Data Management Platform (DMP) Software Market Outlook

The Data Management Platform (DMP) Software market size was valued at approximately USD 3.5 billion in 2023 and is projected to reach around USD 12.8 billion by 2032, growing at a compound annual growth rate (CAGR) of 15.6% during the forecast period. The primary growth factors driving this market include the increasing need for data-driven decision-making, the proliferation of digital marketing channels, and the rising importance of customer-centric business strategies.

One of the key growth factors of the DMP software market is the exponential increase in data generation from various digital sources. With the advent of social media, e-commerce, and various digital platforms, businesses are accumulating vast amounts of data that need to be effectively managed and analyzed. DMP software enables organizations to consolidate, manage, and analyze data from multiple sources, providing valuable insights that drive strategic decisions and personalized customer experiences. This capability is crucial in a competitive business environment where data-driven decisions can significantly influence outcomes.

Another significant driver for the market is the growing adoption of digital marketing strategies across various industry verticals. Businesses are increasingly leveraging digital marketing channels such as social media, email marketing, and online advertising to reach a broader audience. DMP software plays a crucial role in this context by helping businesses to segment their audience, target specific customer groups, and measure the effectiveness of their marketing campaigns. This software allows for more efficient allocation of marketing resources, leading to improved return on investment (ROI) and enhanced customer engagement.

The rising importance of customer-centric business strategies is also fueling the demand for DMP software. Organizations are increasingly focusing on understanding their customers' preferences, behaviors, and needs to deliver personalized experiences. DMP software enables businesses to collect and analyze customer data from various touchpoints, providing a 360-degree view of their customers. This comprehensive understanding allows companies to tailor their products, services, and marketing efforts to meet the specific needs of their customers, thereby enhancing customer satisfaction and loyalty.

Regionally, North America is anticipated to hold the largest market share during the forecast period due to the presence of major market players and early adoption of advanced technologies. Europe is expected to follow closely, driven by stringent data protection regulations that necessitate robust data management solutions. The Asia Pacific region is projected to witness the highest growth rate, attributed to the rapid digital transformation happening across emerging economies such as China and India. Latin America and the Middle East & Africa regions are also expected to contribute significantly to the market growth, supported by increasing investments in digital infrastructure and growing awareness about the benefits of data management platforms.

In the realm of analytics, Data Management Solutions for Analytics play a pivotal role in transforming raw data into actionable insights. These solutions are designed to handle the vast volumes of data generated by businesses, ensuring that data is not only stored efficiently but also easily accessible for analysis. By integrating various data sources, these solutions provide a comprehensive view that aids in strategic decision-making. As organizations strive to remain competitive, the ability to quickly analyze and act on data insights becomes crucial. Data Management Solutions for Analytics empower businesses to harness the full potential of their data, driving innovation and enhancing operational efficiency.

Component Analysis

The Data Management Platform (DMP) Software market can be broadly segmented into Software and Services. The software segment encompasses various types of DMP software solutions that help organizations collect, manage, and analyze data. These software solutions are designed to integrate with multiple data sources and provide a unified platform for data management. The increasing complexity of data and the need for real-time data analytics are driving the demand for advanced DMP software solutions. Companies are continuously innovating to develop software that can handle large volu

This resource contains 1 pdf document dated December 13th 2018. This resource will be a part of the collection "CUAHSI Legacy documents". The abstract is as follows:

Current practices to identify, organize, analyze, and serve data to water resources systems models are typically model and dataset-specific. Data are stored in different formats, described with different vocabularies, and require manual, model-specific, and time-intensive manipulations to find, organize, compare, and then serve to models. This paper presents the Water Management Data Model (WaMDaM) implemented in a relational database. WaMDaM uses contextual metadata, controlled vocabularies, and supporting software tools to organize and store water management data from multiple sources and models and allow users to more easily interact with its database. Five use cases use thirteen datasets and models focused in the Bear River Watershed, United States to show how a user can identify, compare, and choose from multiple types of data, networks, and scenario elements then serve data to models. The database design is flexible and scalable to accommodate new datasets, models, and associated components, attributes, scenarios, and metadata.

