The Biggest Challenges Facing Science

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new Data product platforms are the ideal solution to these bottlenecks. They act as cohesive ecosystems, harmonizing fragmented data landscapes into unified, accessible, and actionable data repositories. By setting universal standards for data quality and validation, these platforms open-up opportunities for deeper insights, strong decision-making, and increased profitability and innovation. These data product platforms provide an in-depth look at the challenges of using data effectively while offering practical solutions and highlighting the high stakes involved in each barrier. — insideBIGDATA, 1d ago

new Cloud innovation leads to data sprawl, causing a lack of visibility in cloud infrastructure. Duplicated data increases the risk of unauthorized access and non-compliance. Security leaders are now adopting DSPM for comprehensive coverage, continuous discovery, and accurate classification of sensitive cloud data. — sentra.io, 2d ago

new Smarter Data Governance: Here, the focus is to reduce the data footprint further by taking some action. We must try and store the data that is a ‘must-have’ and avoid redundant and secondary data that can easily be regenerated from originally sourced data. Faster computing today and in-memory processing enables enterprises to generate reports or aggregated data quickly. These reports may not need to be stored for long periods. Efforts must be made to reduce the data copies. Retention policies must be revisited as per the latest compliance to explore the possibility of data retention periods. Regular dark data and files assessment can help reduce the data footprint by dropping the data that is either redundant or not contributing to the business. — DATAQUEST, 2d ago

Data access and sharing: Access to very large sets of data is critical for energy AI. There are technical and organizational access constraints (e.g. data size, different data systems, different data owners, governance, and more). Innovative technologies can solve these problems (e.g. governed data virtualization). Logical datasets are defined from a variety of data sources (e.g. databases, warehouses, lakes) and centralizes access controls eliminate the need for duplication or migration of data for AI. — Energy Central, 5d ago

..."But effective data governance is a challenge for insurers, many of which are large enterprises. Our survey of 600 global executives revealed half are using five or more technologies to support data management priorities in 2023. This complexity introduces technical debt, interrupts the data supply chain, and makes transparency, data quality and governance significantly more difficult to achieve.”... — insurtechdigital.com, 3d ago

new Bradley Shimmin, chief analyst for AI platforms, analytics and data management at Omdia, said: “Without a doubt, organizations capable of accessing, analyzing and acting on data in near-real time hold a distinct competitive edge over their rivals. It’s no wonder then that the majority of companies rank the ability to access high quality, secure and well-governed data assets as their number one investment driver, according to a detailed study of 320 analytics and data management professionals. Unfortunately, a constant influx of new technologies, data types and data sources together conspire to hold data processing back, turning what should be a crucial, timely asset into a costly performance bottleneck. What’s needed is a key to unlock this data at scale through an analytics acceleration engine that brings the benefits of hardware acceleration to data processing. That’s where vendors like Voltron Data come into play.”... — Datanami, 2d ago

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However, this rapid adoption poses a myriad of challenges around data governance and model management. Without robust data governance capabilities, the potential impact and value added by Generative AI will be severely limited and may even expose organisations to data and cybersecurity risks. — technologymagazine.com, 3d ago

Data classification is the key to achieving both a smart data strategy and compliant AI data governance. Gaining a clear understanding of what aged, duplicate, trivial, and risk data exists within a given environment and being able to properly manage that data, via deletion, migration, or opting to not input it into AI tools, is only possible by first classifying that data. Beyond risk mitigation, data classification also lends itself to data storage cost savings through defensible remediation. Enterprises with robust, ongoing data classification practices, on average, eliminate 10% orphan data risk, eliminate copy data growth due to backup by 20%, and can push 20% more infrequently used data to object storage. Once AI regulations are fully in place, active data classification will also play a significant role in maintaining compliance and avoiding costly financial and reputational penalties. — prnewswire.com, 4d ago

There has been fast-paced growth in the application of artificial intelligence (AI) in cancer research. Despite this growth, there are challenges in both collecting and generating data in a way that makes it easily accessible and usable for AI/ML applications while maintaining security and data quality. Data which is accessible and usable for AI/ML applications is referred to as AI-ready data. AI-ready data can lead to the development of well-validated AI/ML models that can be deployed for research and improvement of healthcare. AI-readiness encompasses various characteristics, including completeness of the data (e.g., sufficient volume and managed missing values), incorporation of data standards (e.g., utilizing ontologies and terminologies whenever possible), computable formats, documentation (process and intent in generating the data), data annotations, data balance, data privacy, data security, among other features. — nih.gov, 4d ago

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Any data problems? Let’s see: Automatic digitization of data, automatic generation of data, real-time data integration… Multiple data stores, data meshes, data fabrics… Data size, data location, data availability, data usability, data entry, data logging, data storage, data movement, data transmittal, data access, data updating, data sharing, data analysis, data analytics… (breathe)… Data governance, data privacy, data security… Data Management is the AI dealbreaker. Emerging best practices, such as data meshes and data fabrics, can help independent teams work in a more cohesive manner that respects today’s emerging data governance, privacy, and security requirements. — HPC + AI on Wall Street, 7w ago

Third, new systems will be needed to incentivize data sharing along with standards for privacy, security, access, aggregation, anonymization, and curation. Systems of privacy-preserving machine learning should implement FAIR principles (that is, findability, accessibility, interoperability, and reusability of digital assets) and ensure standardization of the data’s structure and metadata so that these data can more easily be analyzed by AI. Currently, integration and crosstalk between existing databases of human immunology and genetics remains cumbersome at best and non-existent at worst. — STAT, 8w ago

Access to quality data is fundamental for the success of AI initiatives. However, data accessibility remains unequal across various domains and regions. Furthermore, data quality is paramount. Inaccurate or biased data can lead to flawed AI outcomes, perpetuating existing biases and generating unreliable predictions. Addressing these issues requires collaborative efforts to improve data collection, sharing, and verification practices. — globaltechcouncil.org, 10w ago

