Statistical Analysis Software Market size was valued at USD 7,963.44 Million in 2023 and is projected to reach USD 13,023.63 Million by 2030, growing at a CAGR of 7.28% during the forecast period 2024-2030.

Global Statistical Analysis Software Market Drivers

The market drivers for the Statistical Analysis Software Market can be influenced by various factors. These may include:

Growing Data Complexity and Volume: The demand for sophisticated statistical analysis tools has been fueled by the exponential rise in data volume and complexity across a range of industries. Robust software solutions are necessary for organizations to evaluate and extract significant insights from huge datasets.
Growing Adoption of Data-Driven Decision-Making: Businesses are adopting a data-driven approach to decision-making at a faster rate. Utilizing statistical analysis tools, companies can extract meaningful insights from data to improve operational effectiveness and strategic planning.
Developments in Analytics and Machine Learning: As these fields continue to progress, statistical analysis software is now capable of more. These tools’ increasing popularity can be attributed to features like sophisticated modeling and predictive analytics.
A greater emphasis is being placed on business intelligence: Analytics and business intelligence are now essential components of corporate strategy. In order to provide business intelligence tools for studying trends, patterns, and performance measures, statistical analysis software is essential.
Increasing Need in Life Sciences and Healthcare: Large volumes of data are produced by the life sciences and healthcare sectors, necessitating complex statistical analysis. The need for data-driven insights in clinical trials, medical research, and healthcare administration is driving the market for statistical analysis software.
Growth of Retail and E-Commerce: The retail and e-commerce industries use statistical analytic tools for inventory optimization, demand forecasting, and customer behavior analysis. The need for analytics tools is fueled in part by the expansion of online retail and data-driven marketing techniques.
Government Regulations and Initiatives: Statistical analysis is frequently required for regulatory reporting and compliance with government initiatives, particularly in the healthcare and finance sectors. In these regulated industries, statistical analysis software uptake is driven by this.
Big Data Analytics’s Emergence: As big data analytics has grown in popularity, there has been a demand for advanced tools that can handle and analyze enormous datasets effectively. Software for statistical analysis is essential for deriving valuable conclusions from large amounts of data.
Demand for Real-Time Analytics: In order to make deft judgments fast, there is a growing need for real-time analytics. Many different businesses have a significant demand for statistical analysis software that provides real-time data processing and analysis capabilities.
Growing Awareness and Education: As more people become aware of the advantages of using statistical analysis in decision-making, its use has expanded across a range of academic and research institutions. The market for statistical analysis software is influenced by the academic sector.
Trends in Remote Work: As more people around the world work from home, they are depending more on digital tools and analytics to collaborate and make decisions. Software for statistical analysis makes it possible for distant teams to efficiently examine data and exchange findings.

This statistic depicts the distribution of tools used to compile data and present analytics and/or reports to management, according to a marketing survey of C-level executives, conducted in December 2015 by Black Ink. As of December 2015, 9 percent of respondents used statistical modeling tools, such as IBM's SPSS or the SAS Institute's Statistical Analysis System package, to compile and present their reports.

Explore the Statistical Analysis Software Market trends! Covers key players, growth rate 10.6% CAGR, market size $15.49 Billion, and forecasts to 2033. Get insights now!

The statistic shows the analytics tools currently in use by business organizations in the United States, as well as the analytics tools respondents believe they will be using in two years, according to a 2015 survey conducted by the Harvard Business Review Analytics Service. As of 2015, 73 percent of respondents believed they were going to use predictive analytics for data analysis in two years' time.

The size and share of the market is categorized based on Application (Statistical analysis tools, Data visualization tools, Predictive analytics software, Data mining tools, Business intelligence tools) and Product (Data analysis, Business insights, Market research, Performance management, Forecasting) and geographical regions (North America, Europe, Asia-Pacific, South America, and Middle-East and Africa).

