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.

The purpose of this project is to improve the accuracy of statistical software by providing reference datasets with certified computational results that enable the objective evaluation of statistical software. Currently datasets and certified values are provided for assessing the accuracy of software for univariate statistics, linear regression, nonlinear regression, and analysis of variance. The collection includes both generated and 'real-world' data of varying levels of difficulty. Generated datasets are designed to challenge specific computations. These include the classic Wampler datasets for testing linear regression algorithms and the Simon & Lesage datasets for testing analysis of variance algorithms. Real-world data include challenging datasets such as the Longley data for linear regression, and more benign datasets such as the Daniel & Wood data for nonlinear regression. Certified values are 'best-available' solutions. The certification procedure is described in the web pages for each statistical method. Datasets are ordered by level of difficulty (lower, average, and higher). Strictly speaking the level of difficulty of a dataset depends on the algorithm. These levels are merely provided as rough guidance for the user. Producing correct results on all datasets of higher difficulty does not imply that your software will pass all datasets of average or even lower difficulty. Similarly, producing correct results for all datasets in this collection does not imply that your software will do the same for your particular dataset. It will, however, provide some degree of assurance, in the sense that your package provides correct results for datasets known to yield incorrect results for some software. The Statistical Reference Datasets is also supported by the Standard Reference Data Program.

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 ...

Data Analysis Software Market size was valued at USD 79.15 Billion in 2024 and is projected to reach USD 176.57 Billion by 2031, growing at a CAGR of 10.55% during the forecast period 2024-2031.

Global Data Analysis Software Market Drivers

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

Technological Developments: The need for more advanced data analysis software is being driven by the quick development of data analytics technologies, such as machine learning, artificial intelligence, and big data analytics.
Growing Data Volume: To extract useful insights from massive datasets, powerful data analysis software is required due to the exponential expansion of data generated from multiple sources, including social media, IoT devices, and sensors.
Business Intelligence Requirements: To obtain a competitive edge, organisations in all sectors are depending more and more on data-driven decision-making processes. This encourages the use of data analysis software to find strategic insights by analysing and visualising large, complicated datasets.
Regulatory Compliance: In order to maintain compliance and safeguard sensitive data, firms must invest in data analysis software with strong security capabilities. Examples of these rules and compliance requirements are the CCPA and GDPR.
Growing Need for Real-time Analytics: Companies are under increasing pressure to make decisions quickly, which has led to a growing need for real-time analytics capabilities provided by sophisticated data analysis tools. These skills allow organisations to react quickly to market changes and gain insights.
Cloud Adoption: As a result of the transition to cloud computing infrastructure, businesses of all sizes are adopting cloud-based data analysis software since it gives them access to scalable and affordable data analysis solutions.
The emergence of predictive analytics is being driven by the need for data analysis tools with sophisticated predictive modelling and forecasting skills. Predictive analytics is being used to forecast future trends, customer behaviour, and market dynamics.
Sector-specific Solutions: Businesses looking for specialised analytics solutions to handle industry-specific opportunities and challenges are adopting more vertical-specific data analysis software, which is designed to match the particular needs of sectors like healthcare, finance, retail, and manufacturing.

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

To create the dataset, the top 10 countries leading in the incidence of COVID-19 in the world were selected as of October 22, 2020 (on the eve of the second full of pandemics), which are presented in the Global 500 ranking for 2020: USA, India, Brazil, Russia, Spain, France and Mexico. For each of these countries, no more than 10 of the largest transnational corporations included in the Global 500 rating for 2020 and 2019 were selected separately. The arithmetic averages were calculated and the change (increase) in indicators such as profitability and profitability of enterprises, their ranking position (competitiveness), asset value and number of employees. The arithmetic mean values of these indicators for all countries of the sample were found, characterizing the situation in international entrepreneurship as a whole in the context of the COVID-19 crisis in 2020 on the eve of the second wave of the pandemic. The data is collected in a general Microsoft Excel table. Dataset is a unique database that combines COVID-19 statistics and entrepreneurship statistics. The dataset is flexible data that can be supplemented with data from other countries and newer statistics on the COVID-19 pandemic. Due to the fact that the data in the dataset are not ready-made numbers, but formulas, when adding and / or changing the values in the original table at the beginning of the dataset, most of the subsequent tables will be automatically recalculated and the graphs will be updated. This allows the dataset to be used not just as an array of data, but as an analytical tool for automating scientific research on the impact of the COVID-19 pandemic and crisis on international entrepreneurship. The dataset includes not only tabular data, but also charts that provide data visualization. The dataset contains not only actual, but also forecast data on morbidity and mortality from COVID-19 for the period of the second wave of the pandemic in 2020. The forecasts are presented in the form of a normal distribution of predicted values and the probability of their occurrence in practice. This allows for a broad scenario analysis of the impact of the COVID-19 pandemic and crisis on international entrepreneurship, substituting various predicted morbidity and mortality rates in risk assessment tables and obtaining automatically calculated consequences (changes) on the characteristics of international entrepreneurship. It is also possible to substitute the actual values identified in the process and following the results of the second wave of the pandemic to check the reliability of pre-made forecasts and conduct a plan-fact analysis. The dataset contains not only the numerical values of the initial and predicted values of the set of studied indicators, but also their qualitative interpretation, reflecting the presence and level of risks of a pandemic and COVID-19 crisis for international entrepreneurship.