With data virtualization, business users have access to a holistic view of information, across all the source systems. There is a singular data-access layer established, eliminating the need for physical data movement, while seamlessly handling diverse data formats. This guarantees data accuracy, completeness, and standardization – fostering a reliable foundation for data-driven decisions and analysis. — DigiconAsia, 12w ago

By establishing clear parameters for data access, accuracy, privacy, security, and retention, savvy leaders leverage data governance to ensure quality, democratize access, and protect privacy. An effective data governance can provide clarity into key questions: Questions around data accuracy (e.g., Is the data correct and without discrepancies?), completeness (e.g., Am I getting the full picture?), reliability (e.g., Is the data consistent and trustworthy over time and across contexts?), and relevance (e.g., Does the data provide the insight I need?). — Salesforce, 4w ago

...3. Data Management and Privacy: With the Einstein 1 Platform collecting vast amounts of data for better insights, managing this data becomes paramount. It raises questions about data storage, retrieval, and most importantly, privacy. IT departments will need to ensure data residency, PII protection, GDPR compliance and other regional data protection regulations. Understanding how Data Cloud supports these needs and plays well with other data platforms will be key. For its part, Salesforce seems committed, rightly, to openness and interoperability. — Constellation Research Inc., 10w ago

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...“Without a doubt, organizations capable of accessing, analyzing and acting on data in near-real time hold a distinct competitive edge over their rivals. It’s no wonder then that the majority of companies rank the ability to access high quality, secure and well-governed data assets as their number one investment driver, according to a detailed study of 320 analytics and data management professionals. Unfortunately, a constant influx of new technologies, data types and data sources together conspire to hold data processing back, turning what should be a crucial, timely asset into a costly performance bottleneck. What’s needed is a key to unlock this data at scale through an analytics acceleration engine that brings the benefits of hardware acceleration to data processing. That’s where vendors like Voltron Data come into play,” said Bradley Shimmin, chief analyst for AI platforms, analytics and data management at Omdia. — Help Net Security, 3d ago

The first step in archiving messages is to find an archival solution that prioritizes global administrator access and secures storage with AES encryption and compliance with HIPAA, SOX, and SEC regulations. When choosing a record management platform, it should ensure accessibility in various formats and a cloud-based platform with geographically dispersed data centers for swift eDiscovery responses. This includes data ingestion, indexing, search, eDiscovery, and export. A robust archival system ensures sub-second response times when searching extensive datasets. This level of processing is achievable only through a cloud-computed CPU, capable of real-time scaling for quick and timely search experiences. — TigerConnect, 3d ago

The field of AI, including Deep learning, completely relies on data, and important data is stored in data warehouses, where it is dispersed among multiple tables linked via primary-foreign key relationships. Developing ML models with such data presents a number of difficulties and takes a lot of time and work, as the existing ML approaches are not well suited to learning directly from data that spans several relational tables. Current methods require that data be transformed into a single table via a procedure called feature engineering. — MarkTechPost, 3d ago

With the increasing prevalence of digital devices and online interactions, people are generating more data than ever before. The technical ability to harvest it and the economic incentive to do so are growing. The privacy risks associated with its use have led to the emergence of data protection legislation, such as GDPR. Still, the economic potential of data is enormous, which leads to legislation allowing for sharing, such as the Data Act or the Data Governance Act. Currently, policy work is ongoing focused on the development of environments designed to facilitate data sharing in sector or domain specific areas, such as finance, health, and energy. Policymakers have labelled such environments European Data Spaces (EDS). — Tilburg University, 4d ago

Over the last couple of decades, there has been a rapid growth in the number and scope of agricultural genetics, genomics and breeding databases and resources. The AgBioData Consortium (https://www.agbiodata.org/) currently represents 44 databases and resources (https://www.agbiodata.org/databases) covering model or crop plant and animal GGB data, ontologies, pathways, genetic variation and breeding platforms (referred to as ‘databases’ throughout). One of the goals of the Consortium is to facilitate FAIR (Findable, Accessible, Interoperable, and Reusable) data management and the integration of datasets which requires data sharing, along with structured vocabularies and/or ontologies. Two AgBioData working groups, focused on Data Sharing and Ontologies, respectively, conducted a Consortium-wide survey to assess the current status and future needs of the members in those areas. A total of 33 researchers responded to the survey, representing 37 databases. Results suggest that data-sharing practices by AgBioData databases are in a fairly healthy state, but it is not clear whether this is true for all metadata and data types across all databases; and that, ontology use has not substantially changed since a similar survey was conducted in 2017. Based on our evaluation of the survey results, we recommend (i) providing training for database personnel in a specific data-sharing techniques, as well as in ontology use; (ii) further study on what metadata is shared, and how well it is shared among databases; (iii) promoting an understanding of data sharing and ontologies in the stakeholder community; (iv) improving data sharing and ontologies for specific phenotypic data types and formats; and (v) lowering specific barriers to data sharing and ontology use, by identifying sustainability solutions, and the identification, promotion, or development of data standards. Combined, these improvements are likely to help AgBioData databases increase development efforts towards improved ontology use, and data sharing via programmatic means. Database URL https://www.agbiodata.org/databases. — CGIAR, 6d ago

Standardized Frameworks: The lack of a standardized approach to collecting, analyzing and utilizing SDOH data is a significant hurdle. Without a coherent framework, the efforts of healthcare providers may remain siloed, thereby diminishing the potential impact of the collected data on patient care and outcomes. Developing standardized frameworks for SDOH data collection, analysis and utilization can drive consistency, collective utility, and quality in SDOH data practices. — hitconsultant.net, 6d ago

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Trust of Data and Data Sharing in Supply Chains: One prominent challenge revolves around establishing trust in data and facilitating secure data sharing within complex ecosystems like supply chains. Clients often grapple with issues related to data authenticity, integrity, and privacy, particularly when collaborating with multiple partners and stakeholders. Ensuring that data remains trustworthy as it moves through the supply chain is a top concern. — The Block, 25d ago

