due to the high demand for advanced data management solutions that can efficiently handle large volumes of data. These software solutions are equipped with sophisticated algorithms that can identify and eliminate duplicate data across various storage environments, thereby optimizing storage utilization and improving data processing efficiency. Additionally, the continuous advancements in software capabilities, such as integration with cloud platforms and support for real-time data processing, are further driving the growth of this segment.

Within the software segment, standalone data deduplication software and integrated data deduplication solutions are the primary sub-segments. Standalone software is designed to work independently, providing deduplication capabilities without the need for additional software or hardware componen

OpenCon 2015 Application Open Data

The purpose of this document is to accompany the public release of data collected from OpenCon 2015 applications.Download & Technical Information The data can be downloaded in CSV format from GitHub here: https://github.com/RightToResearch/OpenCon-2015-Application-Data The file uses UTF8 encoding, comma as field delimiter, quotation marks as text delimiter, and no byte order mark.

License and Requests

This data is released to the public for free and open use under a CC0 1.0 license. We have a couple of requests for anyone who uses the data. First, we’d love it if you would let us know what you are doing with it, and share back anything you develop with the OpenCon community (#opencon / @open_con ). Second, it would also be great if you would include a link to the OpenCon 2015 website (www.opencon2015.org) wherever the data is used. You are not obligated to do any of this, but we’d appreciate it!

Data Fields

Unique ID

This is a unique ID assigned to each applicant. Numbers were assigned using a random number generator.

Timestamp

This was the timestamp recorded by google forms. Timestamps are in EDT (Eastern U.S. Daylight Time). Note that the application process officially began at 1:00pm EDT June 1 ended at 6:00am EDT on June 23. Some applications have timestamps later than this date, and this is due to a variety of reasons including exceptions granted for technical difficulties, error corrections (which required re-submitting the form), and applications sent in via email and later entered manually into the form. [a]

Gender

Mandatory. Choose one from list or fill-in other. Options provided: Male, Female, Other (fill in).

Country of Nationality

Mandatory. Choose one option from list.

Country of Residence

Mandatory. Choose one option from list.

What is your primary occupation?

Mandatory. Choose one from list or fill-in other. Options provided: Undergraduate student; Masters/professional student; PhD candidate; Faculty/teacher; Researcher (non-faculty); Librarian; Publisher; Professional advocate; Civil servant / government employee; Journalist; Doctor / medical professional; Lawyer; Other (fill in).

Select the option below that best describes your field of study or expertise

Mandatory. Choose one option from list.

What is your primary area of interest within OpenCon’s program areas?

Mandatory. Choose one option from list. Note: for the first approximately 24 hours the options were listed in this order: Open Access, Open Education, Open Data. After that point, we set the form to randomize the order, and noticed an immediate shift in the distribution of responses.

Are you currently engaged in activities to advance Open Access, Open Education, and/or Open Data?

Mandatory. Choose one option from list.

Are you planning to participate in any of the following events this year?

Optional. Choose all that apply from list. Multiple selections separated by semi-colon.

Do you have any of the following skills or interests?

Mandatory. Choose all that apply from list or fill-in other. Multiple selections separated by semi-colon. Options provided: Coding; Website Management / Design; Graphic Design; Video Editing; Community / Grassroots Organizing; Social Media Campaigns; Fundraising; Communications and Media; Blogging; Advocacy and Policy; Event Logistics; Volunteer Management; Research about OpenCon's Issue Areas; Other (fill-in).

Data Collection & Cleaning

This data consists of information collected from people who applied to attend OpenCon 2015. In the application form, questions that would be released as Open Data were marked with a caret (^) and applicants were asked to acknowledge before submitting the form that they understood that their responses to these questions would be released as such. The questions we released were selected to avoid any potentially sensitive personal information, and to minimize the chances that any individual applicant can be positively identified. Applications were formally collected during a 22 day period beginning on June 1, 2015 at 13:00 EDT and ending on June 23 at 06:00 EDT. Some applications have timestamps later than this date, and this is due to a variety of reasons including exceptions granted for technical difficulties, error corrections (which required re-submitting the form), and applications sent in via email and later entered manually into the form. Applications were collected using a Google Form embedded at http://www.opencon2015.org/attend, and the shortened bit.ly link http://bit.ly/AppsAreOpen was promoted through social media. The primary work we did to clean the data focused on identifying and eliminating duplicates. We removed all duplicate applications that had matching e-mail addresses and first and last names. We also identified a handful of other duplicates that used different e-mail addresses but were otherwise identical. In cases where duplicate applications contained any different information, we kept the information from the version with the most recent timestamp. We made a few minor adjustments in the country field for cases where the entry was obviously an error (for example, electing a country listed alphabetically above or below the one indicated elsewhere in the application). We also removed one potentially offensive comment (which did not contain an answer to the question) from the Gender field and replaced it with “Other.”

About OpenCon

OpenCon 2015 is the student and early career academic professional conference on Open Access, Open Education, and Open Data and will be held on November 14-16, 2015 in Brussels, Belgium. It is organized by the Right to Research Coalition, SPARC (The Scholarly Publishing and Academic Resources Coalition), and an Organizing Committee of students and early career researchers from around the world. The meeting will convene students and early career academic professionals from around the world and serve as a powerful catalyst for projects led by the next generation to advance OpenCon's three focus areas—Open Access, Open Education, and Open Data. A unique aspect of OpenCon is that attendance at the conference is by application only, and the majority of participants who apply are awarded travel scholarships to attend. This model creates a unique conference environment where the most dedicated and impactful advocates can attend, regardless of where in the world they live or their access to travel funding. The purpose of the application process is to conduct these selections fairly. This year we were overwhelmed by the quantity and quality of applications received, and we hope that by sharing this data, we can better understand the OpenCon community and the state of student and early career participation in the Open Access, Open Education, and Open Data movements.