This dataset contains all of the supporting materials to accompany Helsel, D.R., Hirsch, R.M., Ryberg, K.R., Archfield, S.A., and Gilroy, E.J., 2020, Statistical methods in water resources: U.S. Geological Survey Techniques and Methods, book 4, chapter A3, 454 p., https://doi.org/10.3133/tm4a3. [Supersedes USGS Techniques of Water-Resources Investigations, book 4, chapter A3, version 1.1.]. Supplemental material (SM) for each chapter are available to re-create all examples and figures, and to solve the exercises at the end of each chapter, with relevant datasets provided in an electronic format readable by R. The SM provide (1) datasets as .Rdata files for immediate input into R, (2) datasets as .csv files for input into R or for use with other software programs, (3) R functions that are used in the textbook but not part of a published R package, (4) R scripts to produce virtually all of the figures in the book, and (5) solutions to the exercises as .html and .Rmd files. The suff ...

This is the replication package for the analysis done in the paper "Evolution of statistical analysis in empirical software engineering research: Current state and steps forward" (DOI: https://doi.org/10.1016/j.jss.2019.07.002, preprint: https://arxiv.org/abs/1706.00933).

The package includes CSV files with data on statistical usage extracted from 5 journals in SE (EMSE, IST, JSS, TOSEM, TSE). The data was extracted from papers between 2001 - 2015. The package also contains forms, scripts and figures (generated using the scripts) used in the paper.

The extraction tool mentioned in the paper is available in dockerhub via: https://hub.docker.com/r/robertfeldt/sept

In 2023, Morningstar Advisor Workstation was by far the most popular data analytics software worldwide. According to a survey carried out between December 2022 and March 2023, the market share of Morningstar Advisor Workstation was 23.81 percent. It was followed by Riskalyze Elite, with 12.21 percent, and YCharts, with 10.82 percent.

Overview

GMAT is a feature rich system containing high fidelity space system models, optimization and targeting,
built in scripting and programming infrastructure, and customizable plots, reports and data
products, to enable flexible analysis and solutions for custom and unique applications. GMAT can
be driven from a fully featured, interactive GUI or from a custom script language. Here are some
of GMAT’s key features broken down by feature group.

Dynamics and Environment Modelling

• High fidelity dynamics models including harmonic gravity, drag, tides, and relativistic corrections
• High fidelity spacecraft modeling
• Formations and constellations
• Impulsive and finite maneuver modeling and optimization
• Propulsion system modeling including tanks and thrusters
• Solar System modeling including high fidelity ephemerides, custom celestial bodies, libration points, and barycenters
• Rich set of coordinate system including J2000, ICRF, fixed, rotating, topocentric, and many others
• SPICE kernel propagation
• Propagators that naturally synchronize epochs of multiple vehicles and avoid fixed step integration
• and interpolation

Plotting, Reporting and Product Generation

• Interactive 3-D graphics
• Customizable data plots and reports
• Post computation animation
• CCSDS, SPK, and Code-500 ephemeris generation

Optimization and Targeting

• Boundary value targeters
• Nonlinear, constrained optimization
• Custom, scriptable cost functions
• Custom, scriptable nonlinear equality and inequality constraint functions
• Custom targeter controls and constraints

Programming Infrastructure

• User defined variables, arrays, and strings
• User defined equations using MATLAB syntax. (i.e. overloaded array operation)
• Control flow such as If, For, and While loops for custom applications
• Matlab interface
• Built in parameters and calculations in multiple coordinate systems

Interfaces

• Fully featured, interactive GUI that makes simple analysis quick and easy
• Custom scripting language that makes complex, custom analysis possible
• Matlab interface for custom external simulations and calculations
• File interface for the TCOPS Vector Hold