As high-throughput methods become more common, training undergraduates to analyze data must include having them generate informative summaries of large datasets. This flexible case study provides an opportunity for undergraduate students to become familiar with the capabilities of R programming in the context of high-throughput evolutionary data collected using macroarrays. The story line introduces a recent graduate hired at a biotech firm and tasked with analysis and visualization of changes in gene expression from 20,000 generations of the Lenski Lab’s Long-Term Evolution Experiment (LTEE). Our main character is not familiar with R and is guided by a coworker to learn about this platform. Initially this involves a step-by-step analysis of the small Iris dataset built into R which includes sepal and petal length of three species of irises. Practice calculating summary statistics and correlations, and making histograms and scatter plots, prepares the protagonist to perform similar analyses with the LTEE dataset. In the LTEE module, students analyze gene expression data from the long-term evolutionary experiments, developing their skills in manipulating and interpreting large scientific datasets through visualizations and statistical analysis. Prerequisite knowledge is basic statistics, the Central Dogma, and basic evolutionary principles. The Iris module provides hands-on experience using R programming to explore and visualize a simple dataset; it can be used independently as an introduction to R for biological data or skipped if students already have some experience with R. Both modules emphasize understanding the utility of R, rather than creation of original code. Pilot testing showed the case study was well-received by students and faculty, who described it as a clear introduction to R and appreciated the value of R for visualizing and analyzing large datasets.

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.

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.

This data asset includes the datasets used to power the MCDATA tool on the Tableau server.

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.

Transparency in data visualization is an essential ingredient for scientific communication. The traditional approach of visualizing continuous quantitative data solely in the form of summary statistics (i.e., measures of central tendency and dispersion) has repeatedly been criticized for not revealing the underlying raw data distribution. Remarkably, however, systematic and easy-to-use solutions for raw data visualization using the most commonly reported statistical software package for data analysis, IBM SPSS Statistics, are missing. Here, a comprehensive collection of more than 100 SPSS syntax files and an SPSS dataset template is presented and made freely available that allow the creation of transparent graphs for one-sample designs, for one- and two-factorial between-subject designs, for selected one- and two-factorial within-subject designs as well as for selected two-factorial mixed designs and, with some creativity, even beyond (e.g., three-factorial mixed-designs). Depending on graph type (e.g., pure dot plot, box plot, and line plot), raw data can be displayed along with standard measures of central tendency (arithmetic mean and median) and dispersion (95% CI and SD). The free-to-use syntax can also be modified to match with individual needs. A variety of example applications of syntax are illustrated in a tutorial-like fashion along with fictitious datasets accompanying this contribution. The syntax collection is hoped to provide researchers, students, teachers, and others working with SPSS a valuable tool to move towards more transparency in data visualization.

