In 2020, most enterprises according to respondents have multiple tools or applications to perform various data management functions. Of the functions, back up/recovery and data classification were the approaches used most frequently with multiple apps, according to 47 percent of respondents for both.

Many challenges come with adopting a mixed-environment model. In 2023 about 43 percent of respondents reported that data storage costs are one of the biggest challenges in this regard.

The statistic shows the share of data management components that are run in the cloud worldwide, as of October 2016. According to the survey, 42 percent of respondents indicated that their data warehouses were running in the cloud.

This collection contains a snapshot of the learning resource metadata from ESIP's Data management Training Clearinghouse (DMTC) associated with the closeout (March 30, 2023) of the Institute of Museum and Library Services funded (Award Number: LG-70-18-0092-18) Development of an Enhanced and Expanded Data Management Training Clearinghouse project. The shared metadata are a snapshot associated with the final reporting date for the project, and the associated data report is also based upon the same data snapshot on the same date.

The materials included in the collection consist of the following:

• esip-dev-02.edacnm.org.json.zip - a zip archive containing the metadata for 587 published learning resources as of March 30, 2023. These metadata include all publicly available metadata elements for the published learning resources with the exception of the metadata elements containing individual email addresses (submitter and contact) to reduce the exposure of these data.
• statistics.pdf - an automatically generated report summarizing information about the collection of materials in the DMTC Clearinghouse, including both published and unpublished learning resources. This report includes the numbers of published and unpublished resources through time; the number of learning resources within subject categories and detailed subject categories, the dates items assigned to each category were first added to the Clearinghouse, and the most recent data that items were added to that category; the distribution of learning resources across target audiences; and the frequency of keywords within the learning resource collection. This report is based on the metadata for published resourced included in this collection, and preliminary metadata for unpublished learning resources that are not included in the shared dataset.

The metadata fields consist of the following:

Fieldname	Description
abstract_data	A brief synopsis or abstract about the learning resource
abstract_format	Declaration for how the abstract description will be represented.
access_conditions	Conditions upon which the resource can be accessed beyond cost, e.g., login required.
access_cost	Yes or No choice stating whether othere is a fee for access to or use of the resource.
accessibililty_features_name	Content features of the resource, such as accessible media, alternatives and supported enhancements for accessibility.
accessibililty_summary	A human-readable summary of specific accessibility features or deficiencies.
author_names	List of authors for a resource derived from the given/first and family/last names of the personal author fields by the system
author_org - name - name_identifier - name_identifier_type	- Name of organization authoring the learning resource. - The unique identifier for the organization authoring the resource. - The identifier scheme associated with the unique identifier for the organization authoring the resource.
authors - givenName - familyName - name_identifier - name_identifier_type	- Given or first name of person(s) authoring the resource. - Last or family name of person(s) authoring the resource. - The unique identifier for the person(s) authoring the resource. - The identifier scheme associated with the unique identifier for the person(s) authoring the resource, e.g., ORCID.
citation	Preferred Form of Citation.
completion_time	Intended Time to Complete
contact - name - org - email	- Name of person(s) who has/have been asserted as the contact(s) for the resource in case of questions or follow-up by resource user. - Name of organization that has/have been asserted as the contact(s) for the resource in case of questions or follow-up by resource user. - (excluded) Contact email address.
contributor_orgs - name - name_identifier - name_identifier_type - type	- Name of organization that is a secondary contributor to the learningresource. A contributor can also be an individual person. - The unique identifier for the organization contributing to the resource. - The identifier scheme associated with the unique identifier for the organization contributing to the resource. - Type of contribution to the resource made by an organization.
contributors - familyName - givenName - name_identifier - name_identifier_type	- Last or family name of person(s) contributing to the resource. - Given or first name of person(s) contributing to the resource. - The unique identifier for the person(s) contributing to the resource. - The identifier scheme associated with the unique identifier for the person(s) contributing to the resource, e.g., ORCID.
contributors.type	Type of contribution to the resource made by a person.
created	The date on which the metadata record was first saved as part of the input workflow.
creator	The name of the person creating the MD record for a resource.
credential_status	Declaration of whether a credential is offered for comopletion of the resource.
ed_frameworks - name - description - nodes.name	- The name of the educational framework to which the resource is aligned, if any. An educational framework is a structured description of educational concepts such as a shared curriculum, syllabus or set of learning objectives, or a vocabulary for describing some other aspect of education such as educational levels or reading ability. - A description of one or more subcategories of an educational framework to which a resource is associated. - The name of a subcategory of an educational framework to which a resource is associated.
expertise_level	The skill level targeted for the topic being taught.
id	Unique identifier for the MD record generated by the system in UUID format.
keywords	Important phrases or words used to describe the resource.
language_primary	Original language in which the learning resource being described is published or made available.
languages_secondary	Additional languages in which the resource is tranlated or made available, if any.
license	A license for use of that applies to the resource, typically indicated by URL.
locator_data	The identifier for the learning resource used as part of a citation, if available.
locator_type	Designation of citation locatorr type, e.g., DOI, ARK, Handle.
lr_outcomes	Descriptions of what knowledge, skills or abilities students should learn from the resource.
lr_type	A characteristic that describes the predominant type or kind of learning resource.
media_type	Media type of resource.
modification_date	System generated date and time when MD record is modified.
notes	MD Record Input Notes
pub_status	Status of metadata record within the system, i.e., in-process, in-review, pre-pub-review, deprecate-request, deprecated or published.
published	Date of first broadcast / publication.
publisher	The organization credited with publishing or broadcasting the resource.
purpose	The purpose of the resource in the context of education; e.g., instruction, professional education, assessment.
rating	The aggregation of input from all user assessments evaluating users' reaction to the learning resource following Kirkpatrick's model of training evaluation.
ratings	Inputs from users assessing each user's reaction to the learning resource following Kirkpatrick's model of training evaluation.
resource_modification_date	Date in which the resource has last been modified from the original published or broadcast version.
status	System generated publication status of the resource w/in the registry as a yes for published or no for not published.
subject	Subject domain(s) toward which the resource is targeted. There may be more than one value for this field.
submitter_email	(excluded) Email address of

