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Data Wrangling Market Size 2024-2028

The data wrangling market size is forecast to increase by USD 1.4 billion at a CAGR of 14.8% between 2023 and 2028. The market is experiencing significant growth due to the numerous benefits provided by data wrangling solutions, including data cleaning, transformation, and enrichment. One major trend driving market growth is the rising need for technology such as the competitive intelligence and artificial intelligence in the healthcare sector, where data wrangling is essential for managing and analyzing patient data to improve patient outcomes and reduce costs. However, a challenge facing the market is the lack of awareness of data wrangling tools among small and medium-sized enterprises (SMEs), which limits their ability to effectively manage and utilize their data. Despite this, the market is expected to continue growing as more organizations recognize the value of data wrangling in driving business insights and decision-making.

What will be the Size of the Market During the Forecast Period?

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The market is experiencing significant growth due to the increasing demand for data management and analysis in various industries. The market is experiencing significant growth due to the increasing volume, variety, and velocity of data being generated from various sources such as IoT devices, financial services, and smart cities. Artificial intelligence and machine learning technologies are being increasingly used for data preparation, data cleaning, and data unification. Data wrangling, also known as data munging, is the process of cleaning, transforming, and enriching raw data to make it usable for analysis. This process is crucial for businesses aiming to gain valuable insights from their data and make informed decisions. Data analytics is a primary driver for the market, as organizations seek to extract meaningful insights from their data. Cloud solutions are increasingly popular for data wrangling due to their flexibility, scalability, and cost-effectiveness.

Furthermore, both on-premises and cloud-based solutions are being adopted by businesses to meet their specific data management requirements. Multi-cloud strategies are also gaining traction in the market, as organizations seek to leverage the benefits of multiple cloud providers. This approach allows businesses to distribute their data across multiple clouds, ensuring business continuity and disaster recovery capabilities. Data quality is another critical factor driving the market. Ensuring data accuracy, completeness, and consistency is essential for businesses to make reliable decisions. The market is expected to grow further as organizations continue to invest in big data initiatives and implement advanced technologies such as AI and ML to gain a competitive edge. Data cleaning and data unification are key processes in data wrangling that help improve data quality. The finance and insurance industries are major contributors to the market, as they generate vast amounts of data daily.

In addition, real-time analysis is becoming increasingly important in these industries, as businesses seek to gain insights from their data in near real-time to make informed decisions. The Internet of Things (IoT) is also driving the market, as businesses seek to collect and analyze data from IoT devices to gain insights into their operations and customer behavior. Edge computing is becoming increasingly popular for processing IoT data, as it allows for faster analysis and decision-making. Self-service data preparation is another trend in the market, as businesses seek to empower their business users to prepare their data for analysis without relying on IT departments.

Moreover, this approach allows businesses to be more agile and responsive to changing business requirements. Big data is another significant trend in the market, as businesses seek to manage and analyze large volumes of data to gain insights into their operations and customer behavior. Data wrangling is a critical process in managing big data, as it ensures that the data is clean, transformed, and enriched to make it usable for analysis. In conclusion, the market in North America is experiencing significant growth due to the increasing demand for data management and analysis in various industries. Cloud solutions, multi-cloud strategies, data quality, finance and insurance, IoT, real-time analysis, self-service data preparation, and big data are some of the key trends driving the market. Businesses that invest in data wrangling solutions can gain a competitive edge by gaining valuable insights from their data and making informed decisions.

Market Segmentation

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

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Data Wrangling Market Outlook

According to our latest research, the global Data Wrangling market size in 2024 stands at USD 4.1 billion, exhibiting robust momentum across industries. The market is poised to expand at a notable CAGR of 15.6% from 2025 to 2033, projecting a value of USD 13.2 billion by the end of the forecast period. The primary growth factor fueling this surge is the exponential rise in data volumes and the urgent need for efficient data preparation solutions to drive analytics, machine learning, and business intelligence initiatives.

