Displays potential software and hardware product duplicates within a manufacturer. Product duplicates have the same name, component, and manufacturer. Also displays duplicate software versions (patch level and edition must be the same) and hardware models within a product.

Dataset Overview:

Total Records: 749 Original Records: 700 Duplicate Records: 49 (7% of total) File Name: synthetic_claims_with_duplicates.csv Key Features:

Claim Information: Unique claim IDs (CLAIM000001 to CLAIM000700) Employee IDs (EMP0001 to EMP0700) Realistic employee names Financial Data: Amounts range: 100.00 to 20,000.00 Service codes: SVC001, SVC002, SVC003, SVC004 Departments: Finance, HR, IT, Marketing, Operations Transaction Details: Dates within the last 2 years Timestamps for submission Statuses: Submitted, Approved, Paid Random UUIDs for submitter IDs Fraud Detection: 49 exact duplicates (7%) Random distribution throughout the dataset Boolean is_duplicate flag for identification Purpose: The dataset is designed to test fraud detection systems, particularly for identifying duplicate transactions. It simulates real-world scenarios where duplicate entries might occur due to fraud or data entry errors.

Usage:

Testing duplicate transaction detection Training fraud detection models Data validation and cleaning Algorithm benchmarking The dataset is now ready for analysis in your fraud detection system.

Papers on duplicate records.

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 Tools Market size was valued at USD 3.86 Billion in 2023 and is projected to reach USD 6.51 Billion by 2030, growing at a CAGR of 12.3% during the forecast period 2024-2030.

Global Data Deduplication Tools Market Drivers

The market drivers for the Data Deduplication Tools Market can be influenced by various factors. These may include:

Explosion of Data: Effective data deduplication technologies are required due to the exponential growth of data generated by organizations in order to maximize storage capacity and enhance the effectiveness of data management.

Optimising Storage: Organisations are always looking for methods to improve their infrastructure for storage. By reducing redundancy, data deduplication solutions help organizations store more data in less physical space.

Cut Costs: Organisations can decrease storage costs by reducing data duplication because it requires less physical storage gear and may result in lower prices for cloud storage.

Efficiency of Data Backup: The speed and effectiveness of data backup procedures are improved by effective data deduplication. Lower network bandwidth usage and faster backup times are the outcomes of smaller data quantities.

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 data deduplication tools is poised for substantial growth, with a market size valued at XXX million in 2023 and projected to reach XXX million by 2033, exhibiting a CAGR of XX% during the forecast period from 2023 to 2033. The increasing volume of data generated across industries, driven by the proliferation of cloud computing, big data analytics, and the Internet of Things (IoT), is a primary driver fueling market growth. The adoption of data deduplication tools is also being driven by the need for cost optimization, as businesses seek to reduce storage and backup infrastructure expenses. The increasing awareness of data protection and compliance regulations, coupled with the growing threat of cyberattacks, is further contributing to the demand for data deduplication solutions. Key industry trends include the increasing adoption of hybrid cloud environments, the rise of software-defined data centers, and the emergence of artificial intelligence (AI) and machine learning (ML) technologies in data deduplication tools.

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

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.

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

The Data Deduplication Software market is experiencing robust growth, driven by the exponential increase in data volume across various sectors. The market, estimated at $10 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This significant expansion is fueled by several key factors. The rising adoption of cloud computing, particularly hybrid and public cloud models, necessitates efficient data storage and management solutions, leading to increased demand for data deduplication software. Furthermore, stringent data governance regulations and the increasing need for data security are compelling organizations across BFSI, healthcare, government, and education sectors to invest in advanced data deduplication solutions. The market is segmented by cloud deployment type (public, private, hybrid) and application across diverse industries. Leading players like IBM, Microsoft, Dell EMC, and others are driving innovation through advanced algorithms and improved integration with existing IT infrastructures. However, the market also faces certain challenges. High initial investment costs, complexities associated with implementation, and the need for specialized expertise can hinder widespread adoption, particularly among small and medium-sized enterprises (SMEs). Furthermore, the increasing availability of built-in deduplication features in storage systems might present some competition. Nevertheless, the overall market outlook remains positive, with continued growth anticipated due to the persistent need for efficient data storage and management in a world grappling with ever-increasing data volumes and stringent regulatory compliance requirements. The continued rise of Big Data analytics and the expansion of the cloud infrastructure will further propel market growth in the forecast period.

