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Data Management, Analytics & AI

  • Navigating the Evolving AI Infrastructure Landscape

    ·

    Mike Leone

    As AI continues its meteoric rise into business and IT environments, organizations are rapidly assembling or accelerating strategies to support AI technologies across every applicable area. While many organizations are consistent in their efforts to build AI strategies, the components and direction of those strategies often vary. TechTarget’s Enterprise Strategy Group recently surveyed data and IT professionals responsible for the infrastructure supporting AI initiatives at their organization to gain insights into these trends.

    To learn more about these trends, download the free infographic, Navigating the Evolving AI Infrastructure Landscape.

    Download Now

  • Navigating the Evolving AI Infrastructure Landscape

    ·

    Mike Leone

    As AI continues its meteoric rise into business and IT environments, organizations are rapidly assembling or accelerating strategies to support AI technologies across every applicable area. Unlike niche technologies that impact only certain processes or personnel, AI has wide-ranging potential to transform entire businesses, IT environments, and associated teams. In turn, AI strategies must be multi-pronged efforts that properly align business objectives with AI initiatives and expectations, which requires thorough participation from stakeholders across the organization. The underlying infrastructure and other supportive elements must be fully capable of supporting that tandem strategy.

    While many organizations are consistent in their efforts to build AI strategies, the components and direction of those strategies often vary. To assess the evolving AI landscape and the infrastructure that supports it, TechTarget’s Enterprise Strategy Group surveyed 375 data and IT professionals in North America (US and Canada) responsible for strategizing, evaluating, purchasing, and/or managing infrastructure specifically supporting AI initiatives for their organization. This study sought to answer the following questions:

    • What are the primary business objectives for implementing AI? How long does it take for organizations to start seeing value from AI initiatives?
    • What are the top challenges organizations encounter when implementing AI?
    • What individuals or teams influence decision making related to infrastructure used to support AI initiatives? Which of these has the most influence on final decisions?
    • How are organizations planning to address skills gaps related to the selection, implementation, and management of infrastructure supporting AI initiatives?
    • In which physical locations do organizations primarily deploy their AI infrastructure? What are the top factors that influence the choice of these locations? Are AI environments mostly centralized, mostly decentralized, or an even mix of both?
    • What capabilities of AI infrastructure are most important?
    • Are organizations using internal resources, third-party resources, or both to manage their AI infrastructure?
    • How important is sustainability and environmental responsibility when selecting AI infrastructure? How important is a vendor’s stance on these factors when making purchase decisions for AI infrastructure?
    • What types of data do organizations use to build and train AI models and algorithms? What steps do organizations take to ensure accuracy in the data used for building and training these models?
    • How do organizations handle the movement of the large amounts of data required to support AI initiatives? What challenges are involved with this process?
    • How are organizations using synthetic and third-party data to support AI model training?
    • How are organizations using generative AI (GenAI)? What challenges are they encountering?
    • To what extent are developers leveraging AI infrastructure resources? How do developers access these resources?
    • How do organizations measure the success and effectiveness of AI initiatives?
    • What is AI’s impact on employee productivity, processes, workflows, competitiveness, and other factors?

    Survey participants represented a wide range of industries, including financial, manufacturing, retail/wholesale, and healthcare, among others. For more details, please see the Research Methodology and Respondent Demographics sections of this report.

    Already an Enterprise Strategy Group client? Log in to read the full report.
    If you are not yet a Subscription Client but would like to learn more about accessing this report, please contact us.

  • Cloud Data Protection Strategies at a Crossroads

    ·

    Christophe Bertrand

    The broad adoption of public cloud services and containers as sources and repositories of business-critical data puts the onus on data owners to deliver on data protection SLAs for cloud-resident and container-based applications and data. As vendors and the cloud ecosystem evolve and add as-a-service consumption options, end-users are making incorrect comparisons and assumptions, leading to lasting challenges and a market at a crossroads.