Questions

For inquires about the OpenCon 2015 Application data, please contact Nicole Allen at nicole@sparc.arl.org.

Data Deduplication Tools Market Outlook

The data deduplication tools market is experiencing a robust growth trajectory, with the global market size anticipated to reach approximately USD 5.7 billion by 2032, up from USD 2.3 billion in 2023, reflecting a compound annual growth rate (CAGR) of 10.9% during the forecast period. This significant expansion is driven by the increasing need for efficient data management solutions in various industries, which is further augmented by the exponential growth of data generation across the globe. The proliferation of digital content, coupled with the rising adoption of cloud-based solutions, is playing a critical role in advancing the market's growth.

One of the primary growth factors for the data deduplication tools market is the escalating volume of digital data generated by enterprises and individuals alike. Organizations are witnessing an unprecedented surge in data creation due to the proliferation of digital technologies, IoT devices, and enhanced network connectivity. This surge necessitates effective data storage and management solutions to reduce redundancy and optimize storage costs. As businesses aim to maximize their IT infrastructure efficiency, data deduplication tools offer a cost-effective means to eliminate duplicate data, thus freeing up valuable storage space and enhancing data retrieval times. The demand for these tools is further accentuated by the financial implications of data storage, as businesses seek to mitigate the costs associated with purchasing additional storage hardware.

The adoption of cloud computing is another pivotal factor propelling the growth of the data deduplication tools market. As enterprises increasingly migrate their data and applications to cloud environments, the need for data deduplication becomes more pronounced to ensure efficient storage utilization and cost savings. Cloud service providers are integrating deduplication capabilities into their offerings, allowing clients to manage their data more effectively and reduce unnecessary storage expenses. This trend is driving the adoption of data deduplication tools across various sectors, including BFSI, healthcare, and IT, where large volumes of data are routinely processed and stored. The growing reliance on cloud solutions underscores the importance of deduplication tools in modern data management strategies.

Moreover, the evolving regulatory landscape concerning data protection and privacy is contributing to the market's expansion. Organizations are under increasing pressure to comply with stringent data regulations such as GDPR, which mandate the efficient management and protection of personal data. Data deduplication tools play a crucial role in helping businesses adhere to these regulations by ensuring the integrity and accuracy of stored data while minimizing redundancy. This regulatory impetus, combined with the strategic importance of data management in achieving competitive advantage, is spurring investment in deduplication solutions. Consequently, businesses across different industries are prioritizing the adoption of these tools to enhance data quality, security, and compliance.

Regionally, North America is expected to dominate the data deduplication tools market, driven by the presence of a high concentration of technology enterprises and significant investment in IT infrastructure. The region's early adoption of advanced technologies and favorable regulatory environment further support market growth. Europe, with its stringent data protection regulations and focus on data accuracy, also represents a significant market for deduplication solutions. The Asia Pacific region is anticipated to witness the highest growth rate, attributed to the rapid digital transformation across emerging economies, increasing cloud adoption, and growing awareness of data management solutions. The Middle East & Africa and Latin America are also expected to contribute to market growth, albeit at a more moderate pace, as organizations in these regions begin to recognize the benefits of data deduplication in optimizing IT operations.

As organizations continue to grapple with the complexities of managing vast amounts of data, the role of a Data Versioning Tool becomes increasingly critical. These tools provide a systematic approach to managing data changes over time, ensuring that organizations can track, manage, and revert to previous data states if necessary. This capability is particularly valuable in environments where data integrity and consistency are paramount, such as in software deve

Quadrant provides Insightful, accurate, and reliable mobile location data.

Our privacy-first mobile location data unveils hidden patterns and opportunities, provides actionable insights, and fuels data-driven decision-making at the world's biggest companies.

These companies rely on our privacy-first Mobile Location and Points-of-Interest Data to unveil hidden patterns and opportunities, provide actionable insights, and fuel data-driven decision-making. They build better AI models, uncover business insights, and enable location-based services using our robust and reliable real-world data.

We conduct stringent evaluations on data providers to ensure authenticity and quality. Our proprietary algorithms detect, and cleanse corrupted and duplicated data points – allowing you to leverage our datasets rapidly with minimal processing or cleaning. During the ingestion process, our proprietary Data Filtering Algorithms remove events based on a number of both qualitative factors, as well as latency and other integrity variables to provide more efficient data delivery. The deduplicating algorithm focuses on a combination of four important attributes: Device ID, Latitude, Longitude, and Timestamp. This algorithm scours our data and identifies rows that contain the same combination of these four attributes. Post-identification, it retains a single copy and eliminates duplicate values to ensure our customers only receive complete and unique datasets.

We actively identify overlapping values at the provider level to determine the value each offers. Our data science team has developed a sophisticated overlap analysis model that helps us maintain a high-quality data feed by qualifying providers based on unique data values rather than volumes alone – measures that provide significant benefit to our end-use partners.

Quadrant mobility data contains all standard attributes such as Device ID, Latitude, Longitude, Timestamp, Horizontal Accuracy, and IP Address, and non-standard attributes such as Geohash and H3. In addition, we have historical data available back through 2022.

Through our in-house data science team, we offer sophisticated technical documentation, location data algorithms, and queries that help data buyers get a head start on their analyses. Our goal is to provide you with data that is “fit for purpose”.

Snapshot img

Customer Information System Market Size 2024-2028

The customer information system market size is forecast to increase by USD 360.2 million at a CAGR of 7.1% between 2023 and 2028.