Having standardized assay descriptors that adhere to FAIR (Findable, Accessible, Interoperable, and Reusable) principles is crucial for research teams because it ensures that assay data is consistently structured and well-documented. Standardized descriptions promote efficient data sharing and collaborations. Interoperability allows seamless integration with other systems and tools, streamlining data exchange and analysis. FAIR principles boost data accuracy, reproducibility, and comparability, minimizing redundancy. — GEN - Genetic Engineering and Biotechnology News, 8w ago

Infosec and privacy teams struggle to track sensitive files since a file can be accessed by multiple systems, applications, and devices as users share it. This challenge grows as data visibility is limited when documents travel and change through duplication or revisions. Without proper data visibility, you are not aware that sensitive information is shared, moved, changed, or deleted. — Infosecurity Magazine, 4w ago

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Centralized Data Access Management: As enterprises reach a higher level of data maturity, having centralized Data Governance is becoming the best way to enforce compliance and internal security policies. Data silos make it impossible to gain a clear unified view of business data. In addition, organizations that house data in multiple systems face challenges maintaining data security. Centralizing data access creates a less complex environment for data, making it easier to monitor and control the use and protection of data. Centralized access control consolidates access control management into a central system that determines permissions based on predefined policies, and international, local, and industry-specific compliance standards and enables access events to be logged and monitored in real-time to confirm compliance with regulations. — DATAVERSITY, 7d ago

In our globally interconnected information age, cross-border data sharing is both crucial for international collaboration in criminal investigations and a potential source of privacy concerns. Canada must carefully navigate these data-sharing agreements while ensuring that personal information is properly safeguarded across jurisdictions. — Bit Rebels, 7d ago

Still, IoT data can be challenging for organizations that are unprepared. The real-time nature of this data, combined with volume and varied formats (such as telemetry, metrics, and logs), adds much more complexity to any environment. To compound matters, this data usually arrives at speed and through massive streams, making it difficult to leverage this data before it becomes stale and outdated. — RTInsights, 4d ago

During the data mapping process, your organization should also consider data cleansing and transformation. This involves standardizing data formats, eliminating duplicates and ensuring data integrity. By cleansing and transforming the data before migration, your organization enhances data quality and reduces the risk of errors or inconsistencies. — Ultra Consultants | ERP Implementation & Consulting, 4d ago

What’s hindering progress? Lack of data quality and data infrastructure are the two most often cited reasons in the survey. And “many CFOs complain about a prevailing silo mentality that hinders cross-divisional collaboration — a basic prerequisite for making large amounts of data usable,” said Horváth. “Resistance to change” was the fourth most-cited reason. — CFO, 5d ago

Facepalm: OwnCloud is an open-source software designed for sharing and syncing files in distributed and federated enterprise environments. The tool provides collaboration and document-sharing services, but a recently disclosed vulnerability has extended its "sharing" capabilities in an unintended way, compromising sensitive data. — TechSpot, 5d ago

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These contributions would remain useless, however, without an improvement in data interoperability. Seamless data sharing across provider systems and public health agencies is critical for assembling a clear picture of emerging outbreaks. Providers should capture structured, standards-based data in electronic health records and use health information exchanges to enable responsible data sharing at the local, national, and global levels. — MedicalEconomics, 11w ago

Without policy change, the United States will continue trending toward data siloes—an inefficient world in which data is isolated, and its benefits are restricted. Data siloes are repositories of information that exist in a closed system, often sealed off from the rest of an organization or other organizations and incompatible with other datasets. Data sharing spans a whole spectrum of possibilities: on one end are data siloes, where data remains isolated and unshared, and on the other end are data collaboratives, where data flows freely between organizations with no restrictions on use. The United States needs to move more toward data collaboratives, and doing so will require overcoming these legal, social, technical, and economic barriers. It will take coordinated government action to both enable data sharing by default and counter pervasive privacy fears. Specifically, policymakers should:... — itif.org, 10w ago

In biopharma manufacturing, there is a fine line between being data rich and information poor. Sites are filled with disparate systems, increasing the potential for siloed data. But since data is so key, biopharma companies must find better ways to mine that data and use it to improve manufacturing processes and achieve data infrastructure excellence. Which side of the line a company lands on largely comes down to data infrastructure excellence – mastering how data is collected, stored and leveraged to drive meaningful change. Many are looking at data historian technology to deliver multiple-system and cross-site data collection, storage and analysis to drive business innovation and efficiencies. — BioPharma Dive, 14d ago

As an all-in-one healthcare data platform, 1Clearsense treats databases and message types as Data Sources. These sources are ingested and mapped into the Clearsense Healthcare Data Model, which offers accelerators for quick implementation of connections, mapping, and data quality rules. It retains data lineage and traceability while allowing for granular data access policies and security controls. Payer and provider data can reside within the common healthcare data model, supporting source of truth determination and trust rule assignment. Accessible data can be used in Clearsense analytics, outgoing integrations, and customer-specific applications through 1Clearsense's bring-your-own-tool (BYOT) capabilities. — Clearsense, 10w ago

Significant efforts are underway to create common data standards and terminology on SDOH factors to facilitate effective data exchange among providers and other sectors. However, locking providers into one data exchange method doesn’t truly advance interoperable care at all access points or every care setting. When the industry is encouraged to innovate and solve problems such as equal access to digital exchange of patient information, solutions emerge that provide the easy on ramp for post-acute care. — McKnight's Long-Term Care News, 11w ago

Access: Excessive access is the crux of insider threat. Businesses today are built on collaboration and sharing, and often productivity and the availability of data trumps security. Knowing exactly who can access data and limiting that access in a way that does not impact productivity is key to mitigating risk. — The Hacker News, 19d ago