The global big data and business analytics (BDA) market was valued at 168.8 billion U.S. dollars in 2018 and is forecast to grow to 215.7 billion U.S. dollars by 2021. In 2021, more than half of BDA spending will go towards services. IT services is projected to make up around 85 billion U.S. dollars, and business services will account for the remainder. Big data High volume, high velocity and high variety: one or more of these characteristics is used to define big data, the kind of data sets that are too large or too complex for traditional data processing applications. Fast-growing mobile data traffic, cloud computing traffic, as well as the rapid development of technologies such as artificial intelligence (AI) and the Internet of Things (IoT) all contribute to the increasing volume and complexity of data sets. For example, connected IoT devices are projected to generate 79.4 ZBs of data in 2025. Business analytics Advanced analytics tools, such as predictive analytics and data mining, help to extract value from the data and generate business insights. The size of the business intelligence and analytics software application market is forecast to reach around 16.5 billion U.S. dollars in 2022. Growth in this market is driven by a focus on digital transformation, a demand for data visualization dashboards, and an increased adoption of cloud.

This dataset supports our study "Statistical Analysis of Fluorescence Intensity Transients with Bayesian Methods," which introduces Fluorescence Intensity Trace Statistical Analysis (FITSA), a Bayesian approach for direct analysis of fluorescence intensity traces. From these traces, FITSA estimates diffusion coefficient and molecular brightness. The repository contains all fluorescence intensity traces used in our comparative analysis of FITSA and fluorescence correlation spectroscopy (FCS). A README file describes the data structure. We provide both synthetic and experimental datasets that demonstrate various applications of FITSA. When combined with our separately published code, these datasets enable reproduction of our analysis and support further methodological development in the field. Based on our analysis of these traces, we demonstrate that FITSA achieves precision comparable to FCS while requiring substantially fewer photons and shorter measurement times., , , # Experimental and synthetic datasets supporting FITSA: Statistical analysis of fluorescence intensity transients with Bayesian methods

This repository contains the complete set of traces used in the study:

"Statistical Analysis of Fluorescence Intensity Transients with Bayesian Methods"

Authors: Hamed Karimi, Martin Laasmaa, Margus Pihlak, Marko Vendelin

Repository Structure

The datasets are organized in subfolders corresponding to the figures in the study. Since some datasets were used across multiple figures, all relevant figure numbers are included in the subfolder names.

Synthetic Datasets

Multiple synthetic datasets were generated with varying molecular brightness levels, as shown in Figure 5 and associated Supporting Materials figures. These datasets are stored in dedicated subfolders, with the molecular brightness indicated in the subfolder name. For example:

• mu_mol-50k represents data with a molecular brightness of 50,000 1/s

Additional Experimental Dat...,

The aim of this survey was to collect feedback about existing training programmes in statistical analysis for postgraduate researchers at the University of Edinburgh, as well as respondents' preferred methods for training, and their requirements for new courses. The survey was circulated via e-mail to research staff and postgraduate researchers across three colleges of the University of Edinburgh: the College of Arts, Humanities and Social Sciences; the College of Science and Engineering; and the College of Medicine and Veterinary Medicine. The survey was conducted on-line using the Bristol Online Survey tool, March through July 2017. 90 responses were received. The Scoping Statistical Analysis Support project, funded by Information Services Innovation Fund, aims to increase visibility and raise the profile of the Research Data Service by: understanding how statistical analysis support is conducted across University of Edinburgh Schools; scoping existing support mechanisms and models for students, researchers and teachers; identifying services and support that would satisfy existing or future demand.

BestPlace is an innovative retail data and analytics tool created explicitly for medium and enterprise-level CPG/FMCG companies. It's designed to revolutionize your retail data analysis approach by adding a strategic location-based perspective to your existing database. This perspective enriches your data landscape and allows your business to understand better and cater to shopping behavior. An In-Depth Approach to Retail Analytics Unlike conventional analytics tools, BestPlace delves deep into each store location details, providing a comprehensive analysis of your retail database. We leverage unique tools and methodologies to extract, analyze, and compile data. Our processes have been accurately designed to provide a holistic view of your business, equipping you with the information you need to make data-driven data-backed decisions. Amplifying Your Database with BestPlace At BestPlace, we understand the importance of a robust and informative retail database design. We don't just add new stores to your database; we enrich each store with vital characteristics and factors. These enhancements come from open cartographic sources such as Google Maps and our proprietary GIS database, all carefully collected and curated by our experienced data analysts. Store Features We enrich your retail database with an array of store features, which include but are not limited to: Number of reviews Average ratings Operational hours Categories relevant to each point Our attention to detail ensures your retail database becomes a powerful tool for understanding customer interactions and preferences.