Market Analysis for Data Connector Software The global data connector software market is estimated to reach XXX million in 2025, with a CAGR of XX% throughout the forecast period 2025-2033. Key market drivers include the increasing adoption of cloud-based data integration and the need for businesses to access data from multiple sources. Furthermore, the rising demand for data analytics and machine learning is driving the growth of the data connector software market. The market is segmented based on application (large enterprises, SMEs), deployment type (on-premises, cloud-based), and region. North America is the largest market for data connector software, followed by Europe and Asia Pacific. Key industry players include Datapine, Open Automation Software, Adverity, Oracle, Progress, and Persistent Systems.

Area exposed to one or more hazards represented on the hazard map used for risk analysis of the RPP. The hazard map is the result of the study of hazards, the objective of which is to assess the intensity of each hazard at any point in the study area. The evaluation method is specific to each hazard type. It leads to the delimitation of a set of areas on the study perimeter constituting a zoning graduated according to the level of the hazard. The allocation of a hazard level at a given point in the territory takes into account the probability of occurrence of the dangerous phenomenon and its degree of intensity. For PPRTs the hazard levels are determined effect by effect on maps by type of effect and overall on an aggregated level on a synthesis map. All hazard areas shown on the hazard map are included. Areas protected by protective structures must be represented (possibly in a specific way) as they are always considered subject to hazard (case of breakage or inadequacy of the structure). Hazard zones can be described as developed data to the extent that they result from a synthesis using multiple sources of calculated, modelled or observed hazard data. These source data are not concerned by this class of objects but by another standard dealing with the knowledge of hazards. Some areas within the study area are considered “no or insignificant hazard zones”. These are the areas where the hazard has been studied and is nil. These areas are not included in the object class and do not have to be represented as hazard zones.

The pace, with which government agencies, researchers, industry, and the public need to react to national and international challenges of economic, environmental, and social natures, is constantly changing and rapidly increasing. Responses to the global COVID-19 pandemic event, the 2020 Australian bushfire and 2021 flood crisis situations are recent examples of these requirements. Decisions are no longer made on information or data coming from a single source or discipline or a solitary aspect of life: the issues of today are too complex. Solving complex issues requires seamless integration of data across multiple domains and understanding and consideration of potential impacts on businesses, the economy, and the environment. Modern technologies, easy access to information on the web, abundance of openly available data shifts is not enough to overcome previous limitations of dealing with data and information. Data and software have to be Findable, Accessible, Interoperable and Reusable (FAIR), processes have to be transparent, verifiable and trusted. The approaches toward data integration, analysis, evaluation, and access require rethinking to: - Support building flexible re-usable and re-purposeful data and information solutions serving multiple domains and communities. - Enable timely and effective delivery of complex solutions to enable effective decision and policy making. The unifying factor for these events is location: everything is happening somewhere at some time. Inconsistent representation of location (e.g. coordinates, statistical aggregations, and descriptions) and the use of multiple techniques to represent the same data creates difficulty in spatially integrating multiple data streams often from independent sources and providers. To use location for integration, location information needs to be embedded within the datasets and metadata, describing those datasets, so those datasets and metadata would become ‘spatially enabled’.

Problem Statement

👉 Download the case studies here

A global manufacturing company faced challenges in predicting product demand across multiple regions. Inefficient demand forecasting led to frequent stockouts, excessive inventory, and increased operational costs. The lack of accurate forecasts strained the supply chain, disrupting production schedules and delivery timelines. The company required a robust system to streamline operations by accurately predicting demand trends.

Challenge

Managing a complex supply chain with diverse products and fluctuating demand involved several challenges:

Handling large volumes of historical sales and production data from multiple sources . Accounting for seasonal variations, market trends, and external factors like economic shifts and weather.

Reducing lead times while minimizing excess inventory and ensuring product availability.

Solution Provided

An AI-driven demand forecasting system was developed, utilizing time series forecasting models and advanced analytics platforms to predict product demand accurately. The solution was designed to:

Analyze historical data and external variables to identify demand patterns.

Provide region-specific forecasts for optimized inventory management and production planning.

Enable real-time decision-making with dynamic updates to forecasts.

Development Steps

Data Collection

Collected historical sales, production, and market data from various sources, including ERP systems and external factors like weather reports and market indices.

Preprocessing

Cleaned and structured data, removed anomalies, and normalized datasets to ensure consistency and reliability for modeling.

Model Training

Developed time series forecasting models, including ARIMA and LSTM neural networks, to capture long-term trends and short-term fluctuations. Enhanced model performance through feature engineering and cross-validation.