A critical driver for the Data Wrangling market is the rapid digital transformation across industries, which has led to a massive influx of structured and unstructured data. Organizations are increasingly focusing on leveraging their data assets to gain actionable insights, optimize operations, and enhance decision-making processes. However, raw data is often inconsistent, incomplete, or stored in disparate sources, making it challenging to derive value without proper preparation. Data wrangling tools and services address this challenge by enabling seamless data cleansing, transformation, and integration, thus unlocking the true potential of data-driven strategies. As enterprises aim to improve data quality and accelerate time-to-insight, the adoption of advanced data wrangling solutions continues to rise.

Another significant growth factor is the proliferation of artificial intelligence (AI) and machine learning (ML) applications in business environments. High-quality, well-prepared data is the cornerstone of successful AI and ML models. Data wrangling automates the labor-intensive processes of data normalization, deduplication, and enrichment, ensuring that analytics and AI initiatives are fed with reliable inputs. This has become particularly important in sectors such as finance, healthcare, and retail, where real-time analytics and predictive modeling are critical for competitive advantage. As organizations increasingly invest in AI and ML, the demand for scalable and intelligent data wrangling solutions is expected to witness substantial growth throughout the forecast period.

Moreover, the evolving regulatory landscape around data privacy and security is compelling organizations to adopt robust data management practices. Data wrangling solutions not only streamline data preparation but also help ensure compliance with regulations by enabling traceability, data lineage, and auditability. This is particularly relevant for highly regulated industries like BFSI and healthcare, where data integrity and governance are paramount. The integration of advanced features such as automated anomaly detection, role-based access controls, and comprehensive audit trails further enhances the value proposition of data wrangling platforms, driving their adoption across both large enterprises and SMEs.

Regionally, North America remains the dominant market for data wrangling, owing to the early adoption of advanced analytics, a strong presence of leading solution providers, and a mature digital infrastructure. However, Asia Pacific is emerging as the fastest-growing region, fueled by rapid digitalization, increasing investments in cloud technologies, and a burgeoning startup ecosystem. Europe follows closely, driven by stringent data protection regulations and a growing emphasis on data-driven decision-making. Latin America and the Middle East & Africa are also witnessing steady growth as organizations in these regions embrace digital transformation to enhance operational efficiency and customer engagement.

Component Analysis

The Component segment of the Data Wrangling market is bifurcated into Tools and Services, each playing a pivotal role in the overall ecosystem. Data wrangling tools, encompassing both on-premises and cloud-based platforms, are designed to automate and streamline the process of cleaning, structuring, and enriching raw data. These tools are increasingly l

p.p1 {margin: 0.0px 0.0px 0.0px 0.0px; font: 16.0px 'Andale Mono'; color: #29f914; background-color: #000000} span.s1 {font-variant-ligatures: no-common-ligatures} These files are intended for use with the Data Carpentry Genomics curriculum (https://datacarpentry.org/genomics-workshop/). Files will be useful for instructors teaching this curriculum in a workshop setting, as well as individuals working through these materials on their own.

This curriculum is normally taught using Amazon Web Services (AWS). Data Carpentry maintains an AWS image that includes all of the data files needed to use these lesson materials. For information on how to set up an AWS instance from that image, see https://datacarpentry.org/genomics-workshop/setup.html. Learners and instructors who would prefer to teach on a different remote computing system can access all required files from this FigShare dataset.

This curriculum uses data from a long term evolution experiment published in 2016: Tempo and mode of genome evolution in a 50,000-generation experiment (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4988878/) by Tenaillon O, Barrick JE, Ribeck N, Deatherage DE, Blanchard JL, Dasgupta A, Wu GC, Wielgoss S, Cruveiller S, Médigue C, Schneider D, and Lenski RE. (doi: 10.1038/nature18959). All sequencing data sets are available in the NCBI BioProject database under accession number PRJNA294072 (https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA294072).

backup.tar.gz: contains original fastq files, reference genome, and subsampled fastq files. Directions for obtaining these files from public databases are given during the lesson https://datacarpentry.org/wrangling-genomics/02-quality-control/index.html). On the AWS image, these files are stored in ~/.backup directory. 1.3Gb in size.