Extensive Use Cases BestPlace's capabilities stretch across various applications, offering value in areas such as: Competition Analysis: Identify your competitors, analyze their performance, and understand your standing in the market with our extensive POI database and retail data analytics capabilities. New Location Search: Use our rich retail store database to identify ideal locations for store expansions based on foot traffic data, proximity to key points, and potential customer demographics.

Success.ai’s Technographic Data for the North American IT Industry provides unparalleled visibility into the technology stacks, operational frameworks, and key decision-makers powering 30 million-plus businesses across the region’s tech landscape. From established software giants to emerging SaaS startups, this dataset offers verified contacts, firmographic details, and in-depth insights into each company’s technology adoption, infrastructure choices, and vendor partnerships.

Whether you’re aiming to personalize sales pitches, guide product roadmaps, or streamline account-based marketing efforts, Success.ai’s continuously updated and AI-validated data ensures you make data-driven decisions and achieve strategic growth, all backed by our Best Price Guarantee.

Why Choose Success.ai’s North American IT Technographic Data?

1. Comprehensive Technology Insights

   • Access detailed information on software stacks, cloud platforms, hosting providers, cybersecurity tools, CRM solutions, and more.
   • AI-driven validation ensures 99% accuracy, minimizing guesswork and empowering confident engagement with the right tech-focused audiences.

2. Regionally Tailored Focus

   • Includes profiles of IT businesses from Silicon Valley startups to East Coast analytics firms, covering major tech hubs and underserved markets alike.
   • Understand technology adoption patterns influenced by regional trends, regulatory environments, and innovation ecosystems unique to North America.

3. Continuously Updated Datasets

   • Real-time updates reflect emerging vendors, newly adopted tools, infrastructure upgrades, and shifts in IT leadership.
   • Stay aligned with evolving market conditions, competitive landscapes, and customer requirements.

4. Ethical and Compliant

   • Adheres to GDPR, CCPA, and other privacy regulations, ensuring responsible data usage and ethical outreach practices.

Data Highlights:

• 30M+ Verified Business Profiles: Gain insights into software companies, IT consultancies, data analytics providers, cloud integrators, and cybersecurity startups.
• Comprehensive Firmographics: Identify company sizes, revenue ranges, workforce composition, and operational footprints.
• Vendor and Stack Details: Understand which CRMs, ERPs, marketing automation tools, or development frameworks companies rely on.
• Verified Decision-Maker Contacts: Engage with CEOs, CTOs, CIOs, IT directors, DevOps managers, and product leads shaping procurement and integration strategies.

Key Features of the Dataset:

1. Technographic Decision-Maker Profiles

   • Identify and connect with executives, architects, and engineers overseeing vendor selection, digital transformation, and IT investments.
   • Target professionals who influence software procurement, SaaS migrations, and long-term technology roadmaps.

2. Advanced Filters for Precision Targeting

   • Refine outreach by technology categories, usage intensity, company size, region, or industry verticals.
   • Tailor campaigns to align with specific pain points, growth opportunities, or emerging tech trends like AI, IoT, or edge computing.

3. AI-Driven Enrichment

   • Profiles enriched with actionable data enable personalized messaging, highlight unique value propositions, and boost engagement with IT stakeholders.

Strategic Use Cases:

1. Sales and Account-Based Marketing

   • Present IT solutions, infrastructure services, or software licenses directly to companies with compatible tech stacks.
   • Identify warm leads who already use complementary tools, accelerating deal closures and improving conversion rates.

2. Product Development and Roadmap Planning

   • Analyze common technology adoption patterns, security tools, or workflow integrations to inform product enhancements.
   • Align feature sets with industry standards and emerging stacks, ensuring long-term relevance and customer satisfaction.

3. Competitive Analysis and Market Entry

   • Benchmark against leading IT providers, analyze technology maturity curves, and understand customer preferences for particular platforms.
   • Identify new markets or niches where your offering can fill technology gaps or improve operational efficiency.

4. Partnership and Ecosystem Building

   • Connect with partners offering complementary solutions, integration capabilities, or co-marketing opportunities.
   • Foster alliances with MSPs, VARs, or channel partners who can amplify distribution and support end-to-end solutions.

Why Choose Success.ai?

