Data Classification Market size was valued at USD 1664.66 Million in 2024 and is projected to reach USD 9486.25 Million by 2032, growing at a CAGR of 24.3% during the forecast period 2026-2032.

Global Data Classification Market Drivers

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

Increasing Data Volume: In order to maintain data security, compliance, and effective use, there is an increasing requirement to manage and classify the data produced by enterprises in an exponentially growing amount. Regulatory Compliance: Organizations must categorize their data based on the sensitivity levels required by strict data protection laws like the GDPR, CCPA, HIPAA, and others. Adoption of data classification solutions is driven by compliance requirements, which guarantee adherence to regulatory standards and prevent heavy penalties.

Data Security Concerns: Organizations are concentrating on strengthening their data security procedures due to the increase in cyber threats and data breaches. Classifying data makes it easier to find sensitive information and implement the right security measures to keep it safe from theft or unwanted access.

Growing Adoption of Cloud Services: As cloud computing services become more widely used, strong data classification techniques are required to guarantee data security and compliance, particularly when data is transferred between different cloud environments and storage locations. Increasing Awareness of Data Privacy: The need for solutions that allow for better management and protection of sensitive data through classification and encryption is being driven by heightened awareness of data privacy issues among consumers and enterprises. Combining Data Loss Prevention (DLP) Systems: Through the identification, monitoring, and prevention of sensitive information leakage or unlawful transfer, data categorization integrated with DLP systems improves data protection capabilities. Emergence of AI and Machine Learning Technologies: By incorporating these technologies into data categorization systems, data may be identified and classified more automatically and accurately, saving labor and increasing efficiency. Demand for Data Governance and Lifecycle Management: In order to maintain data quality, integrity, and compliance throughout its lifecycle, organizations are realizing more and more how important it is to have effective data governance and lifecycle management. A key component of putting into practice efficient data governance procedures is data classification.

Data Classification Market Outlook

The global data classification market size was valued at approximately USD 700 million in 2023, with a projected CAGR of 24% over the forecast period, leading to an estimated market size of USD 4.5 billion by 2032. The growth of this market is primarily driven by the increasing need for businesses to manage and protect sensitive information, amid a rapidly expanding volume of data and ever-evolving regulatory requirements. The advent of new data protection regulations, such as the General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA), has compelled organizations across industries to invest in robust data classification solutions. These solutions enable businesses to effectively categorize, label, and protect their data, ensuring compliance with stringent data security standards.

One significant growth factor of the data classification market is the digital transformation wave sweeping across industries. As organizations increasingly adopt digital technologies to drive operational efficiencies and enhance customer experiences, the amount of data generated and processed has grown exponentially. This surge in data volume necessitates efficient data management and protection solutions, fuelling demand for data classification systems. Moreover, as businesses strive to derive actionable insights from their data assets, data classification becomes crucial in organizing and prioritizing information for analysis, thereby enhancing decision-making processes and business outcomes.

Cybersecurity threats and data breaches have become pervasive, posing a significant risk to organizations' data assets. Consequently, there is an increasing focus on data security, which is driving the demand for data classification solutions. These solutions offer organizations the ability to identify and classify sensitive information, thereby enabling the implementation of effective security controls. In addition, the rise of remote working and cloud computing has broadened the attack surface, making it imperative for organizations to have robust data classification strategies in place to safeguard their data in a decentralized environment. The need for enhanced data security measures is expected to continue driving the growth of the data classification market throughout the forecast period.

The proliferation of artificial intelligence (AI) and machine learning (ML) technologies presents another compelling growth factor for the data classification market. AI and ML technologies augment traditional data classification methods by automating the process and offering improved accuracy and efficiency in data categorization. These advanced technologies enable organizations to handle large volumes of data more effectively and make data-driven decisions with greater precision. As organizations increasingly recognize the potential of AI and ML in enhancing data classification capabilities, the adoption of AI-powered data classification solutions is anticipated to witness significant growth, further propelling the market.