The Best Management Practices Statistical Estimator (BMPSE) version 1.2.0 was developed by the U.S. Geological Survey (USGS), in cooperation with the Federal Highway Administration (FHWA) Office of Project Delivery and Environmental Review to provide planning-level information about the performance of structural best management practices for decision makers, planners, and highway engineers to assess and mitigate possible adverse effects of highway and urban runoff on the Nation's receiving waters (Granato 2013, 2014; Granato and others, 2021). The BMPSE was assembled by using a Microsoft Access® database application to facilitate calculation of BMP performance statistics. Granato (2014) developed quantitative methods to estimate values of the trapezoidal-distribution statistics, correlation coefficients, and the minimum irreducible concentration (MIC) from available data. Granato (2014) developed the BMPSE to hold and process data from the International Stormwater Best Management Practices Database (BMPDB, www.bmpdatabase.org). Version 1.0 of the BMPSE contained a subset of the data from the 2012 version of the BMPDB; the current version of the BMPSE (1.2.0) contains a subset of the data from the December 2019 version of the BMPDB. Selected data from the BMPDB were screened for import into the BMPSE in consultation with Jane Clary, the data manager for the BMPDB. Modifications included identifying water quality constituents, making measurement units consistent, identifying paired inflow and outflow values, and converting BMPDB water quality values set as half the detection limit back to the detection limit. Total polycyclic aromatic hydrocarbons (PAH) values were added to the BMPSE from BMPDB data; they were calculated from individual PAH measurements at sites with enough data to calculate totals. The BMPSE tool can sort and rank the data, calculate plotting positions, calculate initial estimates, and calculate potential correlations to facilitate the distribution-fitting process (Granato, 2014). For water-quality ratio analysis the BMPSE generates the input files and the list of filenames for each constituent within the Graphical User Interface (GUI). The BMPSE calculates the Spearman’s rho (ρ) and Kendall’s tau (τ) correlation coefficients with their respective 95-percent confidence limits and the probability that each correlation coefficient value is not significantly different from zero by using standard methods (Granato, 2014). If the 95-percent confidence limit values are of the same sign, then the correlation coefficient is statistically different from zero. For hydrograph extension, the BMPSE calculates ρ and τ between the inflow volume and the hydrograph-extension values (Granato, 2014). For volume reduction, the BMPSE calculates ρ and τ between the inflow volume and the ratio of outflow to inflow volumes (Granato, 2014). For water-quality treatment, the BMPSE calculates ρ and τ between the inflow concentrations and the ratio of outflow to inflow concentrations (Granato, 2014; 2020). The BMPSE also calculates ρ between the inflow and the outflow concentrations when a water-quality treatment analysis is done. The current version (1.2.0) of the BMPSE also has the option to calculate urban-runoff quality statistics from inflows to BMPs by using computer code developed for the Highway Runoff Database (Granato and Cazenas, 2009;Granato, 2019). Granato, G.E., 2013, Stochastic empirical loading and dilution model (SELDM) version 1.0.0: U.S. Geological Survey Techniques and Methods, book 4, chap. C3, 112 p., CD-ROM https://pubs.usgs.gov/tm/04/c03 Granato, G.E., 2014, Statistics for stochastic modeling of volume reduction, hydrograph extension, and water-quality treatment by structural stormwater runoff best management practices (BMPs): U.S. Geological Survey Scientific Investigations Report 2014–5037, 37 p., http://dx.doi.org/10.3133/sir20145037. Granato, G.E., 2019, Highway-Runoff Database (HRDB) Version 1.1.0: U.S. Geological Survey data release, https://doi.org/10.5066/P94VL32J. Granato, G.E., and Cazenas, P.A., 2009, Highway-Runoff Database (HRDB Version 1.0)--A data warehouse and preprocessor for the stochastic empirical loading and dilution model: Washington, D.C., U.S. Department of Transportation, Federal Highway Administration, FHWA-HEP-09-004, 57 p. https://pubs.usgs.gov/sir/2009/5269/disc_content_100a_web/FHWA-HEP-09-004.pdf Granato, G.E., Spaetzel, A.B., and Medalie, L., 2021, Statistical methods for simulating structural stormwater runoff best management practices (BMPs) with the stochastic empirical loading and dilution model (SELDM): U.S. Geological Survey Scientific Investigations Report 2020–5136, 41 p., https://doi.org/10.3133/sir20205136