We present GobyWeb, a web-based system that facilitates the management and analysis of high-throughput sequencing (HTS) projects. The software provides integrated support for a broad set of HTS analyses and offers a simple plugin extension mechanism. Analyses currently supported include quantification of gene expression for messenger and small RNA sequencing, estimation of DNA methylation (i.e., reduced bisulfite sequencing and whole genome methyl-seq), or the detection of pathogens in sequenced data. In contrast to previous analysis pipelines developed for analysis of HTS data, GobyWeb requires significantly less storage space, runs analyses efficiently on a parallel grid, scales gracefully to process tens or hundreds of multi-gigabyte samples, yet can be used effectively by researchers who are comfortable using a web browser. We conducted performance evaluations of the software and found it to either outperform or have similar performance to analysis programs developed for specialized analyses of HTS data. We found that most biologists who took a one-hour GobyWeb training session were readily able to analyze RNA-Seq data with state of the art analysis tools. GobyWeb can be obtained at http://gobyweb.campagnelab.org and is freely available for non-commercial use. GobyWeb plugins are distributed in source code and licensed under the open source LGPL3 license to facilitate code inspection, reuse and independent extensions http://github.com/CampagneLaboratory/gobyweb2-plugins.

The IRI Data Library is a powerful and freely accessible online data repository and analysis tool that allows a user to view, manipulate, and download over 400 climate-related data sets through a …Show full descriptionThe IRI Data Library is a powerful and freely accessible online data repository and analysis tool that allows a user to view, manipulate, and download over 400 climate-related data sets through a standard web browser. The Data Library contains a wide variety of publicly available data sets, including station and gridded atmospheric and oceanic observations and analyses, model-based analyses and forecasts, and land surface and vegetation data sets, from a range of sources. It includes a flexible, interactive data viewer that allows a user to visualize. multi-dimensional data sets in several combinations, create animations, and customize and download plots and maps in a variety of image formats. The Data Library is also a powerful computational engine that can perform analyses of varying complexity using an extensive array of statistical analysis tools. Online tutorials and function documentation are available to aid the user in applying these tools to the holdings available in the Data Library. Data sets and the results of any calculations performed by the user can be downloaded in a wide variety of file formats, from simple ascii text to GIS-compatible files to fully self-describing formats, or transferred directly to software applications that use the OPeNDAP protocol. This flexibility allows the Data Library to be used as a collaborative tool among different disciplines and to build new data discovery and analysis tools.

The global visual data analysis tool market is experiencing robust growth, driven by the increasing need for businesses to extract actionable insights from ever-expanding datasets. The market, currently valued at approximately $15 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 18% from 2025 to 2033. This significant expansion is fueled by several key factors. The proliferation of big data, coupled with the rising adoption of cloud-based solutions and advanced analytics techniques, empowers organizations across various sectors – including banking, manufacturing, and government – to make data-driven decisions. Furthermore, the continuous innovation in visualization technologies, offering more intuitive and user-friendly interfaces, is broadening accessibility and accelerating market penetration. The growing demand for real-time data analysis and predictive modeling further contributes to the market's upward trajectory. Despite the significant growth potential, the market faces certain challenges. High implementation costs, particularly for on-premises solutions, and the need for specialized skills to effectively utilize these tools can act as restraints for smaller businesses. However, the emergence of affordable cloud-based alternatives and increased availability of training programs are gradually mitigating these barriers. The market segmentation reveals a clear preference towards cloud-based solutions due to their scalability, flexibility, and cost-effectiveness. The banking and finance sectors, followed by manufacturing and consultancy, represent the largest market segments. Key players like Tableau, Microsoft, and Salesforce are driving innovation and shaping market competition through continuous product enhancements and strategic acquisitions. The geographical landscape displays strong growth potential across North America and Europe, while Asia-Pacific is expected to emerge as a significant market in the coming years.

Data Quality Management Software Market size was valued at USD 4.32 Billion in 2023 and is projected to reach USD 10.73 Billion by 2030, growing at a CAGR of 17.75% during the forecast period 2024-2030.