1. Best Price Guarantee

   • Gain access to premium-quality technographic data at competitive rates, ensuring high ROI for your sales, marketing, and product strategies.

2. Seamless Integration

   • Incorporate verified data into CRM systems, marketing automation platforms, or analytics dashboards via APIs or downloadable formats, streamlining workflows and decision-making.

3....

This dataset accompanies a study designed to test the temporal model hypothesis for the mechanism and treatment of central sensitization. This study uses a cohort retrospective multivariate analysis using a modified adaptive platform design. The analysis is done using the Halili physical therapy statistical analysis tool HPTSAT. The dataset includes raw table and expended results

This dataset combines the work of several different projects to create a seamless data set for the contiguous United States. Data from four regional Gap Analysis Projects and the LANDFIRE project were combined to make this dataset. In the northwestern United States (Idaho, Oregon, Montana, Washington and Wyoming) data in this map came from the Northwest Gap Analysis Project. In the southwestern United States (Colorado, Arizona, Nevada, New Mexico, and Utah) data used in this map came from the Southwest Gap Analysis Project. The data for Alabama, Florida, Georgia, Kentucky, North Carolina, South Carolina, Mississippi, Tennessee, and Virginia came from the Southeast Gap Analysis Project and the California data was generated by the updated California Gap land cover project. The Hawaii Gap Analysis project provided the data for Hawaii. In areas of the county (central U.S., Northeast, Alaska) that have not yet been covered by a regional Gap Analysis Project, data from the Landfire project was used. Similarities in the methods used by these projects made possible the combining of the data they derived into one seamless coverage. They all used multi-season satellite imagery (Landsat ETM+) from 1999-2001 in conjunction with digital elevation model (DEM) derived datasets (e.g. elevation, landform) to model natural and semi-natural vegetation. Vegetation classes were drawn from NatureServe's Ecological System Classification (Comer et al. 2003) or classes developed by the Hawaii Gap project. Additionally, all of the projects included land use classes that were employed to describe areas where natural vegetation has been altered. In many areas of the country these classes were derived from the National Land Cover Dataset (NLCD). For the majority of classes and, in most areas of the country, a decision tree classifier was used to discriminate ecological system types. In some areas of the country, more manual techniques were used to discriminate small patch systems and systems not distinguishable through topography. The data contains multiple levels of thematic detail. At the most detailed level natural vegetation is represented by NatureServe's Ecological System classification (or in Hawaii the Hawaii GAP classification). These most detailed classifications have been crosswalked to the five highest levels of the National Vegetation Classification (NVC), Class, Subclass, Formation, Division and Macrogroup. This crosswalk allows users to display and analyze the data at different levels of thematic resolution. Developed areas, or areas dominated by introduced species, timber harvest, or water are represented by other classes, collectively refered to as land use classes; these land use classes occur at each of the thematic levels. Raster data in both ArcGIS Grid and ERDAS Imagine format is available for download at http://gis1.usgs.gov/csas/gap/viewer/land_cover/Map.aspx Six layer files are included in the download packages to assist the user in displaying the data at each of the Thematic levels in ArcGIS. In adition to the raster datasets the data is available in Web Mapping Services (WMS) format for each of the six NVC classification levels (Class, Subclass, Formation, Division, Macrogroup, Ecological System) at the following links. http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Class_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Subclass_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Formation_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Division_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Macrogroup_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_Ecological_Systems_Landuse/MapServer

The global big data market is forecasted to grow to 103 billion U.S. dollars by 2027, more than double its expected market size in 2018. With a share of 45 percent, the software segment would become the large big data market segment by 2027.

What is Big data?

Big data is a term that refers to the kind of data sets that are too large or too complex for traditional data processing applications. It is defined as having one or some of the following characteristics: high volume, high velocity or high variety. Fast-growing mobile data traffic, cloud computing traffic, as well as the rapid development of technologies such as artificial intelligence (AI) and the Internet of Things (IoT) all contribute to the increasing volume and complexity of data sets.

Big data analytics

Advanced analytics tools, such as predictive analytics and data mining, help to extract value from the data and generate new business insights. The global big data and business analytics market was valued at 169 billion U.S. dollars in 2018 and is expected to grow to 274 billion U.S. dollars in 2022. As of November 2018, 45 percent of professionals in the market research industry reportedly used big data analytics as a research method.