Ecoli_metadata.xlsx: an example Excel file to be loaded during the R lesson.

shell_data.tar.gz: contains the files used as input to the Introduction to the Command Line for Genomics lesson (https://datacarpentry.org/shell-genomics/).

sub.tar.gz: contains subsampled fastq files that are used as input to the Data Wrangling and Processing for Genomics lesson (https://datacarpentry.org/wrangling-genomics/). 109Mb in size.

solutions: contains the output files of the Shell Genomics and Wrangling Genomics lessons, including fastqc output, sam, bam, bcf, and vcf files.

vcf_clean_script.R: converts vcf output in .solutions/wrangling_solutions/variant_calling_auto to single tidy data frame.

combined_tidy_vcf.csv: output of vcf_clean_script.R

This repository showcases some examples of data wrangling and visualization using the output of the USGS's output from a drought prediction model on the Colorado River Basin and example ecology site data.

Data Carpentry lesson to learn how to use command-line tools to perform quality control, align reads to a reference genome, and identify and visualize between-sample variation.

Dataset Card for DS Coder Instruct Dataset

DS Coder is a dataset for instruction fine tuning of language models. It is a specialized dataset focusing only on data science (eg. plotting, data wrangling, machine learnig models, deep learning, and numerical computations). The dataset contains code examples both in R and Python. The goal of this dataset is to enable creation of small-scale, specialized language model assistants for data science projects.

  Dataset Details… See the full description on the dataset page: https://huggingface.co/datasets/ed001/ds-coder-instruct-v1.

Additional file 2. Example of transformed metadata: In this .xlsx (MS Excel) file, we list all the output metadata categories generated for each sample from the transformation of the 1KGP input datasets. The output metadata include information collected from all the four 1KGP metadata files considered. Some categories are not reported in the source metadata files—they are identified by the label manually_curated_...—and were added by the developed pipeline to store technical details (e.g., download date, the md5 hash of the source file, file size, etc.) and information derived from the knowledge of the source, such as the species, the processing pipeline used in the source and the health status. For every information category, the table reports a possible value. The third column (cardinality > 1) tells whether the same key can appear multiple times in the output GDM metadata file. This is used to represent multi-valued metadata categories; for example, in a GDM metadata file, the key manually_curated_chromosome appears once for every chromosome mutated by the variants of the sample.

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Data Visualization Tools Market Size 2025-2029

The data visualization tools market size is forecast to increase by USD 7.95 billion at a CAGR of 11.2% between 2024 and 2029.

The market is experiencing significant growth due to the increasing demand for business intelligence and AI-powered insights. Companies are recognizing the value of transforming complex data into easily digestible visual representations to inform strategic decision-making. However, this market faces challenges as data complexity and massive data volumes continue to escalate. Organizations must invest in advanced data visualization tools to effectively manage and analyze their data to gain a competitive edge. The ability to automate data visualization processes and integrate AI capabilities will be crucial for companies to overcome the challenges posed by data complexity and volume. By doing so, they can streamline their business operations, enhance data-driven insights, and ultimately drive growth in their respective industries.

What will be the Size of the Data Visualization Tools Market during the forecast period?