Validation

Tested the forecasting models on unseen data to evaluate accuracy, reliability, and adaptability across different regions and product categories.

Deployment

Integrated the forecasting system into the company’s existing analytics platform, providing real-time dashboards for supply chain managers and stakeholders.

Continuous Improvement

Implemented a feedback mechanism to refine models with new data and evolving market conditions.

Results

Improved Forecasting Accuracy

Achieved a 25% increase in forecasting accuracy, enabling more precise inventory and production planning.

Reduced Lead Times

Streamlined supply chain operations, reducing lead times and improving delivery schedules.

Optimized Supply Chain Efficiency

Minimized excess inventory while ensuring product availability, leading to cost savings and improved operational efficiency.

Enhanced Decision-Making

Real-time insights empowered supply chain managers to make proactive, data-driven decisions.

Increased Customer Satisfaction

Consistently meeting demand improved customer satisfaction and strengthened market competitiveness.

Data Warehousing Solution Market Outlook

The global data warehousing solution market size was valued at approximately USD 22.1 billion in 2023 and is projected to reach around USD 51.2 billion by 2032, exhibiting a compound annual growth rate (CAGR) of 9.7%. This impressive growth is driven by the increasing adoption of cloud-based solutions, the explosion of big data, and the need for advanced analytics in various sectors.

One of the primary growth factors for this market is the increasing volume of data generated by organizations across different industry verticals. As businesses continue to embrace digital transformation, the need to store, manage, and analyze vast amounts of data becomes critical. Data warehousing solutions help organizations consolidate data from multiple sources, making it easier to retrieve, analyze, and generate actionable insights. This capability is becoming increasingly important as companies strive to gain a competitive edge through data-driven decision-making.

Another significant growth driver is the proliferation of cloud computing. Cloud-based data warehousing solutions offer several advantages over traditional on-premises systems, including scalability, cost-efficiency, and ease of deployment. Organizations are increasingly opting for cloud solutions to reduce infrastructure costs and improve accessibility. The growing adoption of hybrid cloud environments, which combine on-premises and cloud-based solutions, is further propelling the market's growth. Additionally, advancements in artificial intelligence and machine learning are enhancing the capabilities of data warehousing solutions, making them more efficient and effective.

Furthermore, regulatory requirements and compliance mandates are pushing organizations to invest in robust data warehousing solutions. Industries such as BFSI, healthcare, and government are subject to strict data governance and security regulations. Data warehousing solutions enable these organizations to maintain data integrity, ensure compliance, and protect sensitive information. The growing focus on data privacy and security is expected to drive the demand for advanced data warehousing solutions in the coming years.

In this evolving landscape, the role of a Data Lake System is becoming increasingly prominent. Unlike traditional data warehousing solutions, a Data Lake System offers a more flexible and scalable approach to data storage and management. It allows organizations to store vast amounts of raw data in its native format, which can be structured, semi-structured, or unstructured. This capability is particularly beneficial for businesses dealing with diverse data sources and formats, as it eliminates the need for upfront schema definitions and enables more agile data processing and analysis. As companies strive to harness the power of big data, the integration of Data Lake Systems with data warehousing solutions is emerging as a strategic priority, offering a comprehensive platform for data-driven innovation.

The regional outlook for the data warehousing solution market indicates strong growth across various geographies. North America currently holds the largest market share, driven by the presence of major technology companies and the rapid adoption of advanced analytics. The Asia Pacific region is expected to witness the highest growth rate during the forecast period, fueled by the increasing digitization of businesses, government initiatives to promote digital transformation, and the expanding IT infrastructure. Europe is also anticipated to experience significant growth due to the increasing focus on data privacy regulations and the rising demand for cloud-based solutions.

Component Analysis

The data warehousing solution market is segmented by components into software, hardware, and services. The software segment is the largest and fastest-growing segment due to the increasing need for advanced analytical tools and the integration of AI and machine learning capabilities. Software solutions enable organizations to efficiently manage and analyze large volumes of data, providing insights that drive strategic decision-making. The continuous development of innovative software solutions is expected to further boost this segment's growth.