Global Data Quality Management Software Market Drivers

The growth and development of the Data Quality Management Software Market can be credited with a few key market drivers. Several of the major market drivers are listed below:

Growing Data Volumes: Organizations are facing difficulties in managing and guaranteeing the quality of massive volumes of data due to the exponential growth of data generated by consumers and businesses. Organizations can identify, clean up, and preserve high-quality data from a variety of data sources and formats with the use of data quality management software.
Increasing Complexity of Data Ecosystems: Organizations function within ever-more-complex data ecosystems, which are made up of a variety of systems, formats, and data sources. Software for data quality management enables the integration, standardization, and validation of data from various sources, guaranteeing accuracy and consistency throughout the data landscape.
Regulatory Compliance Requirements: Organizations must maintain accurate, complete, and secure data in order to comply with regulations like the GDPR, CCPA, HIPAA, and others. Data quality management software ensures data accuracy, integrity, and privacy, which assists organizations in meeting regulatory requirements.
Growing Adoption of Business Intelligence and Analytics: As BI and analytics tools are used more frequently for data-driven decision-making, there is a greater need for high-quality data. With the help of data quality management software, businesses can extract actionable insights and generate significant business value by cleaning, enriching, and preparing data for analytics.
Focus on Customer Experience: Put the Customer Experience First: Businesses understand that providing excellent customer experiences requires high-quality data. By ensuring data accuracy, consistency, and completeness across customer touchpoints, data quality management software assists businesses in fostering more individualized interactions and higher customer satisfaction.
Initiatives for Data Migration and Integration: Organizations must clean up, transform, and move data across heterogeneous environments as part of data migration and integration projects like cloud migration, system upgrades, and mergers and acquisitions. Software for managing data quality offers procedures and instruments to guarantee the accuracy and consistency of transferred data.
Need for Data Governance and Stewardship: The implementation of efficient data governance and stewardship practises is imperative to guarantee data quality, consistency, and compliance. Data governance initiatives are supported by data quality management software, which offers features like rule-based validation, data profiling, and lineage tracking.
Operational Efficiency and Cost Reduction: Inadequate data quality can lead to errors, higher operating costs, and inefficiencies for organizations. By guaranteeing high-quality data across business processes, data quality management software helps organizations increase operational efficiency, decrease errors, and minimize rework.

Prior to statistical analysis of mass spectrometry (MS) data, quality control (QC) of the identified biomolecule peak intensities is imperative for reducing process-based sources of variation and extreme biological outliers. Without this step, statistical results can be biased. Additionally, liquid chromatography–MS proteomics data present inherent challenges due to large amounts of missing data that require special consideration during statistical analysis. While a number of R packages exist to address these challenges individually, there is no single R package that addresses all of them. We present pmartR, an open-source R package, for QC (filtering and normalization), exploratory data analysis (EDA), visualization, and statistical analysis robust to missing data. Example analysis using proteomics data from a mouse study comparing smoke exposure to control demonstrates the core functionality of the package and highlights the capabilities for handling missing data. In particular, using a combined quantitative and qualitative statistical test, 19 proteins whose statistical significance would have been missed by a quantitative test alone were identified. The pmartR package provides a single software tool for QC, EDA, and statistical comparisons of MS data that is robust to missing data and includes numerous visualization capabilities.

The Report Covers Global Data Quality Tool Market Analysis & Growth and it is Segmented by Deployment Type (On-Premise, Cloud-based), Organization Size (Small and Medium Enterprises, Large Enterprises), Component (Software, Services), End-user Vertical (BFSI, Government, IT and Telecom, Retail and E-commerce, Healthcare), and Geography (North America, Europe, Asia-Pacific, Latin America and Middle East and Africa). The market sizes and forecasts are provided in terms of value (USD million) for all the above segments.

This statistic illustrates the share of instruments adopted by small and medium-sized businesses in Italy in 2016. In 2016, only three percent of the respondents reported that there was no data software dedicated solutions because data analysis was outsourced.