The Customer Information System (CIS) market is experiencing significant growth, driven by the increasing demand for cloud-based solutions. Businesses are recognizing the benefits of cloud-based CIS, including cost savings, scalability, and flexibility. Additionally, the integration of analytics with CIS is a key trend, enabling organizations to gain valuable insights from their customer data and improve operational efficiency. However, the market is not without challenges. Data security concerns and the threat of cyberattacks continue to pose significant risks, necessitating security measures and compliance with data protection regulations. Companies seeking to capitalize on market opportunities must prioritize innovation, invest in advanced security solutions, and offer seamless integration with other business systems. Navigating these challenges effectively will require strategic planning, operational agility, and a customer-centric approach.

What will be the Size of the Customer Information System Market during the forecast period?

Request Free SampleIn today's business landscape, a customer information system plays a pivotal role in managing and enhancing customer interactions. Multi-channel interaction through sales personnel and support services is essential to meet the diverse needs of clients. Real-time analytics and integration with IoT devices enable businesses to gain valuable insights from customer communications channels. Digital technology, such as CRM solutions and cloud services, streamline processes and improve customer satisfaction by ensuring data availability. IT infrastructure and utility meter-to-cash solutions facilitate access controls and automation, reducing response times and errors from duplicate data entries. Smart meters and analytical support provide real-time data processing and AI capabilities, enhancing data security and protection. The adoption of digital technologies continues to evolve, with energy consumption and data security becoming increasingly important considerations. Incorporating IoT devices and access controls into customer information systems allows businesses to optimize their operations and maintain a competitive edge. By focusing on data availability, customer response times, and data protection, organizations can build strong customer relationships and drive growth.

How is this Customer Information System Industry segmented?

The customer information system industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments. DeploymentCloudOn-premisesGeographyNorth AmericaUSAPACChinaJapanEuropeGermanyUKSouth AmericaMiddle East and Africa

By Deployment Insights

The cloud segment is estimated to witness significant growth during the forecast period.Digital technology and cloud solutions are revolutionizing the Customer Information System (CIS) market. Cloud-based CIS eliminates the need for on-premises hardware and software installation, offering enterprises subscription-based access from the company's data center. This model reduces implementation costs and allows for quicker return on investment. Additionally, cloud-based systems provide real-time data availability, enabling efficient multi-channel interaction and customer response times. Artificial Intelligence (AI) and Internet of Things (IoT) Devices are transforming CIS through smart metering, leak detection, and utility meter-to-cash processes. Smart Grids and Smart Meters enable real-time analytics, automating utility gas management and inventory control. Access Controls ensure data protection, while CRM solutions integrate consumer information management and client communications channels. Data management programs facilitate inventory control and analytical support, enabling businesses to make data-driven decisions. Blockchain technology ensures data security and eliminates duplicate data entries. Real-time databases and analytics offer insights into energy consumption patterns, enhancing productivity and profitability. IT Infrastructure and Data Security are crucial components of CIS, ensuring customer satisfaction and information system solutions. Implementation services ensure smooth system integration, while Data Protection regulations maintain data privacy and security. The retail sector is a significant adopter of these advanced CIS solutions, driving market growth.

Get a glance at the market report of share of various segments Request Free Sample

The Cloud segment was valued at USD 369.10 million in 2018 and showed a gradual increase during the forecast period.

Regio

The market for duplicate contact remover apps is experiencing robust growth, driven by the increasing use of smartphones and multiple social media accounts, leading to a proliferation of duplicate contacts across various devices. The market's expansion is fueled by the rising need for efficient contact management, particularly among professionals and individuals managing large contact lists. Businesses are increasingly adopting these apps to streamline their operations and improve data quality, leading to higher productivity and reduced administrative burdens. User demand for seamless data synchronization across platforms and enhanced privacy features further contributes to market expansion. While the exact market size for 2025 is unavailable, a reasonable estimation based on typical growth rates in similar software markets would place it within the range of $150-$200 million. Considering a conservative Compound Annual Growth Rate (CAGR) of 15% for the forecast period (2025-2033), we project substantial growth, reaching a potential market value of $600-$800 million by 2033. This growth trajectory is expected despite potential restraints like the availability of built-in contact management features in operating systems and the apprehension of users regarding data privacy and security related to third-party apps. The competitive landscape is relatively fragmented, with several key players vying for market share. Companies like ActivePrime, Compelson Labs, Systweak Software, and others offer a range of features, from basic duplicate detection to advanced functionalities like merging and deduplication across multiple accounts. Future growth will depend on the ability of these companies to innovate and offer unique value propositions, focusing on features like AI-powered contact organization, improved user interfaces, and enhanced integration with other productivity apps. Geographical expansion, particularly into emerging markets with a growing smartphone user base, will be a crucial factor in driving future revenue. The segment most likely to experience the strongest growth will be the enterprise segment, given the need for improved data management in large organizations. Marketing efforts focusing on the benefits of improved contact management, data accuracy, and time savings are key for success in this market.

Quadrant provides Insightful, accurate, and reliable mobile location data.

Our privacy-first mobile location data unveils hidden patterns and opportunities, provides actionable insights, and fuels data-driven decision-making at the world's biggest companies.

These companies rely on our privacy-first Mobile Location and Points-of-Interest Data to unveil hidden patterns and opportunities, provide actionable insights, and fuel data-driven decision-making. They build better AI models, uncover business insights, and enable location-based services using our robust and reliable real-world data.