    Learn more about these trends with this free infographic.

    Download Now

  • Beyond the GenAI Hype: Real-world Investments, Use Cases, and Concerns

    ·

    Mike Leone

    Generative artificial intelligence (GenAI) recently stormed the market and mindshare of decision makers across industries and major geographic markets. Business leaders see a massive opportunity to positively impact operations and customer strategies with GenAI, but its adoption and use across all business units carry a fair share of trepidation.

    Learn more about these trends with the infographic, Beyond the GenAI Hype: Real-world Investments, Use Cases, and Concerns.

    Download Now

  • Cloud Data Protection Strategies at a Crossroads

    ·

    Christophe Bertrand

    Research Objectives

    The broad adoption of public cloud services and containers as sources and repositories of business-critical data puts the onus on data owners to deliver on data protection SLAs for cloud-resident and container-based applications and data. Users are confused about the data protection levels that public cloud and Kubernetes environments deliver and about the changing protection options (DIY in the cloud, cloud-native third-party solutions, hyperscalers’ built-in features, as-a-service, etc.). As vendors and the cloud ecosystem evolve and add as-a-service consumption options, end-users are making incorrect comparisons and assumptions as well as failing to select the key data protection capabilities they need to maximize their cloud technology investments. This confusion leads to lasting challenges, and the market is now at a crossroads.

    To assess the state of cloud-based data protection and the as-a-service market (e.g., in cloud/to the cloud, BaaS, and DRaaS), TechTarget’s Enterprise Strategy Group (ESG) surveyed 397 IT professionals in North America (US and Canada) familiar with and/or responsible for data protection technology decisions for their organization, specifically around data protection and production technologies that may leverage cloud services as part of the solution. This study sought to answer the following questions:

    • How do organizations define backup-as-a-service (BaaS) and disaster recovery-as-a-service (DRaaS)?
    • What is the adoption status of BaaS, DRaaS, and cloud backup/disaster recovery targets?
    • What groups/roles within organizations are involved with the evaluation of and influence the purchase of public cloud-based data protection solutions? Which group/role typically makes the final purchase decision?
    • How many times in the last 12 months have organizations had to recover data from on-premises and/or public cloud environments? What percentage was recovered on average in those cases?
    • What were the reasons for data recovery efforts in the last 12 months?
    • Would organizations consider a public cloud-based data protection solution that includes an on-premises cache or storage for local recovery to improve data recovery SLAs (e.g., RPO)?
    • What approaches currently protect applications/workloads/data in public cloud infrastructure services?
    • What types of data protection technologies are used in these approaches, and which assets are protected?
    • How is critical public cloud-based unstructured data protected, and what are acceptable recovery times?
    • What is the impact on teams of the daily management and maintenance of public cloud data?
    • How many full-time staff are allotted for data protection objectives associated with cloud data?
    • What methods do organizations use to protect data within virtual machines on public cloud infrastructure?
    • What are organizations’ preferred approach to protecting multiple unique public CSP environments?
    • How do organizations estimate the costs of their cloud backups and recoveries for hyperscalers?
    • What approaches do organizations take to ensure cost-efficient data tiering for the data protection storage supporting their public cloud infrastructure-resident applications?
    • Does organizations’ backup software handle the appropriate tiering of data written to object storage?
    • How important is it to have a container backup and recovery management solution that works across multiple disparate public cloud infrastructure services going forward?
    • Do organizations’ container backup schemas integrate with their current data protection environment?

    Survey participants represented a wide range of industries, including financial, manufacturing, retail/wholesale, and healthcare, among others. For more details, please see the Research Methodology and Respondent Demographics sections of this report.

