Data Management, Analytics & AI

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

August 18, 2023

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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

August 14, 2023

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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

August 14, 2023

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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

July 26, 2023

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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.

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

July 17, 2023

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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]

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

July 7, 2023

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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.

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Challenges Across the Buyer’s Journey for Integrated Partner Solutions

July 7, 2023

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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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Data Platforms: The Path to Achieving Data-driven Empowerment

July 7, 2023

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Stephen Catanzano
Research Objectives

To assess the current and future landscape for data platforms, including supportive technologies and processes, Enterprise Strategy Group surveyed 354 IT professionals in North America (US and Canada) involved with decisions for data and analytics, modern tooling, data technologies, and data-centric processes in their organization.

This study sought to answer the following questions:
- How many tools or services do organizations leverage across all stages of their data platforms, from initial data collection to data visualization?
- What are the key drivers behind modern data platform strategies?
- Which areas of data platforms are most important? Which are most challenging?
- How effective is the interoperability between different vendors contributing to data platform components?
- What is the average delivery time for getting decision-making data to stakeholders? How will this change over the next 24 months?
- How effectively are organizations delivering the right data to the right users at the right time?
- What role do cloud services play in supporting modern data platform strategies? What are the drivers behind the use of cloud services for these platforms?
- Who are the data stakeholders in organizations? To what extent is the number of data stakeholders growing?
- From how many sources do organizations collect data to support their modern data platform initiatives? What types of data sources are used?
- What are the most important expected outcomes from implementing data integration tools or services?
- What types of data repositories are organizations currently using to store data for analysis and processing? What types of data repositories will they be using in 24 months?
- What is the current and future adoption status for data organization tools and services such as data preparation, quality assurance, orchestration, classification, and cataloging?
- What is the current and future data analysis and visualization tool adoption status?
- How does data analysis impact organizations’ decision-making abilities or competitive advantage?
- How will spending on data platform technologies change over the 12 months? Which technologies will represent the most significant investments?
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Data Protection

July 6, 2023

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linchpin
Data Protection

Data protection has never been as challenging as it is today, thanks to the continuing adoption of virtualization and private cloud architectures, the specter of ransomware, the unfettered growth in unstructured data, increased compliance regulations, consolidation initiatives, and aggressive service-level agreements.
Data protection is evolving to adjust to new IT infrastructure driven by digital transformation, cyber threats, legal mandates, and the risk of data loss.

Data protection software and services are essential for preventing downtime, ensuring IT continuity, and facilitating system and data recovery. They are also crucial in the fight against ransomware.

Enterprise Strategy Group’s Data Protection analysts and demand-side research cover the technologies and services available, both in the data center and in the cloud, including:

Backup

Business continuity/Disaster recovery

Cloud data protection

Compliance

Cyber resilience/Ransomware

Data privacy regulations

Data protection

Data replication

DRaaS

E-discovery

Intelligent data management
Research Brief

How Commercial Discovery and Classification Tools Can Ease Data Resilience Skill Shortage Impacts

While even the best-resourced IT organization can perpetually feel like they need more help on hand to contend with the intense cybersecurity threat landscape, some organizations are solving the situation by deploying third-party tools to supersede internally developed tools. Internal resource constraints are forcing firms to find novel ways of stretching thin pools of expertise […]

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Analysts Covering Data Protection

Jon Brown

Senior Analyst, Data Protection, Ops & Sustainability

Areas of Expertise

AIOps

Backup

Business Continuity/Disaster Recovery

Data Protection

DRaaS

FinOps

Read more
Recent Data Protection Insights

Research Brief | March 26, 2025

IBM Announces IBM Storage Ceph as a Service

Scott Sinclair

Cloud & IT Operations

Research Brief | February 28, 2025

HPE ProLiant Compute Gen12 Enhances Security and Efficiency for the AI Era

Scott Sinclair

Cloud & IT Operations

Research Brief | February 25, 2025

Cloud Business Resilience With Commvault Cloud Rewind

Scott Sinclair

Cloud & IT Operations

Research Brief | February 25, 2025

IT Complexity Drives Increased 2025 Spending in Targeted Areas

Scott Sinclair
Emily Marsh
Christian Perry

Infrastructure
Data Platforms

July 6, 2023

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linchpin
Data Platforms

A significant shift toward the use of data for real-time decision-making is becoming a reality for all sizes of companies, as technology in this market has innovated and matured over the past few years.
Digital transformation and digitizing data have brought things like data governance, stewardship, observability, and ITOps into the spotlight.

High-quality data is a prerequisite for reliable and trusted analytics and AI results. Analytics initiatives won’t pay off as expected unless the underlying data is aligned with them.