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A well-defined data governance policy is instrumental in ensuring that client data is handled responsibly and securely. This policy should outline the processes and protocols for data collection, storage, access, and disposal within the firm. Assigning access levels based on job roles and the principle of least privilege minimizes the risk of unauthorized access. — Techiexpert.com, 6d ago

Data Fragmentation: As data gets modified, updated or deleted, over time storage can become fragmented. This leads to space amplification with inefficient disk space usage and increased I/O operations during reads, resulting in read amplification. — insideBIGDATA, 6d ago

The most pressing issue is the ethical use of data. As AI systems require vast amounts of data, ensuring privacy and consent is paramount. This challenge should be addressed by implementing stricter data governance laws and ensuring transparency in how AI systems use and process data. — Dataconomy, 6d ago

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Enhanced data accessibility – Comprehensive communication tools eliminate data silos, providing users with a unified view of data from dissimilar sources. This centralized repository removes obstacles, simplifying access for authorized personnel to retrieve pertinent information. This streamlined process enhances workflow efficiency and promotes collaboration. — Insightssuccess Media and Technology Pvt. Ltd., 7d ago

Customers can also use Amazon Redshift data sharing to securely share this data with multiple consumer clusters used by different teams—both within and across AWS accounts—driving a unified view of business and facilitating self-service access to application data within team clusters while maintaining governance over sensitive operational data. — Zephyrnet, 5d ago

Recognizing that data is a collective asset, sharing analyses and insights across teams is crucial. Whether it is the delivery team ensuring timely arrivals or customer advisors using past data to anticipate needs, disseminating insights enhances overall performance and customer service. — Techiexpert.com, 5d ago

Effective resolutions will likely require collaboration between tech giants and external regulators. Without the tech companies, external regulators will not have the requisite technical expertise or access to internal data and testing to identify harms and mandate appropriate solutions without the risk of unintended consequences. It is therefore essential for tech companies to provide access to internal data to facilitate informed oversight. For instance, the Twitter Developer Platform, which provided API access to internal Twitter data for academics, was a ‘best-in-class’ example of data sharing before access was paywalled.[xxxv]... — Shorenstein Center, 5d ago

Databricks facilitates an open approach to data sharing, allowing teams to collaborate across platforms while securing and governing live datasets, models, dashboards and notebooks. It unifies data teams, streamlines data ingestion and management, and ensures that data lakes seamlessly manage all structured, semi-structured,d and unstructured data. Further, it forms the foundation of Databricks' native machine-learning capabilities. — IT Brief New Zealand, 6d ago

Standardised Master Data: The foundation of SAP MM is the concept of master data, which is a centralised database containing vital information on suppliers, materials, and the whole procurement process. Transactions, reporting, and workflows are all impacted by this repository. Precise master data improves operational coherence and process agility in addition to lowering mistakes. For example, a business can simply monitor procurement procedures with consistent master data, guaranteeing on-time delivery and precise inventory levels. — cioapplicationseurope.com, 6d ago

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Data is in constant movement across hybrid IT, shared with third parties, and transferred internationally. The challenge is to persistently protect high-value data wherever it is, while enabling usability to drive business value. Standards-based, data-centric encryption and anonymization deliver persistent security and privacy wherever data resides, moves, and is used. Voltage data protection enables secure data use with proven scalability in cloud, analytics, and information-sharing use cases, while supporting the requirements of privacy, PCI, HIPAA, financial, and other global and industry regulations. — gbiimpact.com, 6d ago

A dynamic data management system is an architecture commonly employed by many businesses to efficiently store and organize various types of data for the purpose of indexing and linking databases. In this type of database, the value of data is closely tied to the input data during retrieval, and it also specifies how memory resources are allocated for this data. Dynamic data management systems are highly favored because they serve as a central tool for storing, managing, and tracking vital business information. — marketresearchblog.org, 7d ago

Distributed big data processing presents challenges regarding data transfer, responsiveness to new data, code conversion and security. — edgeir.com, 7d ago

Marketers collaborate with legal to ensure secure data sharing through appropriate contract provisions. Crafting solutions and documents for data sharing, processing, and analysis reflects the increasing value of data in driving marketing decisions across various industries. — Martech Podcast, 6d ago

Intelligent automation electronically identifies, parses, and sends the correct data to proper recipients, including IT systems. And new industry guidelines have emerged to protect data accuracy, quality and completeness throughout the journey. — Health IT Answers, 6d ago

Citizen data—that is data produced by people or organizations to directly monitor, demand, or drive change on issues that affect them2 —, has the potential to meet this need. In particular, citizen data is able to fill crucial data gaps for vulnerable and marginalized groups, integrating their situations and conditions into statistical production processes. Citizen data also progresses fundamental values namely fairness, openness, inclusiveness, non-discrimination, accountability and transparency in statistics. — ESCAP, 7d ago

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Granica Screen is a high-accuracy training data privacy service which de-identifies PII and other sensitive information at high scale, unlocking more data for model training and GenAI fine tuning while remaining compliant with data privacy requirements. It champions data security through its privacy-enhancing mechanisms, empowering private machine learning, language models, and AI applications while upholding user data security. — siliconindia.com, 9d ago

Data privacy is a critical concern in eDiscovery, as organizations handle vast amounts of sensitive data during the discovery process. It is essential to protect individuals’ personal information from unauthorized access, disclosure, and misuse. To ensure data privacy, organizations must implement strict access controls, encryption, and anonymization techniques. Additionally, data minimization principles should be followed to only collect and process the necessary data for legal purposes. By prioritizing data privacy, organizations can maintain compliance with regulations and build trust with stakeholders. — Techiexpert.com, 9d ago

Dynamic updating of datasets with accurate data. While AI offers benefits in healthcare, its adoption faces hurdles. To meet clinical treatment’s contextual requirements, capturing accurate data and dynamic updating of datasets are crucial. Therefore, AI solutions must handle unstructured real-world data and frequent real-time updates. A distributed governance model will bring closer proximity between the data producer, data user, and their governing authority. — Montreal AI Ethics Institute, 8d ago