Dive into Market Research Intellect's Data Deduplication Tools Market Report, valued at USD 2.5 billion in 2024, and forecast to reach USD 5.1 billion by 2033, growing at a CAGR of 9.2% from 2026 to 2033.

Dataset-A with one duplicate against an original record and one modification per duplicate record. (CSV 92 kb)

Dataset Card for Quora Duplicate Questions

This dataset contains the Quora Question Pairs dataset in four formats that are easily used with Sentence Transformers to train embedding models. The data was originally created by Quora for this Kaggle Competition.

  Dataset Subsets





  pair-class subset

Columns: "sentence1", "sentence2", "label" Column types: str, str, class with {"0": "different", "1": "duplicate"} Examples:{ 'sentence1': 'What is the step by step… See the full description on the dataset page: https://huggingface.co/datasets/sentence-transformers/quora-duplicates.

Displays potential software and hardware product duplicates within a manufacturer. Product duplicates have the same name, component, and manufacturer. Also displays duplicate software versions (patch level and edition must be the same) and hardware models within a product.

GitHub projects can be easily replicated through the site's fork process or through a Git clone-push sequence. This is a problem for empirical software engineering, because it can lead to skewed results or mistrained machine learning models. We provide a dataset of 10.6 million GitHub projects that are copies of others, and link each record with the project's ultimate parent. The ultimate parents were derived from a ranking along six metrics. The related projects were calculated as the connected components of an 18.2 million node and 12 million edge denoised graph created by directing edges to ultimate parents. The graph was created by filtering out more than 30 hand-picked and 2.3 million pattern-matched clumping projects. Projects that introduced unwanted clumping were identified by repeatedly visualizing shortest path distances between unrelated important projects. Our dataset identified 30 thousand duplicate projects in an existing popular reference dataset of 1.8 million projects. An evaluation of our dataset against another created independently with different methods found a significant overlap, but also differences attributed to the operational definition of what projects are considered as related.

The dataset is provided as two files identifying GitHub repositories using the login-name/project-name convention. The file deduplicate_names contains 10,649,348 tab-separated records mapping a duplicated source project to a definitive target project.

The file forks_clones_noise_names is a 50,324,363 member superset of the source projects, containing also projects that were excluded from the mapping as noise.

This dataset includes data quality assurance information concerning the Relative Percent Difference (RPD) of laboratory duplicates. No laboratory duplicate information exists for 2010. The formula for calculating relative percent difference is: ABS(2*[(A-B)/(A+B)]). An RPD of less the 10% is considered acceptable.

The global duplicate file finder for Windows market is experiencing robust growth, driven by the increasing demand for data management and organization solutions. The market is projected to reach a value of USD XXX million by 2033, growing at a CAGR of XX% over the forecast period from 2025 to 2033, with a base year of 2025. This growth is attributed to factors such as the rising adoption of digital devices, increasing volumes of data being generated and stored, and growing awareness of the importance of data deduplication. Key trends in the duplicate file finder market for Windows include the growing preference for paid software over free versions, the rising adoption of cloud-based duplicate file finders, and the emergence of AI-powered tools for more efficient file management. The market is highly competitive, with a number of well-established players such as Piriform, Systweak Software, Webminds, and WiseCleaner holding significant market shares. The market is geographically segmented into North America, South America, Europe, the Middle East & Africa, and Asia Pacific, with North America expected to remain the dominant region throughout the forecast period.

Market Analysis for Data Deduplication Software The global data deduplication software market is anticipated to reach a valuation of USD 5.7 billion by 2033, growing at a CAGR of 17.5% from 2025 to 2033. The rising volume of data, increasing storage costs, and growing adoption of cloud computing drive this growth. Data deduplication techniques optimize storage space by eliminating redundant data, reducing storage costs and improving data management efficiency. Market segments include cloud deployment models (public, private, hybrid) and application areas (BFSI, public sector, healthcare, education, others). Key market players include IBM, Microsoft, Dell EMC, Fujitsu, Hitachi, and Veritas Technologies. North America dominates the market due to the presence of leading data centers and technological advancements. Asia Pacific is expected to experience significant growth in the coming years due to rising storage needs and the adoption of cloud services.

This contains the dataset for the EMNLP 2024 publication titled BPID: A Benchmark for Personal Identity Deduplication.