The hardware segment includes servers, storage devices, and networking equipment required to support data warehousing operations. While the hardware segment does not grow as rapidly as the softwa

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Business Information Market Size 2025-2029

The business information market size is forecast to increase by USD 79.6 billion, at a CAGR of 7.3% between 2024 and 2029.

The market is characterized by the increasing demand for customer-centric solutions as enterprises adapt to evolving customer preferences. This shift necessitates the provision of real-time, accurate, and actionable insights to facilitate informed decision-making. However, this market landscape is not without challenges. The threat of data misappropriation and theft looms large, necessitating robust security measures to safeguard sensitive business information. As businesses continue to digitize their operations and rely on external data sources, ensuring data security becomes a critical success factor. Companies must invest in advanced security technologies and implement stringent data protection policies to mitigate these risks. Navigating this complex market requires a strategic approach that balances the need for customer-centric solutions with the imperative to secure valuable business data.

What will be the Size of the Business Information Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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In today's data-driven business landscape, the continuous and evolving nature of market dynamics plays a pivotal role in shaping various sectors. Data integration solutions enable seamless data flow between different systems, enhancing cloud-based business applications' functionality. Data quality management ensures data accuracy and consistency, crucial for strategic planning and customer segmentation. Data infrastructure, data warehousing, and data pipelines form the backbone of business intelligence, facilitating data storytelling and digital transformation. Data lineage and data mining reveal valuable insights, fueling data analytics platforms and business intelligence infrastructure. Data privacy regulations necessitate robust data management tools, ensuring compliance and protecting sensitive information.

Sales forecasting and business intelligence consulting offer valuable industry analysis and data-driven decision making. Data governance frameworks and data cataloging maintain order and ethics in the vast expanse of big data analytics. Machine learning algorithms, predictive analytics, and real-time analytics drive business intelligence reporting and process modeling, leading to business process optimization and financial reporting software. Sentiment analysis and marketing automation cater to customer needs, while lead generation and data ethics ensure ethical business practices. The ongoing unfolding of market activities and evolving patterns necessitate the integration of various tools and frameworks, creating a dynamic interplay that fuels business growth and innovation.

How is this Business Information Industry segmented?

The business information industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

End-user

  BFSI
  Healthcare and life sciences
  Manufacturing
  Retail
  Others


Application

  B2B
  B2C


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan
    South Korea


  Rest of World (ROW). 

By End-user Insights

The bfsi segment is estimated to witness significant growth during the forecast period.

In the dynamic business landscape, data-driven insights have become essential for strategic planning and decision-making across various industries. The market caters to this demand by offering solutions that integrate and manage data from multiple sources. These include cloud-based business applications, data quality management tools, data warehousing, data pipelines, and data analytics platforms. Data storytelling and digital transformation are key trends driving the market's growth, enabling businesses to derive meaningful insights from their data. Data governance frameworks and policies are crucial components of the business intelligence infrastructure. Data privacy regulations, such as GDPR and HIPAA, are shaping the market's development.

Data mining, predictive analytics, and machine learning algorithms are increasingly being used for sales forecasting, customer segmentation, and churn prediction. Business intelligence consulting and industry analysis provide valuable insights for organizations seeking competitive advantage. Data visualization dashboards, market research databases, and data discovery tools facilitate data-driven decision making. Sentiment analysis and predictive analytics are essential for marketing automation and business

Area exposed to one or more hazards represented on the hazard map used for risk analysis of the RPP. The hazard map is the result of the study of hazards, the objective of which is to assess the intensity of each hazard at any point in the study area. The evaluation method is specific to each hazard type. It leads to the delimitation of a set of areas on the study perimeter constituting a zoning graduated according to the level of the hazard. The allocation of a hazard level at a given point in the territory takes into account the probability of occurrence of the dangerous phenomenon and its degree of intensity. For multi-random PPRNs, each zone is usually identified on the hazard map by a code for each hazard to which it is exposed.

All hazard areas shown on the hazard map are included. Areas protected by protective structures must be represented (possibly in a specific way) as they are always considered subject to hazard (case of breakage or inadequacy of the structure). Hazard zones can be described as developed data to the extent that they result from a synthesis using multiple sources of calculated, modelled or observed hazard data. These source data are not concerned by this class of objects but by another standard dealing with the knowledge of hazards. Some areas within the study area are considered “no or insignificant hazard zones”. These are the areas where the hazard has been studied and is nil. These areas are not included in the object class and do not have to be represented as hazard zones. However, in the case of natural RPPs, regulatory zoning may classify certain areas not exposed to hazard as prescribing areas (see definition of the PPR class).