Regionally, North America is anticipated to hold the largest share of the data classification market, driven by the early adoption of advanced technologies and stringent data protection regulations. The presence of major technology players and a highly developed IT infrastructure further support the growth of the market in the region. Meanwhile, the Asia Pacific region is expected to exhibit the highest growth rate during the forecast period, attributed to the increasing digitalization initiatives, growing awareness about data security, and a burgeoning number of small and medium enterprises (SMEs) seeking cost-effective data classification solutions. Europe is also witnessing a notable demand for data classification systems, propelled by compliance requirements and a growing emphasis on data privacy.

Component Analysis

The data classification market by component is segmented into software and services. The software segment is the largest and fastest-growing component of the market, owing to the increasing demand for sophisticated data classification tools that offer advanced features such as automation, real-time data analytics, and integration with other enterprise applications. Organizations are increasingly investing in software solutions to enhance their data management capabilities and ensure compliance with data protection regulations. The software segment's growth is further supported by the rising adoption of cloud-based solutions, which offer scalability, flexibility, and cost-effectiveness, making them an attractive option for businesses of al

Access the Global Industry Classification Standard (GICS) system through LSEG, covering over 58,000 trading securities across 125 countries.

According to Cognitive Market Research, the global Data Classification market size will be USD 1842.2 million in 2024. It will expand at a compound annual growth rate (CAGR) of 25.20% from 2024 to 2031.

North America held the major market share for more than 40% of the global revenue with a market size of USD 736.88 million in 2024 and will grow at a compound annual growth rate (CAGR) of 23.4% from 2024 to 2031.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 552.66 million.
Asia Pacific held a market share of around 23% of the global revenue with a market size of USD 423.71 million in 2024 and will grow at a compound annual growth rate (CAGR) of 27.2% from 2024 to 2031.
Latin America had a market share of more than 5% of the global revenue with a market size of USD 92.11 million in 2024 and will grow at a compound annual growth rate (CAGR) of 24.6% from 2024 to 2031.
Middle East and Africa had a market share of around 2% of the global revenue and was estimated at a market size of USD 36.84 million in 2024 and will grow at a compound annual growth rate (CAGR) of 24.9% from 2024 to 2031.
The Solutions is the fastest growing segment of the Data Classification industry

Market Dynamics of Data Classification Market

Key Drivers for Data Classification Market

Increasing Data Privacy and Security Regulations to Boost Market Growth

The growing emphasis on statistics privateness and protection rules is using boom inside the records type marketplace. As businesses face stricter compliance requirements and heightened scrutiny over facts managing practices, there's an increasing need for sturdy data category solutions. These answers assist in categorizing and managing records based on their sensitivity and compliance desires, thereby mitigating risks related to records breaches and non-compliance consequences. Enhanced rules, which include GDPR and CCPA, are prompting corporations to spend money on superior records-type technology to shield touchy statistics and make certain adherence to prison standards, for that reason, fueling marketplace enlargement. For instance, In order to assist Indian businesses in consolidating all facets of risk under one roof via integrated risk management technology, Rotiviti India partnered with Riskconnect.

Expansion of the Data Breaches and Cyberattacks to Drive Market Growth

The surge in statistics breaches and cyberattacks is significantly boosting the facts category market. As cyber threats become more sophisticated and common, businesses are more and more adopting information classification answers to protect sensitive records. These technologies assist in figuring out, categorizing, and securing facts in line with their sensitivity, thereby minimizing the impact of ability breaches. With cyberattacks concentrated on valuable information and regulatory pressures mounting, agencies are investing in information-type systems to decorate their safety posture and ensure compliance. This developing demand for sturdy facts safety measures is riding the growth of the data category market.

Restraint Factor for the Data Classification Market

Complexity and Cost, will Limit Market Growth

The complexity and cost related to records classification are hindering the market boom. Implementing complete information classification solutions often calls for sizeable investment in advanced technology and professional personnel. The complexity of integrating those systems with present IT infrastructure and ensuring correct classification throughout various records assets provides to the mission. Additionally, ongoing maintenance and updates to hold pace with evolving threats and regulatory adjustments contribute to excessive prices. These factors can be especially burdensome for small and medium-sized organizations, limiting their capability to undertake powerful records class answers and thereby restraining usual marketplace enlargement.