The global dynamic data management system market is likely to be valued at US$ 33,960.7 million in 2023. From 2023 to 2033, the market for dynamic data management system is likely to expand at a CAGR of 10.6% to reach US$ 1,24,745.1 million by 2033.

Data Points	Key Statistics
Global Dynamic Data Management System Market CAGR (2023 to 2033)	10.6%
Anticipated Market Value (2023)	US$ 33,960.7 million
Global Dynamic Data Management System Market (2033)	US$ 1,24,754.1 million

Report Scope

Report Attribute	Details
Market Value in 2023	US$ 33,960.7 million
Market Value in 2033	US$ 1,24,745.1 million
Growth Rate	CAGR of 10.6% from 2023 to 2033
Base Year for Estimation	2022
Historical Data	2018 to 2022
Forecast Period	2023 to 2033
Quantitative Units	Revenue in US$ million and CAGR from 2023 to 2033
Report Coverage	Revenue Forecast, Volume Forecast, Company Ranking, Competitive Landscape, Growth Factors, Trends and Pricing Analysis
Segments Covered	Deployment Type Verticals Region
Regions Covered	North America Europe Asia Pacific Middle East and Africa Latin America
Key Countries Profiled	United States Canada Brazil Mexico Germany United Kingdom France Spain Italy China Japan South Korea Malaysia Singapore Australia GCC South Africa Israel
Key Companies Profiled	Oracle Corporation Microsoft Corporation IBM Corporation SAP SE Teradata EMBARCADERO Inc. Couchbase Inc. BMC Software Inc. Actian Corporation SolarWinds MongoDB ManageEngine Altibase
Report Customization & Pricing	Available Upon Request

The Master Data Management (MDM) Tool market has become an essential component of data governance and enterprise information management, enabling organizations to maintain a single, accurate version of their critical business data. As businesses increasingly rely on data-driven decision-making, the demand for effect

The clinical data management system market size was over USD 3.4 billion in 2024 and is estimated to reach USD 12.2 billion by the end of 2037, exhibiting a CAGR of 11.3% during the forecast period, i.e., 2025-2037. North America industry is predicted to account for the largest share of 11.3% by 2037, owing to increased adoption of web-enabled solutions in the region.