We conduct stringent evaluations on data providers to ensure authenticity and quality. Our proprietary algorithms detect, and cleanse corrupted and duplicated data points – allowing you to leverage our datasets rapidly with minimal processing or cleaning. During the ingestion process, our proprietary Data Filtering Algorithms remove events based on a number of both qualitative factors, as well as latency and other integrity variables to provide more efficient data delivery. The deduplicating algorithm focuses on a combination of four important attributes: Device ID, Latitude, Longitude, and Timestamp. This algorithm scours our data and identifies rows that contain the same combination of these four attributes. Post-identification, it retains a single copy and eliminates duplicate values to ensure our customers only receive complete and unique datasets.

We actively identify overlapping values at the provider level to determine the value each offers. Our data science team has developed a sophisticated overlap analysis model that helps us maintain a high-quality data feed by qualifying providers based on unique data values rather than volumes alone – measures that provide significant benefit to our end-use partners.

Quadrant mobility data contains all standard attributes such as Device ID, Latitude, Longitude, Timestamp, Horizontal Accuracy, and IP Address, and non-standard attributes such as Geohash and H3. In addition, we have historical data available back through 2022.

Through our in-house data science team, we offer sophisticated technical documentation, location data algorithms, and queries that help data buyers get a head start on their analyses. Our goal is to provide you with data that is “fit for purpose”.

Context

This notebook serves to showcase my problem solving ability, knowledge of the data analysis process, proficiency with Excel and its various tools and functions, as well as my strategic mindset and statistical prowess. This project consist of an auditing prompt provided by Hive Data, a raw Excel data set, a cleaned and audited version of the raw Excel data set, and my description of my thought process and knowledge used during completion of the project. The prompt can be found below:

Hive Data Audit Prompt

The raw data that accompanies the prompt can be found below:

Hive Annotation Job Results - Raw Data

^ These are the tools I was given to complete my task. The rest of the work is entirely my own.

To summarize broadly, my task was to audit the dataset and summarize my process and results. Specifically, I was to create a method for identifying which "jobs" - explained in the prompt above - needed to be rerun based on a set of "background facts," or criteria. The description of my extensive thought process and results can be found below in the Content section.

Content

Brendan Kelley April 23, 2021

Hive Data Audit Prompt Results

This paper explains the auditing process of the “Hive Annotation Job Results” data. It includes the preparation, analysis, visualization, and summary of the data. It is accompanied by the results of the audit in the excel file “Hive Annotation Job Results – Audited”.

Observation

The “Hive Annotation Job Results” data comes in the form of a single excel sheet. It contains 7 columns and 5,001 rows, including column headers. The data includes “file”, “object id”, and the pseudonym for five questions that each client was instructed to answer about their respective table: “tabular”, “semantic”, “definition list”, “header row”, and “header column”. The “file” column includes non-unique (that is, there are multiple instances of the same value in the column) numbers separated by a dash. The “object id” column includes non-unique numbers ranging from 5 to 487539. The columns containing the answers to the five questions include Boolean values - TRUE or FALSE – which depend upon the yes/no worker judgement.

Use of the COUNTIF() function reveals that there are no values other than TRUE or FALSE in any of the five question columns. The VLOOKUP() function reveals that the data does not include any missing values in any of the cells.

Assumptions

Based on the clean state of the data and the guidelines of the Hive Data Audit Prompt, the assumption is that duplicate values in the “file” column are acceptable and should not be removed. Similarly, duplicated values in the “object id” column are acceptable and should not be removed. The data is therefore clean and is ready for analysis/auditing.

Preparation

The purpose of the audit is to analyze the accuracy of the yes/no worker judgement of each question according to the guidelines of the background facts. The background facts are as follows:

• A table that is a definition list should automatically be tabular and also semantic • Semantic tables should automatically be tabular • If a table is NOT tabular, then it is definitely not semantic nor a definition list • A tabular table that has a header row OR header column should definitely be semantic

These background facts serve as instructions for how the answers to the five questions should interact with one another. These facts can be re-written to establish criteria for each question:

For tabular column: - If the table is a definition list, it is also tabular - If the table is semantic, it is also tabular

For semantic column: - If the table is a definition list, it is also semantic - If the table is not tabular, it is not semantic - If the table is tabular and has either a header row or a header column...

Duplicate Contact Remover Apps Market Outlook

The global market size for duplicate contact remover apps is poised to experience significant growth, with an estimated valuation of $1.2 billion in 2023, projected to reach $2.8 billion by 2032, reflecting a robust CAGR of 9.5%. The primary growth factors driving this market include the increased adoption of smartphones, the proliferation of digital communication platforms, and the rising demand for efficient contact management solutions to streamline personal and professional communication.

The growth of the duplicate contact remover apps market is propelled largely by the increasing penetration of smartphones across the globe. As smartphones become more integral to daily life, managing contacts efficiently is crucial for both individual and enterprise users. Duplicate contacts can cause confusion, hinder effective communication, and lead to data inconsistency. Hence, there is a growing need for applications that can automatically identify and remove redundant contact entries, ensuring a seamless user experience. Furthermore, the rise in digital communication tools and social media platforms, which often result in multiple entries for the same contact, also contributes to the demand for such apps.

Another significant growth driver is the increasing awareness and emphasis on data cleanliness and accuracy. In an era where data is considered the new oil, maintaining accurate and clean contact databases is vital for effective communication and business operations. Duplicate contacts can lead to miscommunication, missed opportunities, and inefficiencies in customer relationship management (CRM) systems. Businesses are increasingly recognizing the importance of maintaining a clean contact database for improved operational efficiency, driving the adoption of duplicate contact remover apps. Additionally, advancements in AI and machine learning technologies enhance the capabilities of these apps, making them more efficient in identifying and merging duplicate entries.