Impact of Covid-19 on the Data Classification Market

The COVID-19 pandemic has had a combined effect on the statistics classification market. On the one hand, the improved shift too far-off work and expanded reliance on virtual systems heightened the want for robust statistics classification answers to stable, sensitive records and make sure compliance with data protection policies. On the other hand, economic uncertainties and price range constraints in the course of t...

These files contain classified topo/bathy lidar data. Data are classified as 1 (valid non-ground topographic data), 2 (valid ground topographic data), 23 (submerged aquatic vegetation), and 29 (valid bathymetric data). Classes 1 and 2 are defined in accordance with the American Society for Photogrammetry and Remote Sensing (ASPRS) classification standards. These data were collected by the Coast...

Supervised classification utilized training texels of 30 x 30 to 90 x 90 pixels cut from GeoTiff orthotiles centered on the coordinates of the grab sample stations. Each texel was assigned to a cluster training set based on that sample’s classification in the original (latent) cluster analysis calculated on similarity of sediment characteristics. However, none of the potential 2470 combinations of backscatter signal characters and their treatments were able to discriminate significantly among these 5 classes, meaning that variation among samples of at least 2 classes overlapped considerably. Recombination into 4 classes (combining Classes 3 and 4) yielded significant discrimination. Mapping of the results showed that one of these classes was likely to be legitimate when applied to the bayside, but additionally was duplicated as an artifact of edge between orthotiles on the oceanside because of fading at the swath margins. This means that backscatter was characteristic of the larger habitat distinctions shown in the latent dendrogram with confidence, and of lesser branches with less confidence. Therefore, the entire oceanside was characterized as one habitat, and classification of the bayside was attempted again in isolation. Recombination into 3 classes (“mud”, “sand”, “gravelly sand”) was able to resolve 3 classes significantly (score = 0.33548) using input factors Contrast, Gray Mean, and Directionality with 30 x 30 pixel (15 x 15 m) texels. Despite good separation in the training texels, with some slight overlap at the 5% confidence ellipsoid for mud and gravel, most areas known to be muddy were classified as being gravelly sand in the resulting classification map. This is likely a function of reflective shell hash in acoustically dark mud having similar contrast to reflective gravel with acoustically dark shadows created by high relief. A test of natural separation (Davies-Bouldin Index) indicated four modes using these characters, so the same factors were used in an unsupervised classification allowing four latent classes. The four latent classes mapped very similar to the previous supervised classification but broke up the latent analog to the “gravelly sand” class. Class error was low at 0.1110. The newly resolved class was clearly mud with shell, based on video ground truthing. This class was combined with the mud class in compiling the final habitat classification map.

Benthic habitat maps were developed for the CACO study areas following the top-down mapping approach, for which habitat map units are geologically defined based on the presumption that geologic environments or features contain distinct biological assemblages. The resulting habitats are classified according to the CMECS framework and are referred to as “biotopes.” The term “biotope” is specific in that it integrates biotic-abiotic characteristics to offer more ecologically meaningful information. In this study, biotopes reflect the relationship between macrofaunal communities and geological features of their associated environments within the defined map units. The resulting biotopes are considered preliminary because the relationships identified have not been repeatedly demonstrated over time.

The North American Industry Classification System (NAICS) is an industry classification system developed by the statistical agencies of Canada, Mexico and the United States. Created against the background of the North American Free Trade Agreement, it is designed to provide common definitions of the industrial structure of the three countries and a common statistical framework to facilitate the analysis of the three economies. NAICS is based on supply side or production oriented principles, to ensure that industrial data, classified to NAICS, is suitable for the analysis of production related issues such as industrial performance.

This metadata layer shows the footprint of areas included in vegetation sampling projects without associated mapping projects. These projects are collecting surveys that can be used to create regional classifications consistent with the Manual California of Vegetation (online) and are consistent with National Vegetation Classification Standards. It provides basic information about each project. It is current as of July 2023. A second dataset, Vegetation (MCV / NVCS) Mapping Projects - California ds515, shows information about fine-scaled mapping projects that use the MCV / NVCS as the basis of classification.