Request Free SampleIn today's data-driven business landscape, the market continues to evolve, integrating advanced capabilities to support various sectors in making informed decisions. Data storytelling and preparation are crucial elements, enabling organizations to effectively communicate complex data insights. Real-time data visualization ensures agility, while data security safeguards sensitive information. Data dashboards facilitate data exploration and discovery, offering data-driven finance, strategy, and customer experience. Big data visualization tackles complex datasets, enabling data-driven decision making and innovation. Data blending and filtering streamline data integration and analysis. Data visualization software supports data transformation, cleaning, and aggregation, enhancing data-driven operations and healthcare. On-premises and cloud-based solutions cater to diverse business needs. Data governance, ethics, and literacy are integral components, ensuring data-driven product development, government, and education adhere to best practices. Natural language processing, machine learning, and visual analytics further enrich data-driven insights, enabling interactive charts and data reporting. Data connectivity and data-driven sales fuel business intelligence and marketing, while data discovery and data wrangling simplify data exploration and preparation. The market's continuous dynamism underscores the importance of data culture, data-driven innovation, and data-driven HR, as organizations strive to leverage data to gain a competitive edge.

How is this Data Visualization Tools Industry segmented?

The data visualization tools industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. DeploymentOn-premisesCloudCustomer TypeLarge enterprisesSMEsComponentSoftwareServicesApplicationHuman resourcesFinanceOthersEnd-userBFSIIT and telecommunicationHealthcareRetailOthersGeographyNorth AmericaUSMexicoEuropeFranceGermanyUKMiddle East and AfricaUAEAPACAustraliaChinaIndiaJapanSouth KoreaSouth AmericaBrazilRest of World (ROW)

By Deployment Insights

The on-premises segment is estimated to witness significant growth during the forecast period.The market has experienced notable expansion as businesses across diverse sectors acknowledge the significance of data analysis and representation to uncover valuable insights and inform strategic decisions. Data visualization plays a pivotal role in this domain. On-premises deployment, which involves implementing data visualization tools within an organization's physical infrastructure or dedicated data centers, is a popular choice. This approach offers organizations greater control over their data, ensuring data security, privacy, and adherence to data governance policies. It caters to industries dealing with sensitive data, subject to regulatory requirements, or having stringent security protocols that prohibit cloud-based solutions. Data storytelling, data preparation, data-driven product development, data-driven government, real-time data visualization, data security, data dashboards, data-driven finance, data-driven strategy, big data visualization, data-driven decision making, data blending, data filtering, data visualization software, data exploration, data-driven insights, data-driven customer experience, data mapping, data culture, data cleaning, data-driven operations, data aggregation, data transformation, data-driven healthcare, on-premises data visualization, data governance, data ethics, data discovery, natural language processing, data reporting, data visualization platforms, data-driven innovation, data wrangling, data-driven s