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Despite growing data security concerns, it is clear that data sharing is essential in today’s business landscape. As data volumes continue to grow and organizations increasingly share more data both internally and externally, teams face the challenge of keeping every single one of these exchanges secure. This is especially vital if businesses are striving to obey specific data use and licensing agreements that enable them to monetize their data products. As a result, organizations should ensure their data sharing processes are adequately reinforced to avoid any data loss or breaches. Federated models for access control management help teams to share data in a controlled way. Centrally imposed rules for regulatory compliance can be augmented with rules defined by data owners for business and contractual compliance. — securitymagazine.com, 10w ago

Mobility data sharing is currently often hampered by a complex legal situation and ambiguities in data sovereignty and data protection. This problem needs to be addressed by bundling the existing legal provisions and more transparent rules on data sovereignty, especially in relation to vehicle data. — eco, 9w ago

Unlike situations where access restrictions hinder data sharing among departments or partner companies, synthetic data's accessibility primarily depends on the owner's jurisdiction. Encouraging data sharing and collaboration has the potential to facilitate responsible, secure, and efficient AI model development. — The Engineer, 12w ago

Limited Data Sharing: Collaboration between states or across healthcare systems is often cumbersome due to incompatible systems and privacy regulations. — ReadWrite, 5w ago

In the era of open data and open science it is important that data on Indigenous knowledge is shared in an ethical manner. Decisions on what data is to be shared should lie with Indigenous populations themselves, ensuring their autonomy and informational self-determination. The subgroup focuses on data principles such as CARE and JUST. Moreover, both Indigenous data sovereignty and data ethics need institution building for data trustees (and similar intermediaries) which would enable selective digital disclosure. — RDA, 7w ago

Data democracy without question shouldn’t be a fuzzy organizational goal. It is core to enabling businesses to run better and transform. Data democracy involves more than adding data expertise. It involves connecting domain and data expertise. The authors suggest data democracy occurs when organizations have pervasive employee appreciation of, access to, and use of data assets and monetization capabilities. — CMSWire.com, 8w ago

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Implementing Data Protection and Data Privacy (DPDP) measures involves challenges both on the government and organizational fronts. Apurva states. “Organizations encounter hurdles in ensuring data encryption for critical information and obtaining consent for data usage from individuals.”... — DATAQUEST, 11d ago

Application – High-quality data is vital in any data-related operation, being a priority in scenarios where you deal with analytics, visualization, reporting, and decision-making. Data with a high degree of integrity is important in business sectors where the reliability of data is key. For example, data integrity is essential in the healthcare system and financial records. — Robotics & Automation News, 10d ago

...“Although there’s significant growth in the number of companies experimenting with generative AI, many face roadblocks from a fractured data infrastructure that lacks real-time data availability and trust,” said Stewart Bond, Vice President, Data Intelligence and Integration Software, IDC. “Data management is the most important area of investment as organisations build an intelligence architecture that delivers insights at scale, supports collective learning, and fosters a data culture. Those that get it right have seen a 4x improvement in business outcomes by removing real-time data availability and trust roadblocks through data streaming, governance, security, and integration–so it’s worth the journey.”... — computerworld.com, 12d ago

An audience participant suggested the formation of consortia to share solutions for free, to increase access to and transparency of groundwater data. Storm Hansen agreed that this is a crucial question, and mentioned ongoing efforts in international collaborations to address data accessibility challenges. However, due to differing approaches to data sharing and secrecy, creating a single global platform is challenging. This was echoed by Auken who hoped that, despite these complexities, ongoing initiatives could provide viable solutions for storing and accessing physical data. — SIWI - Leading expert in water governance, 11d ago

ADS and IOO stakeholders can share a variety of resources, including skills and expertise in addition to sharing of information and data. An example is the Arizona IAM Consortium Collaborative Data Sharing, which is leveraging existing infrastructure to collect performance data on public roads. The research relies on a low data ask from collaborating partners. This may encourage greater openness and participation from ADS stakeholders. — Traffic Technology Today, 11d ago

By analysing data governance models and inherent properties of data, we point towards public data commons as the model securing European values and increasing sharing. — Internet Policy Review, 10d ago

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Data silos and lack of data agility have long been significant issues for local authorities and other public sector organisations. This hinders regional tech ecosystems from growing and developing according to rapidly changing markets. Full fibre technology offers faster data transfers and access to cloud data storage, making data transmission more agile, secure, and future-proof. — techuk.org, 20d ago

Despite the evident benefits of data-driven decision-making, challenges persist in its adoption. One significant challenge in data-driven decision-making is the quality and availability of data. Projects often face significant challenges, such as inadequate data collection methods and incomplete datasets. These challenges often undermine the reliability and accuracy of insights derived from data analysis, leading to flawed conclusions and ineffective decision-making. — usaidlearninglab.org, 12w ago

Setting effective data standards, and not idealistic ones, can only happen through inter-department collaboration. An extension of this data governance principle is to enable access to data through an intuitive catalog. — onetrust.com, 5w ago

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This publication defines basic terminology and explains fundamental concepts in data classification so there is a common language for all to use. It can also help organizations improve the quality and efficiency of their data protection approaches by becoming more aware of data classification considerations and taking them into account in business and mission use cases, such as secure data sharing, compliance reporting and monitoring, zero-trust architecture, and large language models. — The ChannelPro Network, 8d ago

Medical research is dynamic and vast, so institutions have been apprehensive about experimenting with too many Data Management solutions. After all, the risk of exposure and, ultimately, a patient data confidentiality breach can create havoc. At the same time, there’s no denying that data plays an indispensable role in shaping the future of healthcare. In the pursuit of facilitating qualitative and timely care, on-demand data insights are required, and it can’t happen without platform interoperability. — DATAVERSITY, 13d ago