Market Size and Growth: The global AI tools for data analysis market was valued at approximately USD 24,160 million in 2025 and is projected to expand at a CAGR of XX% during the forecast period from 2025 to 2033, reaching a valuation of over USD XX million by 2033. The market growth is attributed to increasing adoption of AI and machine learning (ML) technologies to automate and enhance data analysis processes. Drivers, Trends, and Restraints: Key drivers of the market include the growing volume and complexity of data, the need for real-time insights, and the increasing demand for predictive analytics. Emerging trends such as cloud-based deployment, self-service analytics, and augmented data analysis are further fueling market growth. However, challenges such as data privacy concerns and the lack of skilled professionals in some regions may hinder market expansion.

Large-scale quantitative analyses of biological systems are often performed with few replicate experiments, leading to multiple nonidentical data sets due to missing values. For example, mass spectrometry driven proteomics experiments are frequently performed with few biological or technical replicates due to sample-scarcity or due to duty-cycle or sensitivity constraints, or limited capacity of the available instrumentation, leading to incomplete results where detection of significant feature changes becomes a challenge. This problem is further exacerbated for the detection of significant changes on the peptide level, for example, in phospho-proteomics experiments. In order to assess the extent of this problem and the implications for large-scale proteome analysis, we investigated and optimized the performance of three statistical approaches by using simulated and experimental data sets with varying numbers of missing values. We applied three tools, including standard t test, moderated t test, also known as limma, and rank products for the detection of significantly changing features in simulated and experimental proteomics data sets with missing values. The rank product method was improved to work with data sets containing missing values. Extensive analysis of simulated and experimental data sets revealed that the performance of the statistical analysis tools depended on simple properties of the data sets. High-confidence results were obtained by using the limma and rank products methods for analyses of triplicate data sets that exhibited more than 1000 features and more than 50% missing values. The maximum number of differentially represented features was identified by using limma and rank products methods in a complementary manner. We therefore recommend combined usage of these methods as a novel and optimal way to detect significantly changing features in these data sets. This approach is suitable for large quantitative data sets from stable isotope labeling and mass spectrometry experiments and should be applicable to large data sets of any type. An R script that implements the improved rank products algorithm and the combined analysis is available.

dataset and Octave/MatLab codes/scripts for data analysis Background: Methods for p-value correction are criticized for either increasing Type II error or improperly reducing Type I error. This problem is worse when dealing with thousands or even hundreds of paired comparisons between waves or images which are performed point-to-point. This text considers patterns in probability vectors resulting from multiple point-to-point comparisons between two event-related potentials (ERP) waves (mass univariate analysis) to correct p-values, where clusters of signiticant p-values may indicate true H0 rejection. New method: We used ERP data from normal subjects and other ones with attention deficit hyperactivity disorder (ADHD) under a cued forced two-choice test to study attention. The decimal logarithm of the p-vector (p') was convolved with a Gaussian window whose length was set as the shortest lag above which autocorrelation of each ERP wave may be assumed to have vanished. To verify the reliability of the present correction method, we realized Monte-Carlo simulations (MC) to (1) evaluate confidence intervals of rejected and non-rejected areas of our data, (2) to evaluate differences between corrected and uncorrected p-vectors or simulated ones in terms of distribution of significant p-values, and (3) to empirically verify rate of type-I error (comparing 10,000 pairs of mixed samples whit control and ADHD subjects). Results: the present method reduced the range of p'-values that did not show covariance with neighbors (type I and also type-II errors). The differences between simulation or raw p-vector and corrected p-vectors were, respectively, minimal and maximal for window length set by autocorrelation in p-vector convolution. Comparison with existing methods: Our method was less conservative while FDR methods rejected basically all significant p-values for Pz and O2 channels. The MC simulations, gold-standard method for error correction, presented 2.78±4.83% of difference (all 20 channels) from p-vector after correction, while difference between raw and corrected p-vector was 5,96±5.00% (p = 0.0003). Conclusion: As a cluster-based correction, the present new method seems to be biological and statistically suitable to correct p-values in mass univariate analysis of ERP waves, which adopts adaptive parameters to set correction.