Benthic habitat maps were developed for the Otis Pike and Sunken Forest study areas following the top-down mapping approach, for which habitat map units are geologically defined based on the presumption that geologic environments or features contain distinct biological assemblages. The resulting habitats are classified according to the CMECS framework and are referred to as “biotopes.” The term “biotope” is specific in that it integrates biotic-abiotic characteristics to offer more ecologically meaningful information. In this study, biotopes reflect the relationship between macrofaunal communities and geological features of their associated environments within the defined map units. The resulting biotopes are considered preliminary because the relationships identified have not been repeatedly demonstrated over time, as this study represents the first of its kind within FIIS.

Regulatory Document - Electronic Game Console Classification Standards

The Coastal and Marine Ecological Classification Standard (CMECS) Catalog is the authoritative collection of ecological units (terms + definitions) and unit relationships (the CMECS classification framework).

The CMECS Catalog is the complete representation of the CMECS classification. It contains all units that are or have been members of the CMECS classification throughout its lifecycle, as well as various annotations that provide metadata for each unit that enable Findability, Accessibility, Interoperability, and Reuse (FAIR, https://www.go-fair.org/fair-principles/). The CMECS Catalog (cmecs.owl) file is stored and managed in a Git repository; authoritative versions are publicly released via the NOAA-OCM/cmecs GitHub as changes are made. Version releases also include the CMECS Catalog in CSV and XLSX formats. A browsable text output of the CMECS Catalog ecological units and implementation guidance, the CMECS Thesaurus, is also available in PDF and MD formats.

This release includes changes to the Substrate Component Unit Codes and fixes to Biotic Component typographical errors. Details are available on the CMECS GitHub v1.1.1 Release Page.

Questions? Please contact the CMECS Implementation Group at ocm.cmecs-ig@noaa.gov

For more information about the CMECS Catalog, see the https://github.com/NOAA-OCM/cmecs/wiki.

For more information about CMECS, including technical guidance and classification examples, visit the NOAA Integrated Ocean and Coastal Mapping (IOCM) team's CMECS webpage.

CMECS follows a Dynamic Standard Process to review and adopt changes that are proposed by the CMECS user community when necessary. More information about CMECS maintenance can be found on the NOAA National Centers for Environmental Information (NCEI) CMECS webpage under the Vocabulary Maintenance section, along with instructions for proposing revisions to CMECS and a form for submitting proposals.

LBCS or Land Base classification standards describes current land use practices in Athens-Clarke County

STUDY PURPOSE: The International Standards for Neurological Classification of Spinal Cord Injury (ISNCSCI) provides a widely accepted system for determining level and severity of a human spinal cord injury (SCI). The ISNCSCI is widely used for clinical purposes (communication of level and severity, monitor changes over time, establish rehabilitation goals and therapy programs and to predict neurological recovery on a group level) and in research (characterization, outcome measures as well as inclusion/exclusion criteria and (sub-)grouping criteria). Its successful application demands accuracy in both the examination and classification, of which the latter is the focus of this work. ISNCSCI classification involves precise rules and nuances, and inherent challenges have been described. The heterogeneity of SCI adds further complexity. A comprehensive dataset of representative ISNCSCI cases with annotated classifications is not yet available within the field. Therefore, the purpose of this dataset is to provide such a workbook to illustrate important classification rules, definitions, and nuances for a wide range of spinal cord injuries. DATA COLLECTED: Twenty-six hypothetical ISNCSCI cases were created by the authors to illustrate important classification rules, definitions, and nuances. Each case contains all 134 examined scores (2 body sides times 28 dermatomes light touch scores; 2 times 28 pin prick scores, 2 times 10 myotomes motor scores as well as voluntary anal contraction and deep anal pressure sensation) as well as all 11 classifications components: right and left sensory levels, right and level motor levels, neurological level of injury, completeness, American Spinal Injury Association (ASIA) Impairment Scale, right/left sensory zone of partial preservation, right/left motor zone of partial preservation. Each case additionally contains detailed explanations of the process for classifying each variable. The cases are documented and classified according to the eighth edition of the ISNCSCI revised in 2019 (https://doi.org/10.46292/sci2702-1).