The surge in remote work and the digital transformation of businesses further fuel the need for effective contact management solutions. With employees working from various locations and relying heavily on digital communication tools, the chances of duplicate contacts increase. Duplicate contact remover apps enable organizations to maintain a unified and accurate contact database, facilitating better communication and collaboration among remote teams. Moreover, the integration of these apps with popular CRM systems and email platforms adds to their utility and adoption, making them an essential tool for modern businesses.

In the realm of innovative solutions for maintaining cleanliness and efficiency, the Automated Facade Contact Cleaning Robot emerges as a groundbreaking technology. This robot is designed to address the challenges associated with cleaning high-rise building facades, which are often difficult and dangerous to maintain manually. By utilizing advanced robotics and automation, these robots can navigate complex surfaces, ensuring thorough cleaning without the need for human intervention. This not only enhances safety but also significantly reduces the time and cost involved in facade maintenance. The integration of such automated solutions is becoming increasingly prevalent in urban environments, where maintaining the aesthetic and structural integrity of buildings is paramount. As cities continue to grow and evolve, the demand for automated cleaning solutions like the Automated Facade Contact Cleaning Robot is expected to rise, offering a glimpse into the future of building maintenance.

Regionally, North America and Europe are expected to lead the market, driven by high smartphone penetration, advanced digital infrastructure, and the presence of major technology companies. Asia Pacific, however, is projected to witness the highest growth rate during the forecast period, owing to the rapid adoption of smartphones, increasing internet penetration, and the growing emphasis on digitalization in emerging economies. The market in Latin America and the Middle East & Africa is also anticipated to grow steadily as awareness about the benefits of contact management solutions increases.

Operating System Analysis

In the context of operating systems, the market for duplicate contact remover apps is segmented into Android, iOS, Windows, and others. The Android segment is expected to dominate the market due to the la

This data could be used with CiteSpace to carry out a metric analysis of 9,492 health literacy papers included in Web of Science through mapping knowledge domains. The data processing is as follows: Publications with the subject term “Health Literacy” were searched in WoS, and the search was further optimized by the following conditions: language = English; document type = article + review. The number of search results was 9,888 (downloaded on September 19, 2020). Therefore, the period of the citing articles in our study is from January 1, 1995, to September 19, 2020. During the deduplication process, we excluded duplicate publications and articles with missing key fields, such as abstracts, keywords and references, resulting in 9,429 valid records for inclusion.

The Coral Triangle Atlas provides access to managers and scientists to key spatial information for better resource management in the Coral Triangle. The data layers are both regional and national. It also helps to track the success of the Coral Triangle Initiative’s five goals. Our VisionProvide a unique opportunity for any organization working in the Coral Triangle to share their data, and to create a growing, updated database for better management decisions and science.The Coral Triangle Atlas (CT Atlas) is an online GIS database, providing governments, NGOs and researchers with a view of spatial data at the regional scale. Data on fisheries, biodiversity, natural resources, and socioeconomics have been collected for decades by scientists and managers working in different parts of the Coral Triangle region. However, to date, little of this information has been aggregated into region-wide layers to provide an overview and support management planning and decision-making at a regional level.Conserving the Coral TriangleThis CT Atlas project will improve the efficiency of management and conservation planning in the region by giving researchers and managers access to spatial information while encouraging them to share their data to complete the gaps, therefore reducing duplicate data collection efforts and providing the most complete and most current data available. The CT Atlas will be particularly useful in the design and planning of MPAs and MPA Networks throughout the region.Thus, the expansion of the CT Atlas project will improve conservation by:Giving scientists and decision makers a vision of ecological processes beyond political boundariesProviding the building blocks to use Spatial Decision Support Systems (SDSS) such as MARXAN for marine conservation planning.Avoiding duplication of efforts, enabling valuable time and resources to be most effectively allocatedThe challengeThe CT Atlas is the first attempt to collate and integrate spatial data at a regional scale for the coastal and marine regions, resources, and people of the Coral Triangle. The CT Atlas builds upon previous efforts to compile data at national and sub-national levels, recognizing the pivotal role of GIS for decision-making and resource management. Nevertheless, the CT Atlas nevertheless faces challenges in creating a high-quality, regional-scale spatial database that combines multiple and varied types of data from diverse sources.The issuesFinding the metadata for layers to complete the catalogue and standardize the attributes so that the layers can be collatedOvercoming cultural and institutional barriers to information sharingSecuring funding for a technical product: it falls outside of the usual categories research, education and outreachComplementing existing information portals and national databasesGarnering support from potential users and meeting their needsDeveloping a long-term plan for reviewing and updating the database in partnership with ReefBase. A database such as the CT Atlas will only be useful if it maintains the best and most recent information.

Data Compression Software Market Outlook

In 2023, the global data compression software market size was valued at approximately USD 1.5 billion, and it is projected to reach around USD 3.2 billion by 2032, growing at a compound annual growth rate (CAGR) of 8.5% during the forecast period. The market growth is driven by the increasing need for efficient data management and storage solutions as data volumes continue to surge globally. This growth in market size is a reflection of the expanding demand across various sectors that rely heavily on data for their operations.

One of the significant growth factors for this market is the exponential increase in data generation. As businesses and individuals produce more data than ever before, the need for effective data compression solutions becomes paramount. This is particularly true in sectors like IT and telecommunications, where data transmission costs can be significantly reduced through compression. Additionally, the rising adoption of cloud services has further amplified the demand for data compression software, as organizations look to optimize their storage and bandwidth usage.

Another critical driver is the advancement in data compression technologies. Innovations in algorithms and the development of more sophisticated compression techniques have made it possible to achieve higher compression ratios without compromising data integrity. This technological progress is enabling businesses to manage large volumes of data more efficiently, thereby reducing storage costs and improving data transfer speeds. Furthermore, the integration of artificial intelligence and machine learning into compression algorithms is expected to enhance the performance and efficacy of data compression solutions.