Market Size statistics on the Clinical Trial Data Management Services industry in the US

According to the results of a survey on customer experience (CX) among businesses conducted in the United States in 2021, approximately half of the respondents declared to rely on systems programmed by data scientists for their CX data management strategy. Moreover, ** percent of the respondents declared to rely on data security.

Financial overview and grant giving statistics of Data Management Association Puget Sound

The Unified Data Management Solution (UDMS) market is experiencing significant growth as organizations increasingly recognize the necessity of streamlined data handling and integration. This comprehensive approach allows businesses to manage, analyze, and utilize their data across various platforms and repositories,

Table summarising UK and USA funding body, and Australian institution, mandates for data management plans. Includes supporting links and statistics.Data were collected through searching the internet, including internet archives, for evidence of date of implementation of broad DMP mandate.Data were collected in August 2017.

Data Governance Market is Segmented by Component (Software, Services), Deployment (Cloud, On-Premise), Organization Size (Large Enterprises, Small and Medium Enterprises), Business Function (IT and Operations, Legal and Compliance, and More) Application (Compliance Management, Risk Management, and More), End-User Industry (BFSI, IT and Telecom, and More), Geography. The Market Forecasts are Provided in Terms of Value (USD).

The global clinical data management systems market size was valued at USD 1,837.50 million in 2023 and it is expected to grow to USD 4,490.53 million by 2031 at a CAGR of 13.6%.

The Digital Data Management Systems (DDMS) market has emerged as a critical component in the modern business landscape, enabling organizations to efficiently handle, store, and process vast amounts of data. As the volume of data continues to grow exponentially, driven by factors such as digital transformation, incre

The global market size of Clinical Data Management is $XX million in 2018 with XX CAGR from 2014 to 2018, and it is expected to reach $XX million by the end of 2024 with a CAGR of XX% from 2019 to 2024.
Global Clinical Data Management Market Report 2019 - Market Size, Share, Price, Trend and Forecast is a professional and in-depth study on the current state of the global Clinical Data Management industry. The key insights of the report:
1.The report provides key statistics on the market status of the Clinical Data Management manufacturers and is a valuable source of guidance and direction for companies and individuals interested in the industry.
2.The report provides a basic overview of the industry including its definition, applications and manufacturing technology.
3.The report presents the company profile, product specifications, capacity, production value, and 2013-2018 market shares for key vendors.
4.The total market is further divided by company, by country, and by application/type for the competitive landscape analysis.
5.The report estimates 2019-2024 market development trends of Clinical Data Management industry.
6.Analysis of upstream raw materials, downstream demand, and current market dynamics is also carried out
7.The report makes some important proposals for a new project of Clinical Data Management Industry before evaluating its feasibility.
There are 4 key segments covered in this report: competitor segment, product type segment, end use/application segment and geography segment.
For competitor segment, the report includes global key players of Clinical Data Management as well as some small players.
The information for each competitor includes:
* Company Profile
* Main Business Information
* SWOT Analysis
* Sales, Revenue, Price and Gross Margin
* Market Share

For product type segment, this report listed main product type of Clinical Data Management market
* Product Type I
* Product Type II
* Product Type III

For end use/application segment, this report focuses on the status and outlook for key applications. End users sre also listed.
* Application I
* Application II
* Application III

For geography segment, regional supply, application-wise and type-wise demand, major players, price is presented from 2013 to 2023. This report covers following regions:
* North America
* South America
* Asia & Pacific
* Europe
* MEA (Middle East and Africa)
The key countries in each region are taken into consideration as well, such as United States, China, Japan, India, Korea, ASEAN, Germany, France, UK, Italy, Spain, CIS, and Brazil etc.