The cases cover a wide range of topics such as: - New ISNCSCI concepts introduced with the 2019 revision like the -- Non-SCI taxonomy for documentation of non-SCI related conditions superimposed to the SCI that may influence the examination of motor/sensory scores and impact the classification components (e.g., amputations, peripheral nerve lesions, pain, tendon transfers) -- Broadened ZPP applicability not only for sensorimotor complete, but also for a subset of incomplete lesions - Inherent classification challenges -- Motor incompleteness due to sparing of motor function more than three segments below the motor level -- Use of non-key muscle functions in the determination of motor incompleteness -- Motor levels in the high cervical and thoracic regions, where the motor level follows the sensory level -- The correct classification of levels, completeness and zones of partial preservation for ASIA Impairment Scale E classifications DATA USAGE NOTES:

description: The Standard Occupational Classification (SOC) system is used by Federal statistical agencies to classify workers into occupational categories for the purpose of collecting, calculating, or disseminating data. All workers are classified into one of over 820 occupations according to their occupational definition.; abstract: The Standard Occupational Classification (SOC) system is used by Federal statistical agencies to classify workers into occupational categories for the purpose of collecting, calculating, or disseminating data. All workers are classified into one of over 820 occupations according to their occupational definition.

Satellite-derived data for sea surface temperature, salinity, chlorophyll; euphotic depth; and modeled bottom to surface temperature differences were evaluated to assess the utility of these products as proxies for in situ measurements. The data were used to classify surface waters in three regions of the Gulf of Mexico using subcomponents and modifiers from the Coastal and Marine Ecological Classification Standard (CMECS) Water Column Component (WC) to determine if CMECS categories could be effectively used to categorize in situ data into meaningful management units. The Naval Research Laboratory at the Stennis Space Center (NRL/SSC) processed MODIS-Aqua satellite imagery covering the Gulf of Mexico from January 2005 to December 2009. Daily, level-1B image files from the NASA LAADS Web were processed through the NRL/SSC Automated Processing System (APS). Sea surface temperature and salinity were classified into CMECS WC temperature and salinity subcomponent categories, respectively. Three modifiers from the WC were also used for the pelagic classification: water column stability, productivity, and photic quality. Modeled bottom to surface temperature differences were used to assign classification for water column stability, surface chlorophyll was used to determine productivity, and euphotic depth was used to indicate the photic quality. Maps showing the CMECS Water Column Component classes for chlorophyll concentration, euphotic depth, sea surface salinity, sea surface temperature (HDF4), and bottom-to-surface temperatures (netCDF) were produced from the APS output images.

[Metadata] This dataset contains those marine Water Quality Standards Classifications surrounding the main Hawaiian Islands as specified in DOH Administrative Rules, specifically Hawaii Administrative Rules Title 11, Department of Health, Chapter 54 Water Quality Standards (see p. 19). June 2024: Hawaii Statewide GIS Program staff removed extraneous fields that had been added as part of a 2016 GIS database conversion and were no longer needed. For additional information, please refer to metadata at https://files.hawaii.gov/dbedt/op/gis/data/water_qual_class.pdf or contact Hawaii Statewide GIS Program, Office of Planning, State of Hawaii; PO Box 2359, Honolulu, Hi. 96804; (808) 587-2846; email: gis@hawaii.gov; Website: https://planning.hawaii.gov/gis.