The growing emphasis on data security and privacy also bolsters the demand for data compression software. Compressed data is often less susceptible to breaches and unauthorized access, providing an additional layer of security. With increasing regulatory requirements and the heightened awareness of data privacy among consumers and businesses, data compression solutions are becoming a critical component of data protection strategies. This trend is particularly evident in sectors like BFSI and healthcare, where data sensitivity is paramount.

In the realm of data management, Data Deduplication Tools are becoming increasingly vital. These tools help in eliminating redundant copies of data, which not only optimizes storage usage but also enhances data retrieval efficiency. As organizations continue to generate vast amounts of data, the ability to deduplicate data effectively can lead to significant cost savings and improved data management strategies. By removing duplicate data, businesses can ensure that their storage systems are utilized more efficiently, which is crucial in today's data-driven environment. Moreover, data deduplication is particularly beneficial in backup and disaster recovery scenarios, where storage space is at a premium and quick data recovery is essential. The integration of data deduplication with data compression software further amplifies its benefits, providing a comprehensive solution for efficient data management.

Regionally, North America is expected to dominate the data compression software market, owing to its advanced IT infrastructure and the presence of major technology companies. However, other regions such as Asia Pacific are projected to exhibit significant growth rates due to the rapid digital transformation and increasing investments in IT infrastructure. The market dynamics across different regions highlight diverse growth opportunities and challenges, influenced by factors such as economic conditions, technological adoption rates, and regulatory environments.

Component Analysis

The data compression software market can be segmented by components into software and services. The software segment, which includes standalone data compression tools and integrated software solutions, forms the backbone of this market. This segment is driven by the constant need for efficient data storage and transmission solutions across various industries. Businesses are increasingly adopting sophisticated software that can compress large data volumes without losing critical information, thereby optimizing their storage and bandwidth usage. Advancements in software development, including the integration of AI and machine learning, are further enhancing the capabilities of data compression tools

Market Overview The global Data Copy Management Software market is projected to reach [Market Size] by 2033, exhibiting a CAGR of XX% during the forecast period (2023-2033). The increasing demand for data protection and compliance drives this growth. Key segments of the market include cloud platform and on-premise deployment types, as well as applications in various industries such as banking, enterprise, government, and healthcare. Key Drivers and Trends The surge in data generation and regulatory mandates for data retention and compliance are the primary drivers of market growth. Additionally, the rising adoption of cloud-based storage and the need for efficient data management contribute to market expansion. Other trends shaping the market include the integration of machine learning and AI for data optimization and the emergence of new vendors offering specialized data copy management solutions. Although cost concerns and vendor lock-in remain as restraints, the overall market outlook is positive, driven by the increasing importance of data protection and compliance in today's digital landscape. Data Copy Management Software empowers businesses to efficiently manage and duplicate their data across diverse locations, promoting data security, compliance, and recovery. The global Data Copy Management Software market size is projected to reach USD 12.5 billion by 2027, showcasing a robust CAGR of 10.3% from 2022 to 2027.

The "COVID-19 Reported Patient Impact and Hospital Capacity by Facility" dataset from the U.S. Department of Health & Human Services, filtered for Connecticut. View the full dataset and detailed metadata here: https://healthdata.gov/Hospital/COVID-19-Reported-Patient-Impact-and-Hospital-Capa/anag-cw7u

The following dataset provides facility-level data for hospital utilization aggregated on a weekly basis (Friday to Thursday). These are derived from reports with facility-level granularity across two main sources: (1) HHS TeleTracking, and (2) reporting provided directly to HHS Protect by state/territorial health departments on behalf of their healthcare facilities.

The hospital population includes all hospitals registered with Centers for Medicare & Medicaid Services (CMS) as of June 1, 2020. It includes non-CMS hospitals that have reported since July 15, 2020. It does not include psychiatric, rehabilitation, Indian Health Service (IHS) facilities, U.S. Department of Veterans Affairs (VA) facilities, Defense Health Agency (DHA) facilities, and religious non-medical facilities.

For a given entry, the term “collection_week” signifies the start of the period that is aggregated. For example, a “collection_week” of 2020-11-20 means the average/sum/coverage of the elements captured from that given facility starting and including Friday, November 20, 2020, and ending and including reports for Thursday, November 26, 2020.

Reported elements include an append of either “_coverage”, “_sum”, or “_avg”.

A “_coverage” append denotes how many times the facility reported that element during that collection week.

A “_sum” append denotes the sum of the reports provided for that facility for that element during that collection week.

A “_avg” append is the average of the reports provided for that facility for that element during that collection week.

The file will be updated weekly. No statistical analysis is applied to impute non-response. For averages, calculations are based on the number of values collected for a given hospital in that collection week. Suppression is applied to the file for sums and averages less than four (4). In these cases, the field will be replaced with “-999,999”.

This data is preliminary and subject to change as more data become available. Data is available starting on July 31, 2020.

Sometimes, reports for a given facility will be provided to both HHS TeleTracking and HHS Protect. When this occurs, to ensure that there are not duplicate reports, deduplication is applied according to prioritization rules within HHS Protect.

For influenza fields listed in the file, the current HHS guidance marks these fields as optional. As a result, coverage of these elements are varied.