The need for a names-based cyber-infrastructure for digital biology is based on the argument that scientific names serve as a standardized metadata system that has been used consistently and near universally for 250 years. As we move towards data-centric biology, name-strings can be called on to discover, index, manage, and analyze accessible digital biodiversity information from multiple sources. Known impediments to the use of scientific names as metadata include synonyms, homonyms, mis-spellings, and the use of other strings as identifiers. We here compare the name-strings in GenBank, Catalogue of Life (CoL), and the Dryad Digital Repository (DRYAD) to assess the effectiveness of the current names-management toolkit developed by Global Names to achieve interoperability among distributed data sources. New tools that have been used here include Parser (to break name-strings into component parts and to promote the use of canonical versions of the names), a modified TaxaMatch fuzzy-matcher (to help manage typographical, transliteration, and OCR errors), and Cross-Mapper (to make comparisons among data sets). The data sources include scientific names at multiple ranks; vernacular (common) names; acronyms; strain identifiers and other surrogates including idiosyncratic abbreviations and concatenations. About 40% of the name-strings in GenBank are scientific names representing about 400,000 species or infraspecies and their synonyms. Of the formally-named terminal taxa (species and lower taxa) represented, about 82% have a match in CoL. Using a subset of content in DRYAD, about 45% of the identifiers are names of species and infraspecies, and of these only about a third have a match in CoL. With simple processing, the extent of matching between DRYAD and CoL can be improved to over 90%. The findings confirm the necessity for name-processing tools and the value of scientific names as a mechanism to interconnect distributed data, and identify specific areas of improvement for taxonomic data sources. Some areas of diversity (bacteria and viruses) are not well represented by conventional scientific names, and they and other forms of strings (acronyms, identifiers, and other surrogates) that are used instead of names need to be managed in reconciliation services (mapping alternative name-strings for the same taxon together). On-line resolution services will bring older scientific names up to date or convert surrogate name-strings to scientific names should such names exist. Examples are given of many of the aberrant forms of ‘names’ that make their way into these databases. The occurrence of scientific names with incorrect authors, such as chresonyms within synonymy lists, is a quality-control issue in need of attention. We propose a future-proofing solution that will empower stakeholders to take advantage of the name-based infrastructure at little cost. This proposed infrastructure includes a standardized system that adopts or creates UUIDs for name-strings, software that can identify name-strings in sources and apply the UUIDs, reconciliation and resolution services to manage the name-strings, and an annotation environment for quality control by users of name-strings.

Area exposed to one or more hazards represented on the hazard map used for risk analysis of the Risk Prevention Plan (RPP). The hazard map is the result of the study of hazards, the objective of which is to assess the intensity of each hazard at any point in the study area. The evaluation method is specific to each hazard type. It leads to the delimitation of a set of areas on the study perimeter constituting a zoning graduated according to the level of the hazard. The allocation of a hazard level at a given point in the territory takes into account the probability of occurrence of the dangerous phenomenon and its degree of intensity.

For multi-random PPRNs, each zone is usually identified on the hazard map by a code for each hazard to which it is exposed. All hazard areas shown on the hazard map are included. Areas protected by protective structures must be represented (possibly in a specific way) as they are always considered subject to hazard (case of breakage or inadequacy of the structure). Hazard zones can be described as developed data to the extent that they result from a synthesis using multiple sources of calculated, modelled or observed hazard data. These source data are not concerned by this class of objects but by another standard dealing with the knowledge of hazards.

Some areas within the study area are considered “no or insignificant hazard zones”. These are the areas where the hazard has been studied and is nil. These areas are not included in the object class and do not have to be represented as hazard zones. However, in the case of natural RPPs, regulatory zoning may classify certain areas not exposed to hazard as prescribing areas (see definition of the PPR class).

Context Social Media has been taking up everything on the Internet. People getting the latest news, useful resources, life partner and what not. In a world where Social media plays a big role in giving news, we must also know that news which affects our sentiments are going to get spread like a wildfire. Based on the Headline and the title, and according to the date given and the Social media platforms, you have to predict how it has affected the human sentiment scores. You have to predict the column “SentimentTitle” and “SentimentHeadline”.