The U.S. Bureau of Land Management (BLM) contracted Aerial Information Systems, Inc. (AIS) to continue vegetation classification development and fine-scale vegetation mapping of 1,016,668 acres over four subareas within Inyo, Kern, and Imperial counties of the Desert Renewable Energy Conservation Plan (DRECP) region. The four subareas are designated as Salton Sea South (224,763 acres), Jawbone South (204,133 acres), Owens Valley (392,906 acres), and Picacho (194,866 acres). Work performed is based on the classification and mapping standards as outlined in the Survey of California Vegetation, Classification, and Mapping Standards developed by the California Department of Fish and Wildlifes (CDFW) Vegetation, Classification, and Mapping Program (VegCAMP) (VegCAMP, 2020). California Native Plant Society (CNPS), as a subcontractor to AIS, conducted any classification development work needed for this project and conducted the accuracy assessment surveys. The subareas included in this map are Jawbone North (151,986 acres), Jawbone South (204,133 acres), and Owens Valley (392,906 acres). BIOS ds735 contains the other areas.The previous mapping for the DRECP region was conducted in two phases from 2011 to 2016 for the California Energy Commission. The maps were primarily produced to support the DRECP by helping planners more accurately identify high quality habitat and rare communities as they consider renewable energy sources and conservation opportunities. In 2011-2012 AIS and VegCAMP created a fine-scale vegetation map covering approximately six million acres of portions of the Mojave and Sonoran Deserts in southern California (Menke et al., 2013). In addition, mapping of 95,981 acres within Rice and Vidal Valleys in the Colorado Desert portion of the Sonoran Desert was completed by AIS in 2013-2014 as an extension to the original project. Subsequently, between 2014 and 2016, AIS was tasked to create a fine-scale vegetation map of 2,195,415 acres of desert in Inyo, San Bernardino, Riverside, and Imperial counties in southern California. Areas mapped include the eastern and central portions of the Mojave Desert as well as the Lower Colorado Valley (also referred to as the Colorado Desert), and the Arizona Upland subdivisions of the Sonoran Desert (Menke et al., 2016). The vegetation classification follows Federal Geographic Data Committee (FGDC) and National Vegetation Classification Standards (NVCS). The classification is based on new and previous survey and classification work. The map was produced applying heads-up digitizing techniques using a base of 2016 or 2018 one-meter National Agricultural Imagery Program (NAIP) imagery (true-color and color infrared), in conjunction with ancillary data and imagery sources. Map polygons are assessed for Vegetation Type, Percent Cover, Exotics, Development Disturbance, and other attributes. The minimum mapping unit (MMU) is 10 acres; exceptions are made for wetlands and certain wash types (which were mapped to a 1 or 5 acre MMU, depending on type) and areas characterized as Land Use polygons (which were mapped to a 2.5 acre MMU). In the progression to non-desert areas the MMU transitioned to 1 acre for upland types and 1/4 acre for special types. Field reconnaissance and accuracy assessment enhanced map quality. There were a total of 126 mapping classes. The overall accuracy assessment ratings for the final vegetation map were 86.23 percent for Users Accuracy, and 87.9 for Producers Accuracy.Accuracy assessment ratings for the subareas included in this map are as follows:Owens Valley: Users Accuracy 83.9, Producers Accuracy 85.4Jawbone North: Users Accuracy 86.7, Producers Accuracy 85.5Jawbone South: Users Accuracy 82.8, Producers Accuracy 84.3For detailed information please refer to the following reports: Reyes, E., J. Evens, A. Glass, Sikes, K, Keeler-Wolf, T., D. Johnson, S. Winitsky, J. Menke and A. Hepburn. 2020 CALIFORNIA VEGETATION MAP IN SUPPORT OF THE DESERT RENEWABLE ENERGY CONSERVATION PLAN . U.S. Bureau of Land Management; 12/2020.Menke, J., E. Reyes, A. Hepburn, D. Johnson and J. Reyes. California Vegetation Map in Support of the Desert Renewable Energy Conservation Plan (2014-2016 Additions). Aerial Information Systems; 5/2016.Menke, J., E. Reyes, A. Glass, D. Johnson and J. Reyes. 2013 California Vegetation Map in Support of the Desert Renewable Energy Conservation Plan. California Dept. of Fish and Wildlife, California Energy Commission; 4/2013.