    (more…)

  • Bridging Innovation and Code: How Generative AI Is Transforming the Application Landscape

    ·

    Paul Nashawaty

    Application developers are challenged with efficiently creating innovative solutions while managing time constraints, which can be mitigated by the transformative impacts of generative artificial intelligence (AI) streamlining code generation and accelerating development processes. Organizations have integrated generative AI (GenAI) into their operational setup to accelerate code creation, refine code structures, elevate code quality, and deliver personalized customer experiences. By harnessing GenAI, application developers tackle issues by capitalizing on the technology’s ability to automate tasks, drive creativity, and deliver innovative solutions.

    (more…)

  • The Strategic and Evolving Role of Data Governance

    ·

    Christophe Bertrand

    For organizations on a digital transformation journey, sound data governance practices must play a strategic role. As the amount of data and value of that data to the business continue to increase, so too does the importance of managing its availability, usability, integrity, and security.

    Learn more about these trends with the infographic, The Strategic and Evolving Role of Data Governance.

    Download Now

  • Activity Trends: What’s Driving Growth Across The TechTarget & BrightTALK Network

    ·

    Enterprise Strategy Group

    About This Report

    This report covers trending areas of interest across 240+ IT markets over the last 6 months (January 2023 – June 2023) in five (5) regions across the TechTarget & BrightTALK network: WW, NA, EMEA, APAC, LATAM.

    • Top 20 markets driving activity
      • Represents the top 20 broad technology markets driving the most activity in the last 6 months. Activity data can help to show where audience research is growing or declining and therefore help reinforce which markets are hot or declining.
    • 25 topic areas on the rise
      • Shows the top 25 granular topics growing the most across the TechTarget network in the last 6 months. This gives insight into the content areas that are on the rise right now.

    Discover what’s trending on our network, which you can leverage to engage IT buyers in market now and improve marketing and sales effectiveness.

    Already an Enterprise Strategy Group client? Log in to read the full report.
    If you are not yet a Subscription Client but would like to learn more about accessing this report, please contact us.

  • Beyond the GenAI Hype: Real-world Investments, Use Cases, and Concerns

    ·

    Mike Leone

    Research Objectives

    • Determine the current market state of generative AI, including adoption and budget strategies.
    • Identify current and planned GenAI use cases and prioritization across organizations.
    • Understand key challenge areas with GenAI and investment requirements to address them.
    • Investigate key areas of application and focus for GenAI technologies, including cybersecurity, application development, analytics, and customer experience. (more…)

  • Beyond the GenAI Hype: Real-world Investments, Use Cases, and Concerns

    ·

    Mike Leone

    Research Objectives

    While AI in general was already assimilating into the everyday business and IT lexicon thanks to ongoing AI and analytics strategies and initiatives, GenAI recently stormed the market and mindshare of decision makers across industries and major geographic markets. Business leaders see a massive opportunity to positively impact operations and customer strategies with GenAI, but its adoption and use across all business units carry a fair share of trepidation.

    Most organizations are aware of GenAI, and a rising percentage are currently formulating strategies to both harness the technology’s benefits and control its use to prevent data quality issues and information leaks. To assess the state of GenAI strategies and plans, TechTarget’s Enterprise Strategy Group surveyed 670 IT professionals and business decision makers in North America (65%), EMEA (18%), APAC (16%), and LATAM (2%) involved with generative AI initiatives in their organization. This study sought to answer the following questions:

    • What is the status of GenAI initiatives within organizations?
    • How are organizations using, or planning use, large language models (LLMs) to support GenAI initiatives?
    • Are organizations allocating, or planning to allocate, budget to support GenAI initiatives? If so, what is the percentage of IT budgets allocated to GenAI?
    • In which lines of business are organizations currently applying GenAI? Moving forward, which of these areas will benefit most from the use of GenAI?
    • Which teams or stakeholders actively contribute to shaping GenAI initiatives in organizations?
    • What technology investments are needed to support GenAI initiatives?
    • What do organizations identify as the primary benefits of using GenAI in their environments?
    • What are the most prioritized use cases for GenAI, particularly in environments where the technology is applied across multiple areas?
    • What are the biggest challenges organizations face in GenAI implementations?
    • In which areas do organizations feel they need to invest (time and/or money) to support the use of GenAI?
    • What type of third parties do organizations currently, or plan to, work with to support GenAI initiatives?
    • Are organizations more or less likely to consider vendors that incorporate GenAI capabilities as part of their products or services?
    • How much more, if at all, are organizations willing to pay for a product or service that uses GenAI versus a comparable product or service that does not use GenAI?
    • What types of information or media would help organizations assess GenAI?
    • For which application development use cases are organizations using, or planning to use, GenAI? What about use cases for security and customer experience (CX)? Where will investments be made?
    • How do, or will, organizations ensure the security and privacy of data used in GenAI models?
    • For which security use cases are organizations using, or planning to use, GenAI?
    • Which areas of the analytics lifecycle will benefit most from the use of GenAI?

    Survey participants represented a wide range of industries, including financial, manufacturing, retail/wholesale, and healthcare, among others. For more details, please see the Research Methodology and Respondent Demographics sections of this report.

    (more…)

  • Cloud Data Protection Strategies at a Crossroads

    ·

    Christophe Bertrand

    Research Objectives

    • Assess the state of cloud-based data protection and the as-a-service market (i.e., in cloud/to the cloud, BaaS, and DRaaS).
    • Explore end-user challenges and highlight requirements.
    • Establish the role of key decision makers and personas in the buying cycle.
    • Assess the use and impact of cloud technologies for data protection. (more…)

  • The Strategic and Evolving Role of Data Governance

    ·

    Christophe Bertrand

    Research Objectives

    For organizations well along on the path of their digital transformation journey, sound data governance practices are playing a strategic role. As the amount of data and value of that data to the business continue to increase, so too does the importance of managing its availability, usability, integrity, and security. Data governance is a loosely applied term in the data management space. As ecosystems evolve and become more distributed, end-users are struggling to connect the dots between the important elements of data governance like data classification, data indexing, data placement, e-discovery, and compliance.

    In order to understand the benefits and challenges of data governance initiatives, establish the current state of deployments, identify gaps, and highlight future expectations, TechTarget’s Enterprise Strategy Group (ESG) surveyed 376 IT and business decision makers currently responsible for the governance technologies, processes, and programs used to manage their organizations’ data.

    This study sought to answer the following questions:

    • What is the approximate total volume of data organizations have stored on their corporate servers and storage systems? What is the approximate volume of unstructured data?
    • At approximately what rate do organizations believe their total volume of data is growing annually? What technology features/capabilities do organizations use to manage overall data growth?
    • What percentage of organizations’ total data contains personally identifiable information (PII) or other sensitive data?
    • In terms of data repositories, how distributed is the total volume of data for the average organization? How does this change, if at all, for PII and other sensitive information?
    • For approximately how long have organizations had their data governance practices in place?
    • How have stakeholder roles and levels of corporate involvement for organizations’ data governance initiatives evolved over the last two years?
    • Have organizations implemented or considered implementing a data governance team?
    • What are the areas of greatest concern for organizations when it comes to potential non-compliance with data governance managed regulations?
    • What is the biggest challenge for organizations when it comes to implementing and managing data governance initiatives?
    • Generally speaking, how has the use of public cloud services impacted organizations’ abilities to manage and execute data governance programs, processes, and procedures? Specifically, what SaaS application types present the biggest challenges to organizations in terms of implementing or extending data governance practices?
    • Do organizations currently leverage any data classification tools or processes? For those that do, is data indexing and classification done at the metadata or content level?
    • What are the most significant business drivers underlying organizations’ data governance programs?
    • Have organizations experienced a cybersecurity incident that impacted their ability to meet/adhere to data governance requirements in the last 12 months?

    Survey participants represented a wide range of industries including manufacturing, technology, financial services, and retail/wholesale. For more details, please see the Research Methodology and Respondent Demographics sections of this report.

    (more…)