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Data Management, Analytics & AI

  • Glean Empowers Enterprises to Deliver AI Agents at Scale, Built on Their Existing Data Foundation

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    Stephen Catanzano

    What does it mean to be ready for AI agents? Having a full understanding of and access to all of your data, with strong data governance, security, and data quality, is critical for AI success. The challenge to effectively empower employees and the business to utilize the mountain of unstructured data plagues many organizations, and now with the critical need to use this data as a foundation for AI use cases across the business, there is a hyperfocus on data readiness for AI. Glean, a leader in enterprise search and AI agents, is quickly enabling its customers to take advantage of AI agents for every employee and line of business. The strategy of implementing enterprise search to help build the data foundation for AI is one that organizations should strongly consider.

    To learn more, download the free brief, Glean Empowers Enterprises to Deliver AI Agents at Scale, Built on Their Existing Data Foundation.

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  • Qlik, an Analytics and Data Integration Leader, Is Quickly Empowering AI Agents for Its Customers and Addressing Core Data Foundation Needs

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    Stephen Catanzano

    As organizations rush to understand and deploy AI agents, they are quickly realizing the need for an AI-powered data platform that can help prepare their data for AI and analytics empowerment. The same data foundation is required for both, making the transition from an analytic platform to an AI platform a natural one. The same data quality, governance, and trust needed for analytics are necessary for generative AI and AI agents. With its new agent-building tools and data lake capabilities, including Iceberg, Qlik demonstrates thought leadership as it continues to meet market and customer demands.

    To learn more, download the free brief, Qlik, an Analytics and Data Integration Leader, Is Quickly Empowering AI Agents for Its Customers and Addressing Core Data Foundation Needs.

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  • Reinventing Data Loss Prevention: Adapting Data Security to the Generative AI Era

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    Todd Thiemann

    Enterprises need to provide access to sensitive data while controlling against the unauthorized disclosure of that information from inadvertent leakage, insider threats, and outside attacks targeting data. Work-from-home and bring-your-own-device initiatives pose increased data loss prevention (DLP) challenges, and generative AI (GenAI) has opened new avenues for data leakage. Although DLP is a top investment category when it comes to data security, enterprises continue to struggle to classify data and control against data loss. Enterprise Strategy Group recently surveyed IT and cybersecurity professionals to gain insights into these trends.

    To learn more, download the free infographic, Reinventing Data Loss Prevention: Adapting Data Security to the Generative AI Era.

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  • Reinventing Data Loss Prevention: Adapting Data Security to the Generative AI Era

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    Todd Thiemann

    Enterprises need to provide access to sensitive data while controlling against the unauthorized disclosure of that information from inadvertent leakage, insider threats, and outside attacks targeting data. Work-from-home and bring-your-own-device initiatives pose increased DLP challenges, and new collaboration platforms and GenAI applications have opened new avenues for data leakage. Additionally, the proliferation of cloud services poses threats for data exfiltration, while intellectual property and trade secrets take new forms that do not lend themselves to conventional DLP solutions.

    Although DLP is a top investment category when it comes to data security, enterprises continue to struggle to classify data and control against data loss. Whether an enterprise DLP solution or DLP functionality within another security technology, current offerings generate considerable false positive alerts that distract teams that must evaluate and respond to such alerts. Existing approaches relying on regular expression (regex) rules can be brittle and require considerable maintenance, while current DLP solutions frequently encounter scaling and performance issues. Furthermore, complex data types like software code or health sciences data can be difficult to categorize.

    To gain insights into these trends, Enterprise Strategy Group surveyed 370 IT and cybersecurity professionals in North America (U.S. and Canada) involved with identity security technologies and processes.

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  • Industries Vary Navigation Routes Through Data Readiness Requirements

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    Stephen Catanzano

    All major industries recognize the value of data readiness for successful AI use—after all, AI is only as effective as the data fed into its models. However, recent research by Enterprise Strategy Group, now part of Omdia, discovered that industries vary in their approaches to data readiness, showing differences in influencing factors, challenges encountered, scaling strategies, and success metrics.

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  • Data Complexity Inhibits Large Midmarket Organizations Along Their AI Journey

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    Stephen Catanzano

    Although teams across industries are eager to unlock the potential of AI within their businesses and operations, they regularly run into roadblocks in integration complexity, data source utilization, and data quality and trust. Recent research by Enterprise Strategy Group, now part of Omdia, revealed that some of these issues might be correlated to organizational size as they are often more acute for certain groups.

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  • Emerging Databases Prompt Extensive Due Diligence

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    Stephen Catanzano

    As important pieces of infrastructure supporting analytics and AI initiatives, databases are seeing renewed attention by IT teams today. Recent research by Informa TechTarget’s Enterprise Strategy Group found that organizations are considering a wide range of factors for emerging databases, analytics databases, and data models.

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  • Organizations Assemble GenAI Use Cases for Databases

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    Stephen Catanzano

    Organizations continue to race ahead with exciting GenAI use cases, and database teams are busily modifying and tuning the core infrastructure these efforts rely on. Recent research by Informa TechTarget’s Enterprise Strategy Group investigated today’s top GenAI use cases that are proliferating across a range of database types.

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  • Pre- and Post-launch AI Assessments Surge as Organizations Assess Fit and Function

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    Mike Leone

    Organizations investing in AI are implementing comprehensive value assessments both during deployment and after launch. While this strategy strives to ensure AI solutions continue to align to business objectives and deliver on their promised value, the resulting sprawl of performance and value metrics may be overcomplicating the process of finding full-stack AI solutions. Recent research by Informa TechTarget’s Enterprise Strategy Group revealed that teams may have trouble finding satisfactory AI platforms as their requirements sprawl.

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  • AI Investments Continue as Organizations Chase Benefits and Address Challenges

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    Mike Leone

    Organizations are continuing a flurry of investments in AI, often spurred by ongoing enthusiasm around generative AI initiatives and their potential to radically improve operational efficiency and productivity. Recent research by Informa TechTarget’s Enterprise Strategy Group revealed that teams will sometimes find themselves paradoxically having to apply some of these investments to specific issues introduced by AI deployment.

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  • Major Industries Set Their Sights on Responsible AI

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    Mike Leone

    Against a backdrop of AI excitement and continuous rollouts of new large language models, organizations are contending with the critical balance of innovation and responsible AI, including strategies for data privacy, bias mitigation, compliance, ethical usage, and more. Recent research by Informa TechTarget’s Enterprise Strategy Group revealed that, although most organizations are making progress in ensuring AI is used ethically and responsibly, different industries vary widely on their approaches.

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  • Organizations Continue Their Journey Toward Holistic, Proactive Responsible AI

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    Mike Leone

    Even moderate AI usage within an organization warrants a responsible AI strategy supported by technical stakeholders and foundational policies, but recent research by Informa TechTarget’s Enterprise Strategy Group found that certain teams generally have more comprehensive practices for responsible AI than others. Using those organizations as a model for responsible AI development can help other teams get their operations in order as they roll out more initiatives that expose the business to greater risk in the event of unethical or noncompliant AI use.

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