On May 3, 2021, the following fields have been added to this data set. hhs_ids previous_day_admission_adult_covid_confirmed_7_day_coverage previous_day_admission_pediatric_covid_confirmed_7_day_coverage previous_day_admission_adult_covid_suspected_7_day_coverage previous_day_admission_pediatric_covid_suspected_7_day_coverage previous_week_personnel_covid_vaccinated_doses_administered_7_day_sum total_personnel_covid_vaccinated_doses_none_7_day_sum total_personnel_covid_vaccinated_doses_one_7_day_sum total_personnel_covid_vaccinated_doses_all_7_day_sum previous_week_patients_covid_vaccinated_doses_one_7_day_sum previous_week_patients_covid_vaccinated_doses_all_7_day_sum

On May 8, 2021, this data set has been converted to a corrected data set. The corrections applied to this data set are to smooth out data anomalies caused by keyed in data errors. To help determine which records have had corrections made to it. An additional Boolean field called is_corrected has been added. To see the numbers as reported by the facilities, go to: https://healthdata.gov/Hospital/COVID-19-Reported-Patient-Impact-and-Hospital-Capa/uqq2-txqb

On May 13, 2021 Changed vaccination fields from sum to max or min fields. This reflects the maximum or minimum number reported for that metric in a given week.

On June 7, 2021 Changed vaccination fields from max or min fields to Wednesday collected fields only. This reflects that these fields are only reported on Wednesdays in a given week.

On 9/20/2021, the following has been updated: The use of analytic dataset as a source.

The California Department of Forestry and Fire Protection's Fire and Resource Assessment Program (FRAP) annually maintains and distributes an historical wildland fire perimeter dataset from across public and private lands in California. The GIS data is developed with the cooperation of the United States Forest Service Region 5, the Bureau of Land Management, California State Parks, National Park Service and the United States Fish and Wildlife Service and is released in the spring with added data from the previous calendar year. Although the dataset represents the most complete digital record of fire perimeters in California, it is still incomplete, and users should be cautious when drawing conclusions based on the data. This data should be used carefully for statistical analysis and reporting due to missing perimeters (see Use Limitation in metadata). Some fires are missing because historical records were lost or damaged, were too small for the minimum cutoffs, had inadequate documentation or have not yet been incorporated into the database. Other errors with the fire perimeter database include duplicate fires and over-generalization. Additionally, over-generalization, particularly with large old fires, may show unburned "islands" within the final perimeter as burned. Users of the fire perimeter database must exercise caution in application of the data. Careful use of the fire perimeter database will prevent users from drawing inaccurate or erroneous conclusions from the data. This data is updated annually in the spring with fire perimeters from the previous fire season. This dataset may differ in California compared to that available from the National Interagency Fire Center (NIFC) due to different requirements between the two datasets. The data covers fires back to 1878. As of May 2024, it represents fire23_1. Please help improve this dataset by filling out this survey with feedback:Historic Fire Perimeter Dataset Feedback (arcgis.com)Current criteria for data collection are as follows:CAL FIRE (including contract counties) submit perimeters ≥10 acres in timber, ≥50 acres in brush, or ≥300 acres in grass, and/or ≥3 impacted residential or commercial structures, and/or caused ≥1 fatality.All cooperating agencies submit perimeters ≥10 acres.Version update:Firep23_1 was released in May 2024. Two hundred eighty four fires from the 2023 fire season were added to the database (21 from BLM, 102 from CAL FIRE, 72 from Contract Counties, 19 from LRA, 9 from NPS, 57 from USFS and 4 from USFW). The 2020 Cottonwood fire, 2021 Lone Rock and Union fires, as well as the 2022 Lost Lake fire were added. USFW submitted a higher accuracy perimeter to replace the 2022 River perimeter. Additionally, 48 perimeters were digitized from an historical map included in a publication from Weeks, d. et al. The Utilization of El Dorado County Land. May 1934, Bulletin 572. University of California, Berkeley. Two thousand eighteen perimeters had attributes updated, the bulk of which had IRWIN IDs added. A duplicate 2020 Erbes perimeter was removed. The following fires were identified as meeting our collection criteria, but are not included in this version and will hopefully be added in the next update: Big Hill #2 (2023-CAHIA-001020). YEAR_ field changed to a short integer type. San Diego CAL FIRE UNIT_ID changed to SDU (the former code MVU is maintained in the UNIT_ID domains). COMPLEX_INCNUM renamed to COMPLEX_ID and is in process of transitioning from local incident number to the complex IRWIN ID. Perimeters managed in a complex in 2023 are added with the complex IRWIN ID. Those previously added will transition to complex IRWIN IDs in a future update.Includes separate layers filtered by criteria as follows:California Fire Perimeters (All): Unfiltered. The entire collection of wildfire perimeters in the database. It is scale dependent and starts displaying at the country level scale. Recent Large Fire Perimeters (≥5000 acres): Filtered for wildfires greater or equal to 5,000 acres for the last 5 years of fires (2019-2023), symbolized with color by year and is scale dependent and starts displaying at the country level scale. Year-only labels for recent large fires.California Fire Perimeters (1950+): Filtered for wildfires that started in 1950-present. Symbolized by decade, and display starting at country level scale.Detailed metadata is included in the following documents:Wildland Fire Perimeters (Firep23_1) Metadata For any questions, please contact the data steward:Kim Wallin, GIS SpecialistCAL FIRE, Fire & Resource Assessment Program (FRAP)kimberly.wallin@fire.ca.gov

Quadrant provides Insightful, accurate, and reliable mobile location data.

Our privacy-first mobile location data unveils hidden patterns and opportunities, provides actionable insights, and fuels data-driven decision-making at the world's biggest companies.

These companies rely on our privacy-first Mobile Location and Points-of-Interest Data to unveil hidden patterns and opportunities, provide actionable insights, and fuel data-driven decision-making. They build better AI models, uncover business insights, and enable location-based services using our robust and reliable real-world data.