Despite great advances in data collection, the detailed picture of global country-country migrant flows remains incomplete. The lack of a comprehensive, high-quality data base covering not only stocks, but also flows is and remains the pivotal obstacle for the analysis of international migration at a global level. In the current situation it occurs, that methodological progress enabling the handling of large migration data sets is much more advanced than (raw) data collection and the harmonization of that information. Therefore, some results of the novel methods converting information on stocks into flow estimates might be instructive, but cannot be fully used for research and sound policy advice, as long, as the raw-data problem is not solved or significantly tempered. — knomad.org, 11d ago

To seize these digital opportunities, local government must prioritise data quality assurance, governance, and management. This involves regular validation, updates, cleansing, and the implementation of data standards, ensuring data accuracy, timeliness, and relevance. This is an area where Egress Group can add real value to our customers in local government. Our Data Quality Assessment consists of reviewing existing source systems and data governance protocols, then illustrating the best approach for data quality considerations and resolutions. Through a commitment to data quality, local government can fully leverage the potential of digital transformation, benefiting both governance and its citizens. — techuk.org, 11d ago

Data Storage and Access: This involves establishing a robust platform that guarantees trust, confidentiality, and straightforward access to data. To make DPP data storage more efficient, a DPP database could already be created before entry into the EU market. In the lifecycle stages, the company should be able to enter data directly into DPP to facilitate data sharing and easy accessibility. — METAV.RS, 14d ago

In the evolving landscape of data analytics and AI, the ability to access both structured and unstructured data directly from the storage layer is an increasingly common – and necessary – approach. But protecting this data without creating policy bloat on storage or delaying speed to data is a challenge. — Immuta, 12d ago

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But there are ways that both the data access controls can protect data and ensure data access compliance with data regulations. These approaches guarantee that authorized individuals have access to relevant data, while simultaneously monitoring data usage in real-time and implementing appropriate protective measures. Once these comprehensive and inherently compliant methods are established, data access is expedited. — Global Security Mag Online, 6w ago

Despite the crucial need to make the most of data, various challenges stand in the way. “Data quality and metadata tagging remain a challenge even though they have improved and are continuing to improve,” Christianson observes. “Multimodal data integration also remains a challenge, not least due to continued exponential growth of data volume, velocity, and variety, and to greater scrutiny of data veracity.”... — GEN - Genetic Engineering and Biotechnology News, 7w ago

Data Management and Analysis: Genomics generates vast amounts of data, creating challenges in data storage, management, and analysis. Genomic datasets are large, complex, and require sophisticated computational infrastructure and algorithms for processing and interpretation. Managing and analyzing such massive datasets pose technical and logistical challenges, including data storage, scalability, data integration, and data privacy. — marketsandmarkets.com, 6w ago

The framework should outline the processes and workflows for data governance activities. This includes data classification and categorization, data profiling, data quality assessment, data access requests, and data issue resolution. Processes for data stewardship, data lineage tracking, and data documentation should also be defined. Collectively, these processes enable organizations to manage data effectively by ensuring that data is consistently monitored, improved, and aligned with business objectives. For instance, data profiling processes can help identify data quality issues early, and data access request processes ensure that only authorized personnel have access to the data. Well-defined data governance processes enhance data transparency, reduce risks, and streamline data management operations. — Datamation, 10w ago

With advanced data collection, ethical considerations take center stage. The challenge lies in finding a balance between comprehensive data collection and preserving privacy. Data relevance and use are also important. Data should not be collected unless the need for it is clear, and collected data must be analyzed, the statistics produced made widely available, and effectively used. This calls for strong national systems that follow the ten fundamental principles of official statistics, as well as government officials, civil society and development practitioners who are data literate and equipped to use data effectively. — ESCAP, 28d ago

Building trust in data handling is key to customer loyalty. Data governance is increasingly vital for organisations given regulations and potential fines. This also encompasses data sovereignty issues, where personal data needs to reside within the borders of its origin. — Financial IT, 16d ago

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My work concerns large-scale interoperation of observation and experimentation systems coupled with information systems (before on terrestrial and marine (agro)ecosystems, now in health sciences with a focus on pathogenic agents). This includes the dissemination of life science knowledge and data sharing practices for meta-analyses. I am particularly interested in new methods of data mining and data representation in the form of graphs to analyze heterogeneous data, and in metadata schemas, data annotation, data quality and semantic interoperability topics. I am also a FAIR data management and open science addict and "Data Management Plan with FAIR Compliance" evangelizer. — RDA, 14d ago

Digital technologies promote seamless information sharing and collaboration, which in companies as big as some utilities, enables easy access to vast information pools and fosters knowledge exchange within teams. Real-time communication, document sharing, and collaboration are facilitated, erasing geographical boundaries and expediting decision-making. — Energy Central, 12d ago

Why, you might ask, is Data Governance so critical in today’s data-driven world? Imagine data as the lifeblood of an organization, coursing through its veins to make informed decisions, power operations, and drive innovation. Without proper governance, this lifeblood can become polluted, leak away, or stagnate, leading to dire consequences. Data Governance instills trust. A robust governance framework ensures compliance with regulatory requirements, safeguarding sensitive information and preserving the organization’s reputation. By defining data ownership, establishing data standards, and enforcing data quality controls, organizations can rely on their data as a trusted asset. When data is well-managed, employees spend less time searching for information and more time using it productively. This optimization of data processes directly impacts the bottom line, reducing costs and increasing revenue. — Comparitech, 12d ago

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Gaming applications are committed to maintaining data flow, ensuring a great player experience despite their huge and constant userbase. However, data retrieval blockchain data through RPC and APIs cannot ensure 100% adequate data flow and standard query speed. This causes interruption to the players and, meanwhile, limits the game’s features and capabilities. — Blockchain Deployment and Management Platform | Zeeve, 11d ago