Content This is a subset of the dataset of the same name available in the UCI Machine Learning Repository The collected data relates to a period of 8 months, between November 2015 and July 2016, accounting for about 100,000 news items on four different topics: economy, microsoft, obama and palestine.

Dataset Information The attributes for each of the dataset are :

IDLink (numeric): Unique identifier of news items Title (string): Title of the news item according to the official media sources Headline (string): Headline of the news item according to the official media sources Source (string): Original news outlet that published the news item Topic (string): Query topic used to obtain the items in the official media sources Publish-Date (timestamp): Date and time of the news items' publication Facebook (numeric): Final value of the news items' popularity according to the social media source Facebook Google-Plus (numeric): Final value of the news items' popularity according to the social media source Google+ LinkedIn (numeric): Final value of the news items' popularity according to the social media source LinkedIn SentimentTitle: Sentiment score of the title, Higher the score, better is the impact or +ve sentiment and vice-versa. (Target Variable 1) SentimentHeadline: Sentiment score of the text in the news items' headline. Higher the score, better is the impact or +ve sentiment. (Target Variable 2)

Original Data Source: News Popularity in Multiple Social Media Platforms

Area exposed to one or more surfacial hazards represented on the hazard map used for risk analysis of the RPP. The hazard map is the result of the study of hazards, the objective of which is to assess the intensity of each hazard at any point in the study area. The evaluation method is specific to each hazard type. It leads to the delimitation of a set of areas on the study perimeter constituting a zoning graduated according to the level of the hazard. The allocation of a hazard level at a given point in the territory takes into account the probability of occurrence of the dangerous phenomenon and its degree of intensity. For multi-random PPRNs, each zone is usually identified on the hazard map by a code for each hazard to which it is exposed.

All hazard areas shown on the hazard map are included. Areas protected by protective structures must be represented (possibly in a specific way) as they are always considered subject to hazard (case of breakage or inadequacy of the structure). Hazard zones can be described as developed data to the extent that they result from a synthesis using multiple sources of calculated, modelled or observed hazard data. These source data are not concerned by this class of objects but by another standard dealing with the knowledge of hazards. Some areas within the study area are considered “no or insignificant hazard zones”. These are the areas where the hazard has been studied and is nil. These areas are not included in the object class and do not have to be represented as hazard zones. However, in the case of natural RPPs, regulatory zoning may classify certain areas not exposed to hazard as prescribing areas (see definition of the PPR class).

you are required to split your attention between multiple sources of information (2018) for string quartet and large ensemble presents the players with a stream of auditory cues to which they must respond with specified sounds. The cues regularly switch between different types and are directed at different sub-groups within the two ensembles, requiring the players to think and act very quickly. The piece explores cognitive load and the way we remember associations between cues and responses. The cues include samples of real-world sounds which induce a range of different responses, as well as text-to-speech computer voices reading extracts from the Harvard Sentences (a set of phonetically balanced texts developed in the 1960s to test artificial voice modelling) and giving other verbal cues. The title is adapted from Mousavi, Low and Sweller’s 'Reducing Cognitive Load by Mixing Auditory and Visual Presentation Modes’ (1995) in which they investigate the split-attention effect and its impact on cognitive load. In the piece, all the cues and responses are aural, requiring players to negotiate the stream of information in one mode. The increased cognitive load affects the speed of response by players and the variations in time required to complete sound-producing actions on the different instruments, producing an unpredictable trail of sounds after each cue.This item contains the research timeline and research questions for you are required to split your attention between multiple sources of information.

Drug side-effects, or adverse drug reactions, have become a major public health concern and remain one of the main causes of drug failure and of drug withdrawal once they have reached the market. Therefore, the identification of potential severe side-effects is a challenging issue. In this paper, we develop a new method to predict potential side-effect profiles of drug candidate molecules based on their chemical structures and target protein information on a large scale. We propose several extensions of kernel regression model for multiple responses to deal with heterogeneous data sources. The originality lies in the integration of the chemical space of drug chemical structures and the biological space of drug target proteins in a unified framework. As a result, we demonstrate the usefulness of the proposed method on the simultaneous prediction of 969 side-effects for approved drugs from their chemical substructure and target protein profiles and show that the prediction accuracy consistently improves owing to the proposed regression model and integration of chemical and biological information. We also conduct a comprehensive side-effect prediction for uncharacterized drug molecules stored in DrugBank and confirm interesting predictions using independent information sources. The proposed method is expected to be useful at many stages of the drug development process.