Additional file 1. Example of translation from VCF into GDM format for genomic region data: This .xlsx (MS Excel) spreadsheet exemplifies the transformation of the original 1KGP mutations—expressed in VCF format—into GDM genomic regions. As a demonstrative example, some variants about chromosome X have been selected from the source data (in VCF format) and listed in the first table at the top of the file. The values of columns #CHROM, POS, REF and ALT appear as in the source. We removed the details that are unnecessary for the transformation from the column INFO. In the column FORMAT it is indicated exclusively the value “GT”, meaning that the next columns contain only the genotype of the samples (this and other conventions are expressed in the VCF specification document and in the header section of each VCF file). In multiallelic variants (examples e, f.1 and f.2), the genotype indicates with a number which of the alternative alleles in ALT is present in the corresponding samples (e.g., the number 2 means that the second variant is present); otherwise, it only assumes the values 0—mutation absent, or 1—the mutation is present. Additionally, the genotype indicates whether one or both chromosome copies contain the mutation and which one, i.e., the left one or the right one; the mutated alleles are normally separated by a pipe (“|”), if not otherwise specified in the header section; we do not know which chromosome copy is maternal or paternal, but as the 1KGP mutations are “phased”, we know that the “left chromosome” is the same in every mutation located in the same chromosome of the same donor. As in this example we have only one column after the FORMAT one, the mutations described are relative to only one sample, called “HG123456”. Actually, this sample does not exist in the source, but serves the purpose of demonstrating several mutation types that are found in the original data. The table reports six variants in VCF format, with the last one repeated two times to show how different values of genotype lead to a different translation (indeed, examples f.1 and f.2 differ only for the last column). Below in the same file, the same variants appear converted in GDM format. The transformation outputs the chr, left, right, strand, AL1, AL2, ref, alt, mut_type and length columns. The value of strand is positive in every mutation, as clarified by the 1KGP Consortium after the release of the data collections. Values of AL1 and AL2 express on which chromatid the mutation occur and depend on the value of the original genotype (column HG123456). The values of the other columns, namely chr, left, right, ref, alt, mut_type and length, are obtained from the variant original values after the split of multi-allelic variants, the transformation of the original position into 0-based coordinates, and the removal of repeated nucleotide bases from the original REF and ALT columns. In 0-based coordinates, a nucleotide base occupies the space between the coordinates x and x + 1. So, SNPs (examples a and f.2) are encoded as the replacement of ref at position between left and right with alt. Insertions (examples c and f.1) are described as the addition of the sequence of bases in alt at the position indicated in left and right, i.e., in between two nucleotide bases. Deletions (example b) are represented as the substitution of ref between positions left and right with an empty value (alt is indeed empty in this case). Finally, structural variants (examples d and e) such as copy number variations and large deletions have an empty ref because, according to the VCF specification document, the original column REF reports a nucleotide (called padding-base) that is located before the scope of the variant on the genome and is unnecessary in a 0-based representation. In this file, we reported only the columns relevant for the understanding of the transformation method regarding the mutation coordinates, reference and alternative alleles. Actually, in addition to the ones reported in the second table, the transformation adds some more columns, called as the attributes in the original INFO column to capture a selection of the attributes present in the original file.

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Data Analytics Market Size 2025-2029

The data analytics market size is forecast to increase by USD 288.7 billion, at a CAGR of 14.7% between 2024 and 2029.

The market is driven by the extensive use of modern technology in company operations, enabling businesses to extract valuable insights from their data. The prevalence of the Internet and the increased use of linked and integrated technologies have facilitated the collection and analysis of vast amounts of data from various sources. This trend is expected to continue as companies seek to gain a competitive edge by making data-driven decisions. However, the integration of data from different sources poses significant challenges. Ensuring data accuracy, consistency, and security is crucial as companies deal with large volumes of data from various internal and external sources. Additionally, the complexity of data analytics tools and the need for specialized skills can hinder adoption, particularly for smaller organizations with limited resources. Companies must address these challenges by investing in robust data management systems, implementing rigorous data validation processes, and providing training and development opportunities for their employees. By doing so, they can effectively harness the power of data analytics to drive growth and improve operational efficiency.

What will be the Size of the Data Analytics Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free SampleIn the dynamic and ever-evolving the market, entities such as explainable AI, time series analysis, data integration, data lakes, algorithm selection, feature engineering, marketing analytics, computer vision, data visualization, financial modeling, real-time analytics, data mining tools, and KPI dashboards continue to unfold and intertwine, shaping the industry's landscape. The application of these technologies spans various sectors, from risk management and fraud detection to conversion rate optimization and social media analytics. ETL processes, data warehousing, statistical software, data wrangling, and data storytelling are integral components of the data analytics ecosystem, enabling organizations to extract insights from their data. Cloud computing, deep learning, and data visualization tools further enhance the capabilities of data analytics platforms, allowing for advanced data-driven decision making and real-time analysis. Marketing analytics, clustering algorithms, and customer segmentation are essential for businesses seeking to optimize their marketing strategies and gain a competitive edge. Regression analysis, data visualization tools, and machine learning algorithms are instrumental in uncovering hidden patterns and trends, while predictive modeling and causal inference help organizations anticipate future outcomes and make informed decisions. Data governance, data quality, and bias detection are crucial aspects of the data analytics process, ensuring the accuracy, security, and ethical use of data. Supply chain analytics, healthcare analytics, and financial modeling are just a few examples of the diverse applications of data analytics, demonstrating the industry's far-reaching impact. Data pipelines, data mining, and model monitoring are essential for maintaining the continuous flow of data and ensuring the accuracy and reliability of analytics models. The integration of various data analytics tools and techniques continues to evolve, as the industry adapts to the ever-changing needs of businesses and consumers alike.