Connected vehicle data, on the other hand, can offer accurate data about vehicular movements. And because vehicles are tracked, not individuals, this data collection offers a level of detachment that’s becoming increasingly essential in our privacy-conscious world. And unlike mobile phone data, connected vehicle data provides insights into both trucks (supply chain) and cars (consumer trends), making the analysis richer and more nuanced. — Silicon Republic, 13d ago

During all early planning discussions, site burdens must be top of mind. When developing a data flow strategy, sponsors should give thought to easy-to-use technologies for frontline teams to manage while optimizing data collection and monitoring. Purpose-driven technologies can help sites by automating continuous data collection, aggregation and review. When considering massive amounts of data via sources like continuous glucose monitors, establishing intuitive tech-enabled solutions that can efficiently process and quality check the data can be critical to lessening bottlenecks in processes necessary to secure the insights needed. — hitconsultant.net, 13d ago

Team teaching is especially valuable when training covers many topics or is intended for a multidisciplinary audience. Instructors may specialize in different topics (e.g. data management vs. reporting guidelines), fields, or study types (e.g. in vitro vs. preclinical vs. clinical biomedical research). Consider sharing course syllabi and materials as open access resources via repositories (e.g. Zenodo, Open Science Framework, PsychArchives) to help make reproducibility and open science courses the norm. — eLife, 11d ago

With such diverse infrastructures, edge traffic originating from IoT, mobile and other latency-sensitive systems can often be processed, routed and managed locally without needing to return to centralized core networks. This distributed architecture improves resiliency by preventing single points of failure and helps scale overall throughput by balancing load locally across edge resources. Application response times and network bandwidth utilization benefit as core infrastructure remains unburdened by real-time data transmissions and processing. — TGDaily, 12d ago

...– The biggest blockers to using video product data effectively are managing data security and protection (48%) and processing vast amounts of data into actionable insights (44%). Perceived challenges vary across regions. — Cynopsis Media, 14d ago

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Data Classification: First and foremost, AI’s ability to facilitate precise data classification lays the foundation for enhanced Data Management. This capability empowers leaders to meticulously organize and categorize their data assets, resulting in improved Data Quality and accessibility. Such streamlined Data Management processes not only fuel more efficient decision-making but also fuel innovation by providing a solid data foundation upon which creative solutions can be built. — DATAVERSITY, 12d ago

...– Patient data is dispersed across various sources, necessitating meticulous planning for aggregation. Standardizing data types and formats, ensuring quality and accuracy, and establishing data governance processes are crucial. Collaboration among organizations is essential for successful data pooling and analysis. — WriteUpCafe.com, 14d ago

Blockchain technology offers several advantages in healthcare, such as data security, interoperability, and real-time access. It enables secure sharing of patient data among authorized parties while maintaining data integrity. — The Nation Newspaper, 12d ago

With the advantages of AI come significant data privacy concerns. Supply chain and procurement divisions are treasure troves of sensitive data, making them attractive targets for cyber-criminals. Safeguards like secure storage systems and encryption protocols are vital. Responsibility, though, also lies in training employees on data handling protocols and limiting access to sensitive information. — supplychainbrain.com, 14d ago

Zatta further elaborated, "AI-based pricing deploys machine learning and data analytics techniques to analyse vast data volumes, determining pricing decisions that maximise profits or serve other objectives, such as boosting market share or customer satisfaction. Data quality and IT architecture are seen as the biggest hindrances to the adoption of AI-based pricing. However, akin to any data project, organisations should start small, with pilot projects. The perceived enormity of big data projects often daunts organisations. This is about evolution in pricing and data, not a revolution. Yet, the essence of time is critical in the AI pricing game, and corporations need to keep pace with their competitors to gain benefits."... — IT Brief UK, 12d ago

From a data analytics and IT perspective, the data lakehouse tackles bad data through native deletions and offset management. It’s also incredibly secure, giving administrators the ability to create “mini-lakehouses” specific to certain users (and restricting access within the platform). — Arcadia, 19d ago

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However, the lack of standardization around collecting, organizing, and harnessing this data may lead to organizations having an unrealistic perception of their practices. While they may believe social determinants of health data is leading to effective interventions, screenings and follow-up initiatives may be inconsistent, the report noted. — RevCycleIntelligence, 17d ago

Unstructured data is a big and growing space, as customers try to gain value from vast stores of backups, files, images and logs. Object storage is an increasingly popular choice for unstructured data because it’s good at storing very large volumes of data with metadata that allows for more information than existing file formats. — ComputerWeekly.com, 14d ago

The practicality of data partnerships becomes a major topic as the hunt for data intensifies. Many artificial intelligence datasets are already derived from internet-scraped data provided by online users, making data partnerships a viable alternative. However, as data value rises, competition for datasets is expected to heat up, creating concerns about institutions’ and individuals’ willingness to disclose their data with AI companies. — DATAQUEST, 18d ago

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...“The Bank of England’s The Future of Post Trade report in 2020 highlighted quite a few pain points we still see across our client base,” Stevenson says. “Data collection as part of the client on-boarding process is not standardised, margin calculations, communications and dispute resolution processes differ across firms, information beyond material economic trade data not exchanged early enough in the trade life cycle and may be inaccurate, incomplete, or not standardised, automated interfaces for data exchange are underdeveloped, and the lack of standardised mechanisms for the collective sharing of data on suspicious activity in real time.”... — e-Forex, 9w ago

But there are ways that both the data access controls can protect data and ensure data access compliance with data regulations. These approaches guarantee that authorised individuals have access to relevant data, while simultaneously monitoring data usage in real-time and implementing appropriate protective measures. Once these comprehensive and inherently compliant methods are established, data access is expedited. — Professional Security, 12w ago