We conduct stringent evaluations on data providers to ensure authenticity and quality. Our proprietary algorithms detect, and cleanse corrupted and duplicated data points – allowing you to leverage our datasets rapidly with minimal processing or cleaning. During the ingestion process, our proprietary Data Filtering Algorithms remove events based on a number of both qualitative factors, as well as latency and other integrity variables to provide more efficient data delivery. The deduplicating algorithm focuses on a combination of four important attributes: Device ID, Latitude, Longitude, and Timestamp. This algorithm scours our data and identifies rows that contain the same combination of these four attributes. Post-identification, it retains a single copy and eliminates duplicate values to ensure our customers only receive complete and unique datasets.

We actively identify overlapping values at the provider level to determine the value each offers. Our data science team has developed a sophisticated overlap analysis model that helps us maintain a high-quality data feed by qualifying providers based on unique data values rather than volumes alone – measures that provide significant benefit to our end-use partners.

Quadrant mobility data contains all standard attributes such as Device ID, Latitude, Longitude, Timestamp, Horizontal Accuracy, and IP Address, and non-standard attributes such as Geohash and H3. In addition, we have historical data available back through 2022.

Through our in-house data science team, we offer sophisticated technical documentation, location data algorithms, and queries that help data buyers get a head start on their analyses. Our goal is to provide you with data that is “fit for purpose”.

Quadrant provides Insightful, accurate, and reliable mobile location data.

Our privacy-first mobile location data unveils hidden patterns and opportunities, provides actionable insights, and fuels data-driven decision-making at the world's biggest companies.

These companies rely on our privacy-first Mobile Location and Points-of-Interest Data to unveil hidden patterns and opportunities, provide actionable insights, and fuel data-driven decision-making. They build better AI models, uncover business insights, and enable location-based services using our robust and reliable real-world data.

We conduct stringent evaluations on data providers to ensure authenticity and quality. Our proprietary algorithms detect, and cleanse corrupted and duplicated data points – allowing you to leverage our datasets rapidly with minimal processing or cleaning. During the ingestion process, our proprietary Data Filtering Algorithms remove events based on a number of both qualitative factors, as well as latency and other integrity variables to provide more efficient data delivery. The deduplicating algorithm focuses on a combination of four important attributes: Device ID, Latitude, Longitude, and Timestamp. This algorithm scours our data and identifies rows that contain the same combination of these four attributes. Post-identification, it retains a single copy and eliminates duplicate values to ensure our customers only receive complete and unique datasets.

We actively identify overlapping values at the provider level to determine the value each offers. Our data science team has developed a sophisticated overlap analysis model that helps us maintain a high-quality data feed by qualifying providers based on unique data values rather than volumes alone – measures that provide significant benefit to our end-use partners.

Quadrant mobility data contains all standard attributes such as Device ID, Latitude, Longitude, Timestamp, Horizontal Accuracy, and IP Address, and non-standard attributes such as Geohash and H3. In addition, we have historical data available back through 2022.

Through our in-house data science team, we offer sophisticated technical documentation, location data algorithms, and queries that help data buyers get a head start on their analyses. Our goal is to provide you with data that is “fit for purpose”.

Master Data Management Product Data Syndication (PDS) Market size was valued at USD 1.8 Billion in 2023 and is projected to reach USD 5.55 Billion by 2030, growing at a CAGR of 16.3 % during the forecast period 2024-2030.

Global Master Data Management Product Data Syndication (PDS) Market Drivers

The market drivers for the Master Data Management Product Data Syndication (PDS) Market can be influenced by various factors. These may include:

Proliferation of E-commerce: The volume and complexity of product data have increased exponentially as a result of the quick development of online marketplaces and e-commerce platforms. Businesses may effectively manage and disseminate correct, current product information across numerous channels with the use of master data management (MDM) systems that have product data syndication capabilities. This enhances web visibility, customer satisfaction, and sales conversion rates. Demand for Omnichannel Retailing: Retailers must guarantee consistency and accuracy in product information across all touchpoints as consumers expect a seamless shopping experience across multiple channels, including websites, mobile apps, social media platforms, and brick-and-mortar stores. Retailers may improve brand reputation and customer happiness by using MDM PDS solutions to synchronize product data in real-time and give consistent product information across numerous channels. Regulatory Compliance and Data Governance: For companies operating in highly regulated industries including healthcare, pharmaceuticals, food and beverage, and consumer goods, compliance with regulatory regulations, industry standards, and data governance rules is essential. MDM PDS solutions facilitate traceability, labelling, and reporting requirements while guaranteeing the accuracy, consistency, and completeness of product data. This helps enterprises stay compliant. Globalization and Expansion Initiatives: Businesses have difficulties with localization, language translation, and cultural adaption of product information as they enter new markets and regions. Organizations can support international expansion ambitions and market penetration goals by using MDM PDS solutions to manage multilingual product catalogues, adapt product descriptions and specifications, and adhere to regional rules and labelling requirements. Demand for Real-time Data Synchronization: Businesses must react swiftly to shifts in the market, client preferences, and competitive dynamics in the fast-paced business world of today. With real-time data synchronization capabilities provided by MDM PDS solutions, businesses can improve their agility and responsiveness by launching new goods more quickly, updating product information dynamically, and taking advantage of developing market opportunities. Pay Attention to Data Quality and Accuracy: Having accurate and trustworthy product data is crucial for increasing sales, decreasing returns, and building client loyalty. By removing duplicate entries, resolving discrepancies, standardizing and enhancing product information, and maintaining a single source of truth for product data throughout the company, MDM PDS solutions assist businesses in improving the quality of their data.