Reasons to Purchase this Report:
* Analyzing the outlook of the market with the recent trends and SWOT analysis
* Market dynamics scenario, along with growth opportunities of the market in the years to come
* Market segmentation analysis including qualitative and quantitative research incorporating the impact of economic and non-economic aspects
* Regional and country level analysis integrating the demand and supply forces that are influencing the growth of the market.
* Market value (USD Million) and volume (Units Million) data for each segment and sub-segment
* Competitive landscape involving the market share of major players, along with the new projects and strategies adopted by players in the past five years
* Comprehensive company profiles covering the product offerings, key financial information, recent developments, SWOT analysis, and strategies employed by the major market players
* 1-year analyst support, along with the data support in excel format.
We also can offer customized report to fulfill special requirements of our clients. Regional and Countries report can be provided as well.

Graph and download economic data for Producer Price Index by Commodity: Data Processing and Related Services: Data Management, Information Transformation and Other Services (WPU381103) from Dec 2008 to May 2025 about information technology, management, information, processed, services, commodities, PPI, inflation, price index, indexes, price, and USA.

This dataset is about books. It has 11 rows and is filtered where the book is Statistics for management and economics. It features 7 columns including author, publication date, language, and book publisher.

Statistics Software Market Outlook

The global statistics software market size is projected to grow from USD 10.5 billion in 2023 to USD 18.7 billion by 2032, exhibiting a CAGR of 6.5% over the forecast period. The growth of this market is driven by the increasing adoption of data-driven decision-making processes across various industries, the rising need for statistical modeling and analysis tools, and the growing emphasis on advanced analytics to gain competitive advantages. Additionally, the expanding use of artificial intelligence (AI) and machine learning (ML) technologies to enhance the capabilities of statistics software is contributing significantly to market growth.

One of the primary growth factors of the statistics software market is the increasing reliance on data analytics and business intelligence tools across different sectors. Organizations are leveraging statistical software to analyze large volumes of data generated through various digital channels, enabling them to make informed decisions and identify new business opportunities. This trend is particularly evident in the healthcare, finance, and retail sectors, where data-driven insights are crucial for improving operational efficiency, customer satisfaction, and overall performance.

Another key driver for the market is the proliferation of big data and the need for advanced data management solutions. With the exponential growth of data generated by various sources such as social media, IoT devices, and enterprise systems, there is a heightened demand for robust statistical software that can handle complex data sets and perform sophisticated analyses. This has led to increased investments in the development of innovative statistics software solutions that offer enhanced features and capabilities, such as real-time data processing, predictive analytics, and automated reporting.

The integration of AI and ML technologies into statistics software is also significantly boosting market growth. These technologies enable more accurate and efficient data analysis, allowing organizations to uncover hidden patterns and trends that were previously impossible to detect. AI-powered statistical tools can automate repetitive tasks, reduce human error, and provide deeper insights into data, thereby enhancing the overall decision-making process. As a result, there is a growing adoption of AI-driven statistics software across various industries, further propelling market expansion.

Regionally, North America is expected to maintain its dominance in the statistics software market, owing to the presence of numerous leading software providers, high adoption of advanced analytics solutions, and substantial investments in research and development. However, the Asia Pacific region is anticipated to witness the highest growth rate over the forecast period, driven by the rapid digital transformation of businesses, increasing awareness of data analytics benefits, and supportive government initiatives promoting technological advancements.

Component Analysis

The statistics software market is segmented by component into software and services. The software segment includes various types of statistical analysis tools, ranging from basic data visualization software to advanced predictive analytics platforms. This segment holds the largest market share due to the widespread adoption of software solutions that enable organizations to analyze and interpret data efficiently. The continuous development of innovative features, such as real-time analytics, data mining, and machine learning capabilities, is further driving the demand for statistics software.

In contrast, the services segment encompasses consulting, implementation, training, and support services provided by software vendors and third-party providers. These services are crucial for organizations to effectively utilize statistical software and maximize its benefits. The growing complexity of data and the need for specialized expertise in data analysis are driving the demand for professional services in the statistics software market. Moreover, as more businesses adopt advanced analytics solutions, the need for ongoing support and training services is expected to increase, contributing to the growth of the services segment.

The integration of cloud computing with statistics software is also influencing the component-wise growth of this market. Cloud-based solutions offer several advantages, such as scalability, flexibility, and cost-effectiveness, making them an attractive option for organizations of all sizes. As a result, there is a