Electronic Discovery (eDiscovery) Market Outlook

The global Electronic Discovery (eDiscovery) market size was valued at approximately $10.1 billion in 2023 and is projected to reach around $17.7 billion by 2032, expanding at a compound annual growth rate (CAGR) of 6.5% during the forecast period. This growth is primarily driven by the increasing volume of electronically stored information (ESI) and the rising need for effective digital data management and litigation support. As organizations continue to generate vast amounts of digital data, the demand for efficient eDiscovery solutions that can handle, process, and analyze this information will catalyze the market's expansion.

One of the key growth factors propelling the eDiscovery market is the exponential rise in data generation across various sectors. Organizations, regardless of their size or industry, are accumulating vast quantities of data from multiple sources. This surge in data volume, driven by the proliferation of digital communication channels, social media, and IoT devices, necessitates robust eDiscovery solutions to efficiently manage and sift through electronic data for legal and investigatory purposes. Furthermore, the tightening regulatory landscape and the need to comply with data protection laws, such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), are compelling organizations to adopt comprehensive eDiscovery tools that can ensure compliance and mitigate legal risks.

Another significant driver of the eDiscovery market is the growing complexity of legal investigations and the subsequent demand for advanced eDiscovery tools. Legal professionals are increasingly required to handle intricate cases involving massive datasets. This has spurred the development of sophisticated eDiscovery software that incorporates artificial intelligence (AI) and machine learning to streamline the data review process, enhance accuracy, and reduce the time and cost associated with legal discovery. Additionally, the integration of predictive coding and data analytics into eDiscovery solutions is enabling organizations to efficiently process large volumes of data, identify relevant information, and make informed decisions more rapidly, further fueling market growth.

The emergence of remote work and the increasing reliance on cloud-based solutions have significantly impacted the eDiscovery market. With the adoption of cloud technology, organizations are better equipped to manage and store electronic data, providing scalability and flexibility that on-premises solutions may lack. Cloud-based eDiscovery solutions offer enhanced collaboration capabilities, enabling legal teams to work seamlessly across geographical boundaries. Moreover, as remote work becomes a norm, the need for secure and agile eDiscovery tools that can handle data from diverse sources and devices is expected to drive market growth. This shift towards cloud-based solutions is anticipated to play a crucial role in shaping the future landscape of the eDiscovery market.

Regionally, North America currently dominates the eDiscovery market, accounting for the largest market share owing to the presence of major technology companies and law firms, along with the stringent regulatory framework in the region. The Asia Pacific region is projected to witness the highest growth rate during the forecast period, driven by the increasing adoption of digital technologies, rapid industrialization, and the growing awareness of data privacy and management. Europe also holds a significant market share, supported by stringent data protection laws and the presence of a robust legal industry. The Middle East & Africa and Latin America regions are expected to experience moderate growth, fueled by the gradual digital transformation and increasing awareness regarding the benefits of eDiscovery solutions.

Component Analysis

The eDiscovery market, segmented by components into software and services, presents a diverse landscape of growth and opportunities. Software solutions constitute a significant portion of this market, as they provide the necessary tools for data capturing, processing, analysis, and review. The market for eDiscovery software is anticipated to grow considerably over the forecast period as demand surges for advanced solutions that incorporate artificial intelligence and machine learning. These technologies facilitate more efficient data processing, reduce manual workloads, and offer predictive coding features that enhance the accuracy and speed of data analysis. Organizations are increasingly investing in such sophisticated software to gain a compet

Area exposed to flood hazard shown on the hazard map used for risk analysis of the RPP. The hazard map is the result of the study of the flood hazard, the objective of which is to assess the intensity of the hazard at any point in the study area. The evaluation method is specific to each hazard type. It leads to the delimitation of a set of areas on the study perimeter constituting a zoning graduated according to the level of the hazard. The allocation of a hazard level at a given point in the territory takes into account the probability of occurrence of the dangerous phenomenon and its degree of intensity.

