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Data Management, Analytics & AI

  • 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.

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

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    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.

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  • The Strategic and Evolving Role of Data Governance

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    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

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    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.

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  • 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.

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  • Data Platforms: The Path to Achieving Data-driven Empowerment

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    Stephen Catanzano

    Data management teams require new capabilities to effectively harness and extract value from expansive volumes of data culled from a growing number of sources. The goal is to reach a maturity level where insights are delivered in real time to keep pace with the operational needs of the business, innovate faster, and build competitive advantages.

    Learn more about these trends with the infographic, Data Platforms: The Path to Achieving Data-driven Empowerment.

    Download Now

  • Teradata and Microsoft Collaborate To Bring Advance AI and ML at Scale to Enterprises

    ·

    Stephen Catanzano

    Teradata and Microsoft have long played a major role in the advancement of data and analytics. An example of this is their new collaboration announcement to bring Teradata VantageCloud Lake and ClearScape analytics to the Microsoft Azure cloud. This partnership aims to provide enterprises with the ability to leverage AI and machine learning (ML) at scale across their organizations to extract value from data, which helps to empower data-driven decision-making and may lead to faster future innovations.

    A recent research survey by TechTarget’s Enterprise Strategy Group found that organizations widely recognize the value of data analysis. When asked how they would assess the impact of data analysis for decision-making or as a competitive advantage, an amazing 99% of respondents said the impact was positive (66%) or extremely positive (33%).[1]

    Picture1-4

    The integration of Teradata’s cloud-native platform and ClearScape Analytics with Microsoft Azure services, including Azure Machine Learning, offers customers enhanced analytics capabilities and the opportunity to explore generative AI.

    We have seen interest in generative AI gaining significant traction across industries. If fact, TechTarget has seen a 2,699% growth in generative AI editorial and content consumption over the past 6 months.[2] Recognizing this growing demand, Teradata and Microsoft are leveraging their expertise and innovative technologies to help organizations harness the power of generative AI to drive improvements in business performance and customer experiences.

    There are two important solutions that Teradata and Microsoft are bringing to market. The first is Teradata’s VantageCloud Lake on Azure, which serves as a foundation for delivering AI and ML. In the recent Enterprise Strategy Group research survey, organizations reported that they overwhelmingly procure their data repository tools in public clouds like Azure.[3] VantageCloud Lake on Azure provides a modern cloud-native architecture that separates compute and storage to offer independent, elastic, and multicluster compute capabilities against Azure Data Lake Storage. This combined solution enables Azure customers to execute a wide range of analytic workloads, including AI/ML, with harmonized data across their organizations. The offering also includes an exclusive high-availability feature that enhances cloud availability and optimized system sizes for improved overall uptime.

    The second solution is ClearScape Analytics, coupled with Azure Machine Learning. This combination of technologies is designed to deliver analytics capabilities for AI, ML, and generative AI use cases. Teradata’s ClearScape Analytics and Microsoft Azure ML provide end-to-end analytic pipelines, encompassing data preparation, model training, and operationalization at scale.

    When combining data from VantageCloud Lake with ClearScape Analytics and Azure ML, businesses can activate analytics across the enterprise and explore new generative AI use cases. This collaboration between Teradata and Microsoft demonstrates their continued commitment to advancing data and analytics and empowering organizations to embrace cutting-edge technologies to drive innovation at an enterprise scale.

    [1] Source: Enterprise Strategy Group Research Report, Data Platforms: The Path to Achieving Data-driven Empowerment, June 2023.

    [2] Source: Core Topic Activity Growth Report (Worldwide, TTGT only, All Segments, July ’22-Dec ’22 vs. Jan ’23-Jun ’23).

    [3] Source: Enterprise Strategy Group Research Report, Data Platforms: The Path to Achieving Data-driven Empowerment, June 2023.

  • Business Drivers for Adopting Integrated Partner Solutions

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    Doug Cahill

    Increased IT complexity and the need to focus resources on strategic initiatives are pushing IT leaders to embrace product options that support technology convergence and platform consolidation. Integrated solutions from multiple vendors are a viable option to achieve those goals. Research from TechTarget’s Enterprise Strategy Group found that boosting IT team productivity is the leading business driver for buying integrated solutions and that business expectations are being fully met or exceeded in a majority of cases.

    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.

  • Challenges Across the Buyer’s Journey for Integrated Partner Solutions

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    Doug Cahill

    Various challenges complicate the process of researching, evaluating, purchasing, and deploying integrated solutions from strategic vendor partners. Research by TechTarget’s Enterprise Strategy Group found that current users encounter unique challenges in each stage—and for each stage, only about one-fifth of survey respondents said they didn’t experience any challenges. The often self-guided education that occurs during the research stage of the buyer’s journey is critical not only for solution selection, but also for project success and follow-on investments with the providers of the integrated solution. Buyers need to push solution vendors to address common challenges, while partnering vendors must take a unified approach in doing so.

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