How is this Data Analytics Industry segmented?

The data analytics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. ComponentServicesSoftwareHardwareDeploymentCloudOn-premisesTypePrescriptive AnalyticsPredictive AnalyticsCustomer AnalyticsDescriptive AnalyticsOthersApplicationSupply Chain ManagementEnterprise Resource PlanningDatabase ManagementHuman Resource ManagementOthersGeographyNorth AmericaUSCanadaEuropeFranceGermanyUKMiddle East and AfricaUAEAPACChinaIndiaJapanSouth KoreaSouth AmericaBrazilRest of World (ROW)

By Component Insights

The services segment is estimated to witness significant growth during the forecast period.The market is experiencing significant growth as businesses increasingly rely on advanced technologies to gain insights from their data. Natural language processing is a key component of this trend, enabling more sophisticated analysis of unstructured data. Fraud detection and data security solutions are also in high demand, as companies seek to protect against threats and maintain customer trust. Data analytics platforms, including cloud-based offeri

Analysis of ‘NFL Combine - Performance Data (2009 - 2019)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/redlineracer/nfl-combine-performance-data-2009-2019 on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

This dataset contains information from the NFL Combine (2009 to 2019), including the results from sports performance tests and draft outcomes.

Content

As sports statistics are in the public domain, this database was freely downloaded from https://www.pro-football-reference.com/

Acknowledgements

I appreciate the efforts of https://www.pro-football-reference.com/ in collating and hosting sports related data, and Kaggle for providing a platform for sharing datasets and knowledge.

Inspiration

This dataset is useful for beginners and intermediate users, where they can practice visualisations, analytics, imputation, data cleaning/wrangling, and classification modelling. For example: What are the variables of importance in predicing round pick or draft status? Which school has the highest number of players being drafted into NFL? What position type or player type is most represented at the NFL Combine? Do drafted and undrafted players perform differently on performance tests?

--- Original source retains full ownership of the source dataset ---

The interpretation of biological data sets is essential for generating hypotheses that guide research, yet modern methods of global analysis challenge our ability to discern meaningful patterns and then convey results in a way that can be easily appreciated. Proteomic data is especially challenging because mass spectrometry detectors often miss peptides in complex samples, resulting in sparsely populated data sets. Using the R programming language and techniques from the field of pattern recognition, we have devised methods to resolve and evaluate clusters of proteins related by their pattern of expression in different samples in proteomic data sets. We examined tyrosine phosphoproteomic data from lung cancer samples. We calculated dissimilarities between the proteins based on Pearson or Spearman correlations and on Euclidean distances, whilst dealing with large amounts of missing data. The dissimilarities were then used as feature vectors in clustering and visualization algorithms. The quality of the clusterings and visualizations were evaluated internally based on the primary data and externally based on gene ontology and protein interaction networks. The results show that t-distributed stochastic neighbor embedding (t-SNE) followed by minimum spanning tree methods groups sparse proteomic data into meaningful clusters more effectively than other methods such as k-means and classical multidimensional scaling. Furthermore, our results show that using a combination of Spearman correlation and Euclidean distance as a dissimilarity representation increases the resolution of clusters. Our analyses show that many clusters contain one or more tyrosine kinases and include known effectors as well as proteins with no known interactions. Visualizing these clusters as networks elucidated previously unknown tyrosine kinase signal transduction pathways that drive cancer. Our approach can be applied to other data types, and can be easily adopted because open source software packages are employed.

Additional file 3: Example of report formatting request to send to the lab.