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Our seasoned analysts couple their industry-leading B2B research with in-depth buyer intent data for unparalleled insights about critical technology markets.
Clients trust us across their GTMs—from strategy and product development to competitive insights and content creation—because we deliver high-quality, actionable support.
Browse our extensive library of research reports, research-based content, and blogs for actionable data and expert analysis of the latest B2B technology trends, market dynamics, and business opportunities.
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.
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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…)
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.
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.
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.
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.
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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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.
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?
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:
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 […]
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:
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 […]