Enterprise Strategy Group’s Data Platforms analysts and demand-side research cover every aspect of an organization’s data platform strategy and technology framework, including:

Data collection

Data integration

Data management

Data observability

Data platforms

Data preparation

Data quality

Data search/log

Data streaming

Data transformation

Data virtualization

Data warehousing

Databases

DataOps & data mesh
Research Report

Rethinking Database Requirements in the Age of AI

The growing use of GenAI is changing how businesses manage operations and make decisions. Databases are becoming the core infrastructure of AI-based projects, providing the foundation for use cases that require efficiency and accuracy. It’s now a necessity to use cutting-edge tools such as vector and RAG for processing AI data, and organizations are seeking […]

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Analysts Covering Data Platforms

Stephen Catanzano

Senior Analyst, Data Management

Areas of Expertise

AI Tools for Data

Data Foundations for AI

Data Lakes & Warehouses

Data Management

Data Observability

Data Platforms

Read more
Recent Data Management Insights

Research Brief | April 30, 2025

Industries Vary Navigation Routes Through Data Readiness Requirements

Stephen Catanzano
Emily Marsh
Christian Perry

Data Analytics & Artificial Intelligence

Research Brief | April 25, 2025

Data Complexity Inhibits Large Midmarket Organizations Along Their AI Journey

Stephen Catanzano
Emily Marsh
Christian Perry

Data Management

Research Brief | March 17, 2025

Emerging Databases Prompt Extensive Due Diligence

Stephen Catanzano
Mike Leone
Emily Marsh
Christian Perry

Data Analytics & Artificial Intelligence

Research Brief | March 17, 2025

Organizations Assemble GenAI Use Cases for Databases

Stephen Catanzano
Mike Leone
Emily Marsh
Christian Perry

Data Management
Data Analytics & Artificial Intelligence

July 6, 2023

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linchpin
Data Analytics & Artificial Intelligence

To find more value from constantly growing data sets and to become more data-driven, organizations are prioritizing investments in big data and data analytics initiatives.
Companies must quickly, easily, reliably, and securely process, merge, transform, and transact with data to derive value and insights at the speed of business.

Organizations must improve data management efficiency to effectively leverage their data.

According to our research, two-thirds of organizations are turning to automation to gain efficiency: The second-most researched automation-related topic among IT buyers whose purchase research we observe is Business Intelligence and Analytics. Our Data Analytics & AI analysts and demand-side research help tech vendors succeed in this rapidly evolving market by covering essential topics that include:

Analytics

Artificial intelligence hardware

BI & data visualization

Data platforms

Data science & machine learning platforms

Databases, warehouses, lakes & platforms

DataOps & MLOps

Generative AI

Speech, NLP & computer vision
Research Brief

Data Complexity Inhibits Large Midmarket Organizations Along Their AI Journey

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 […]

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Analysts Covering Data Analytics & Artificial Intelligence

Mark Beccue

Principal Analyst, Artificial Intelligence

Areas of Expertise

AI Development Platforms & Tools

AI Lifecycle Management

AI Risk Management

Foundation Models/LLMs

Legacy and Generative AI

Natural Language AI

Read more

Mike Leone

Practice Director, Data Management, Analytics & AI

Areas of Expertise

Analytics

Artificial Intelligence (AI)

BI & Data Visualization

Data Platforms

Data Science/Machine Learning

Data/AI Lifecycle

Read more
Recent Data Analytics & Artificial Intelligence Insights

Research Brief | May 13, 2025

Saturated Application Environments Push AI-led Observability to Its Early Limits

Torsten Volk
Emily Marsh
Christian Perry

Cloud & IT Operations

Research Brief | May 13, 2025

Industries Dive Headfirst Into AI-enhanced Observability

Torsten Volk
Emily Marsh
Christian Perry

Cloud & IT Operations

Infographic | May 9, 2025

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

Todd Thiemann

Cybersecurity

Research Report | May 9, 2025

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

Todd Thiemann
Emily Marsh

Cybersecurity
Oracle X10M Delivers High Performance with Triple Query Speed, Making it Ideal for AI and Analytics Workloads

June 27, 2023

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Stephen Catanzano

In today’s data-driven world, the power of artificial intelligence (AI) and advanced analytics is undeniable. They have the potential to revolutionize industries, drive innovation, and unlock valuable insights. However, behind every successful AI and analytics initiative lies a crucial foundation: excellent data management capabilities.

In a recent research survey from TechTarget’s Enterprise Strategy Group, Data Platforms: The Path to Achieving Data-driven Empowerment, when they were asked about the most important areas of their data platform, 31% of organizations ranked data management, including databases, at the top. In the same survey, 50% of participants identified faster business decision-making as the leading driver and goal for their modern data platform strategies, with 19% focused on creating competitive advantage. Database performance is critical to reaching the data-driven outcomes desired by organizations.

Oracle has introduced its latest addition to the Exadata database machine family, the Exadata X10M, which is powered by AMD’s EPYC server processors. This release marks a significant milestone for Oracle and its database performance capabilities. The Exadata platform is known for its co-engineered hardware and software, specifically designed to support enterprise data management. It offers a complete stack system with optimized processors, memory, and storage, as well as system software for efficient data storage, indexing, and movement. With the Exadata X10M, Oracle continues its commitment to delivering high-performance database servers by leveraging AMD’s EPYC CPUs, incorporating up to 96 multithreaded cores, DDR5 memory, and RDMA over Converged Ethernet (RoCE) for low-latency, high-bandwidth connectivity.

Oracle’s optimization efforts extend beyond hardware to its software stack, ensuring linear scalability and maximum performance across multiple cores. The Exadata X10M stands out with its high performance, surpassing its predecessor, the X9M, in terms of OLTP performance, analytics, and database consolidation. Oracle’s decades-long expertise in both enterprise database software and hardware solutions enables the company to provide a tailored data management platform that combines performance, value, and cost-effectiveness.

Customers can deploy Exadata on-premises, in the cloud, or as a hybrid solution, benefiting from Oracle’s collaboration with public cloud providers and high-speed interconnectivity options. Overall, Oracle’s focus on innovation and customer-centric solutions positions Exadata X10M as a compelling choice for organizations seeking high performance in their data management and analytics initiatives.