Despite its immense potential, the widespread implementation of genomic sequencing still faces several challenges. One major obstacle is the lack of infrastructure and resources in low-and middle-income countries, which limits their ability to participate in global genomic surveillance efforts. Additionally, access to pathogen genomic sequences can be hampered due to many issues, for example, over rights of publication and credit sharing. (11) Investment in national and regional genomics infrastructure and facilities and ensuring interoperability between data repository systems should be a priority. (12)... — Open Access Government, 7w ago

Cloud technology emerges as a potent medium for data-driven collaboration, overcoming geographical and time constraints. Cloud-based platforms serve as repositories of shared knowledge, offering real-time access to information and facilitating seamless data sharing among teams. — Field Service News › The world's leading resource for field service professionals, 9w ago

...1. Data Quality and Accessibility: AI relies on high-quality data. Enterprises often struggle with data silos, inconsistent data formats, and data privacy issues that can hinder AI implementation. — Analytics Insight, 12w ago

Effective collaboration between data governance teams and privacy teams can significantly impact the ROI of privacy initiatives. Data governance teams establish and enforce policies, procedures, and standards for data handling, which directly supports privacy compliance. By ensuring accurate data classification, adequate data retention and robust access controls, good data governance can reduce the risk of privacy breaches and regulatory fines. Furthermore, data governance facilitates streamlined data inventory and mapping, aiding privacy teams in identifying sensitive information and implementing targeted protection measures. Clear documentation of data flows and sharing practices enhances privacy impact assessments and simplifies compliance reporting. — onetrust.com, 4w ago

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Third, data quality is pivotal, and regular updates and continuous record-keeping are indispensable for future robust forecasting. This is where data governance comes into play. Effective data governance policies that ensure the quality and accessibility of public data are integral to this process, enabling AI/ML algorithms to work their magic. — medicalxpress.com, 20d ago

Postdoctoral researcher Tom Aben gave a short presentation about the VIA AUGUSTA project he is involved in, which is about organizing the sharing of data in interorganizational relationships to support the energy transition. In the presentation, Tom addressed several challenges, such as data not being used that much yet, organizations largely working reactively, different stakeholders having conflicting goals, information asymmetries, laws preventing the sharing of data, information systems of different suppliers not matching, et cetera. The importance of governance of interorganizational relationships was stressed, including both contractual governance and relationship governance revolving around transparency, trust, and setting common goals. Becoming smarter is not simply introducing digital technologies, it requires interorganizational collaboration because organizations cannot operate in isolation. — Tilburg University, 18d ago

The power of metadata. With world-leading metadata capabilities, Mediaflux improves data discovery and fosters collaboration and knowledge sharing. The platform’s global distributed access ensures data can be retrieved from any location, facilitating international collaboration among data-intensive organizations such as research facilities, universities, entertainment studios, and government institutions. — Datanami, 18d ago

At the heart of this transformation lies the strategic management of data. Logical data management, characterized by a systematic, structured approach to handling information, plays a pivotal role in enabling governments and businesses to make informed decisions. It ensures that data is not only accessible but also meaningful, regardless of its source or format. Nationwide initiatives such as Aadhar and COWIN36 would benefit greatly from a logical data management approach. After all, national records and identities are systems that need to extract and unify information from myriad sources, regardless of formats and data types. — CXOToday.com - Technology News, Business Technology News, Information Technology News, Tech News India, 18d ago

The first elevated risk is related to data exposure. With employees working remotely and business processes realigned to support telework, security controls may degrade, leaving protected information susceptible to exposure. Organizations must identify and protect private or sensitive data, including (but not limited to) financial data, customer information, employee data and intellectual property. Either customer information or employee data may include personally identifiable information (PII) that will often require additional protections due to privacy requirements at the state, national or super-national level. — Security Boulevard, 18d ago

Traditional cloud computing models are limited in their ability to handle substantial real-time data volumes. Bandwidth limitations can cause transmission delays for critical patient information, hindering timely analysis and prompt treatment. Plus, storing sensitive medical data on remote cloud servers can generate concerns about data protection. — hitconsultant.net, 19d ago

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A data exchange platform enables the transfer and provision of data with all the necessary functions such as protection, compliance, and traceability of transactions. Both data recipients and data providers need a protected and docile environment to take advantage of their data's prospects and include the player that adequately meets their data needs. As the number of data increases, a data market can optimize data acquisition and regulate the flow of data. With the rapid development of the Internet of Things (IoT) and other innovative technologies, a data market can add value to data through insightful data partnerships with the expanded ecosystem. All of these factors contribute to the growth opportunities for the data exchange platform services market over the planned timeframe. A data exchange platform is the most advanced environment in which companies can share, obtain, exchange, assign, and utilize data and/or organize data ecosystems to build strong data partnerships. Data exchange platforms prescribe legal, technological, security, and consent requirements to support current and future data policies and frameworks. As organizations in both the public and private sectors recognize the importance of data as a key strategic element in achieving market goals such as creating new revenue streams or developing productivity, investment in services is increasing, which is expected to increase in the global data exchange platform services market Forecast period.In November 2020, Harbr Group Limited had focused on offering a platform for enterprise data sharing by investing USD 38.5 million to empower businesses by sharing big data for data sharing. — openPR.com, 29d ago

Cloud storage has transformed data accessibility and security by providing users with unprecedented convenience, scalability, and mobility. It has changed the way we store and access data, making it available from any location with an internet connection. Furthermore, cloud storage platforms prioritize data security through redundancy, encryption, and access controls, preventing unauthorized access and preserving data integrity. Hence, cloud storage will remain an important tool for consumers and organizations as the digital landscape evolves, revolutionizing how we store, access, and safeguard data. — CXOToday.com - Technology News, Business Technology News, Information Technology News, Tech News India, 12w ago

As data streams converge, challenges arise. Siloed data repositories that once sufficed now hinder the flow of insights. The IT department faces dismantling these data barriers, forging connections that allow information to traverse service and product realms. Ensuring data quality, consistency, and security becomes paramount. — Field Service News › The world's leading resource for field service professionals, 9w ago

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