See full Data Guide here.Ground Water Classifications Polygon:

Ground Water Quality Classifications is a polygon feature-based layer compiled at 1:24,000 scale that includes water quality classification information for groundwaters for all areas of the State of Connecticut. Ground Waters means waters flowing through earth materials beneath the ground surface and the Ground Water Quality Classifications is a designation of the use of the ground waters. The Ground Water Quality Classifications is based primarily on the Adopted Water Quality Classifications Map sheets with information collected and compiled from 1986 to 1997 by major drainage basin. The maps were hand-drawn at 1:50,000-scale in ink on Mylar which had been underprinted with a USGS topographic map base. The digital layer includes ground water water quality classifications. It does not include water quality classifications for ground waters below surface waterbodies. Surface Water Quality Classifications are defined separately in a set of data layers comprised of line and polygon features. The Ground Water Quality Classifications and the Surface Water Quality Classifications are usually presented together as a depiction of water quality classifications in Connecticut. The Ground Water Quality Classes are GA, GAA, GAAs, GB and GC. Classes GAA and GA designate areas of existing or potential drinking water. All ground waters not otherwise classified are considered as Class GA. Class GAAs is for ground water that is tributary to a public water supply reservoir. Class GB is used where ground water is not suitable for drinking water. Class GC is used for assimilation of permitted discharges. Modified classes GA-Impaired, GAA-Impaired, GAA-Well-Impaired, GAA-Well and GA-NY are found in the data layer to categorize special cases of GA or GAA that may not be meeting the goal (impaired), surround public water supply wells (Well) or contribute to a public water supply watershed for another state (NY). There are three elements that make up the Water Quality Standards which is an important element in Connecticut's clean water program. The first of these is the Standards themselves. The Standards set an overall policy for management of water quality in accordance with the directive of Section 22a-426 of the Connecticut General Statutes. In simple terms the policies can be summarized by saying that the Department of Energy and Environmental Protection shall: Protect surface and ground waters from degradation, Segregate waters used for drinking from those that play a role in waste assimilation, Restore surface waters that have been used for waste assimilation to conditions suitable for fishing and swimming, Restore degraded ground water to protect existing and designated uses, Provide a framework for establishing priorities for pollution abatement and State funding for clean up, Adopt standards that promote the State's economy in harmony with the environment. The second element is the Criteria, the descriptive and numerical standards that describe the allowable parameters and goals for the various water quality classifications. The final element is the Classification Maps that show the Class assigned to each surface and groundwater resource throughout the State. These maps also show the goals for the water resources, and in that manner provide a blueprint and set of priorities for Connecticut's efforts to restore water quality. Although federal law requires adoption of Water Quality Standards for surface waters, Water Quality Standards for ground waters are not subject to federal review and approval. Connecticut's Standards recognize that surface and ground waters are interrelated and address the issue of competing use of ground waters for drinking and for waste water assimilation. These Standards specifically identify ground water quality goals, designated uses and those measures necessary for protection of public and private drinking water supplies; the principal use of Connecticut ground waters. These three elements comprise the Water Quality Standards and are adopted using the public participation procedures contained in Section 22a-426 of the Connecticut General Statutes. The Standards, Criteria and Maps are reviewed and revised roughly every three years. Any change is considered a revision requiring public participation. The public participation process consists of public meetings held at various locations around the State, notification of all chief elected officials, notice in the Connecticut Law Journal and a public hearing. The Classification Maps are the subject of separate public hearings which are held for the adoption of the map covering each major drainage basin in the State. The Water Quality Standards and Criteria documents are available on the DEEP website, www.ct.gov/deep. The Ground and Surface Water Quality Classifications do not represent conditions at any one particular point in time. During the conversion from a manually maintained to a digitally maintained statewide data layer the Housatonic River and Southwest Coastal Basins information was updated. The publication date of the digital data reflects the official adoption date of the most recent Water Quality Classifications. Within the data layer the adoption dates are: Housatonic and Southwest Basins - March 1999, Connecticut and South Central Basins - February 1993, Thames and Southeast Basins - December 1986. This data is updated.