Areas protected by protective structures must be represented (possibly in a specific way) as they are always considered subject to hazard (case of breakage or inadequacy of the structure). Hazard zones can be described as developed data to the extent that they result from a synthesis using multiple sources of calculated, modelled or observed hazard data. These source data are not concerned by this class of objects but by another standard dealing with the knowledge of hazards. Some areas within the study area are considered “no or insignificant hazard zones”. These are the areas where the hazard has been studied and is nil. These areas are not included in the object class and do not have to be represented as hazard zones. However, in the case of natural RPPs, regulatory zoning may classify certain areas not exposed to hazard as prescribing areas (see definition of the PPR class).

Area exposed to one or more hazards represented on the hazard map used for risk analysis of the Risk Prevention Plan (RPP). The hazard map is the result of the study of hazards, the objective of which is to assess the intensity of each hazard at any point in the study area. The evaluation method is specific to each hazard type. It leads to the delimitation of a set of areas on the study perimeter constituting a zoning graduated according to the level of the hazard. The allocation of a hazard level at a given point in the territory takes into account the probability of occurrence of the dangerous phenomenon and its degree of intensity.

For multi-random PPRNs, each zone is usually identified on the hazard map by a code for each hazard to which it is exposed. All hazard areas shown on the hazard map are included. Areas protected by protective structures must be represented (possibly in a specific way) as they are always considered subject to hazard (case of breakage or inadequacy of the structure). Hazard zones can be described as developed data to the extent that they result from a synthesis using multiple sources of calculated, modelled or observed hazard data. These source data are not concerned by this class of objects but by another standard dealing with the knowledge of hazards.

Some areas within the study area are considered “no or insignificant hazard zones”. These are the areas where the hazard has been studied and is nil. These areas are not included in the object class and do not have to be represented as hazard zones. However, in the case of natural RPPs, regulatory zoning may classify certain areas not exposed to hazard as prescribing areas (see definition of the PPR class).

Area exposed to one or more hazards represented on the hazard map used for risk analysis of the RPP. The hazard map is the result of the study of hazards, the objective of which is to assess the intensity of each hazard at any point in the study area. The evaluation method is specific to each hazard type. It leads to the delimitation of a set of areas on the study perimeter constituting a zoning graduated according to the level of the hazard. The allocation of a hazard level at a given point in the territory takes into account the probability of occurrence of the dangerous phenomenon and its degree of intensity. For multi-random PPRNs, each zone is usually identified on the hazard map by a code for each hazard to which it is exposed. All hazard areas shown on the hazard map are included. Areas protected by protective structures must be represented (possibly in a specific way) as they are always considered subject to hazard (case of breakage or inadequacy of the structure). Hazard zones can be described as developed data to the extent that they result from a synthesis using multiple sources of calculated, modelled or observed hazard data. These source data are not concerned by this class of objects but by another standard dealing with the knowledge of hazards. Some areas within the study area are considered “no or insignificant hazard zones”. These are the areas where the hazard has been studied and is nil. These areas are not included in the object class and do not have to be represented as hazard zones. However, in the case of natural RPPs, regulatory zoning may classify certain areas not exposed to hazard as prescribing areas (see definition of the PPR class).

Harmonization of monitoring protocols and analytical methods is a crucial issue for transnational marine environmental status assessment, yet not the only one. Coherent data management and quality control become very relevant when environmental status is assessed at regional or subregional scale (e.g., for the Mediterranean or the Adriatic Sea), thus requiring data from different sources. Heavy metals are among the main targets of monitoring activities. Significant efforts have been dedicated to share best practices for monitoring and assessment of ecosystem status and to strengthen the network of national, regional and European large data infrastructures in order to facilitate the access to data among countries. Data comparability and interoperability depend not only on sampling and analytical protocols but also on how data and metadata are managed, quality controlled and made accessible. Interoperability is guaranteed by using common metadata and data formats, and standard vocabularies to assure homogeneous syntax and semantics. Data management of contaminants is complex and challenging due to the high number of information required on sampling and analytical procedures, high heterogeneity in matrix characteristics, but also to the large and increasing number of pollutants. Procedures for quality control on heterogeneous datasets provided by multiple sources are not yet uniform and consolidated. Additional knowledge and reliable long time-series of data are needed to evaluate typical ranges of contaminant concentration. The analysis of a coherent and harmonized regional dataset can provide the basis for a multi-step quality control procedure, which can be further improved as knowledge increases during data validation process.