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Content marketing is getting more and more competitive

In the beginning, achieving success with content marketing was quite easy. Since very few companies used these tactics, those who did gained a clear advantage.

Today the world has drastically changed. Now the word has spread about the success some companies have had with content marketing and the value it has delivered, the content and information space has become crowded and very competitive.

The result: consumers are now drowning in information. Something called content fatigue has kicked in.

Still, content marketing is one of the most successful marketing tactics out there. Recent studies show that content marketing “generates over three times as many leads as outbound marketing and costs 62% less.

So, the question is: what differentiates content marketing leaders from the rest of the pack? What do marketers have to do to get content marketing right?

One essential part of the answer is this: There is a need to focus on delivering highly valuable and relevant content to your audience. Content should be tailor-made to the specific pain points and needs of the people you want to reach.

Besides making content more helpful and easier to consume, content marketing leaders increasingly use original data-based research as a means to increase the value their target group gains from consuming their content.

Since there is so much information available today, consumers nowadays are very selective about what they consume and trust. Our own research shows that 98 percent of BI Survey participants agree with the statement “I place high emphasis on the trustworthiness of the source”.

Therefore, being able to provide content based on original market research from an independent and neutral analyst firm will give you a head-start on the competition when it comes to building a successful content marketing program.

Our topical user surveys are specifically designed to give you this head-start.

What our surveys offer

BARC's Topical Surveys provide you with a wealth of benefits.

Attract new visitors

BARC surveys provide valuable content for decision-makers who need to research a certain topic. Use our research to attract and convert new website visitors.

Generate leads

BARC surveys are an excellent tool for lead generation. As a sponsor you can distribute this high-quality content on your website for download.

Level-up your content

Fuel your content channels with blogs or posts based on our original survey-based research. Attract readers who are looking for information on how to address a specific pain point.

Create awareness

Create awareness among customers, press, partners and investors. Position yourself as a relevant provider and thought leader on a particular topic.

Understand your audience

Find out what your target group thinks about a certain topic, which pain points you need to address, and which benefits they expect.

Gain insights

Get first hand and representative information on how a market trend is perceived by real end users and where they want to invest.

Reach/Statistics

250

Readers of BARC research per year worldwide

18

Verified survey participants

10

Annual market research user studies
About

BARC Topical Surveys

Topical Surveys support end-user organizations in their IT/digital strategies and their choice of partners. Our research analyzes the latest market and technology trends – as well as changes to ecosystems – to guide end-user organizations in their digital transformation journey.

Each DACH-wide study is built on interactions with – and a survey of – 150+ senior buy-side executives. Global surveys are based on 250+ participants.

Topical Surveys are conducted in an independent and objective manner on behalf of several stakeholders.

Our surveys

Choose the survey to fit into your campaigns

Our research agenda reflects the hottest, most relevant topics and pains points in the data, BI and analytics space. See what we have in store for the upcoming year:

The Future of BI & Analytics - Adopting Generative AI for Analytics: Early Trends, Lessons, and Best Practices

Start: Q3 2023
Publication: Q1 2024
Region: Global

The public availability of OpenAI’s Generative Pretrained Transformer ChatGPT marked a decisive shift in the perception of AI. BI & Analytics (BIA) is expected to make a huge leap forward by fully leveraging generative AI.

Generative AI will change the use of BIA for developers, power users and casual users as well as administrators. It can speed up code-creation and the development of semantic models and dashboards beyond what anyone expected a year ago. When integrated properly, generative AI can help answer business users’ questions much faster than current analysis methods and with greater flexibility than most NLQ features marketed today.

This study will examine where companies see the biggest potential and what steps have already been taken.

Key questions:

  • What are the goals and objectives companies are pursuing through generative AI in BIA?
  • What distinguishes the most successful companies from less successful ones?
  • What are the main concerns (e.g., security, data privacy) and issues (e.g., ‘hallucination’, training) that companies face?
  • What use cases are already being implemented and which are in the pipeline?
  • What areas of BIA have the greatest potential for improvement?
  • What tools are currently used and how are they integrated into the current BIA landscape?
  • What skills and resources are required for successful implementation?

Optimizing Your Architecture for AI Innovation - Augmenting Existing Data and Analytic Infrastructure for AI/ML

Start: Q4 2023
Publication:  Q1 2024
Region: Global

This research paper explores the evolving landscape of AI and the challenges end users face as they navigate new technologies, architecture and data strategies. The focus will be on the clash between innovation and traditional systems, particularly concerning the new platform strategies adopted by mega-cloud vendors and how they compare to innovative point solutions or customers who have invested in existing technologies.

Furthermore, the research will investigate the needs and expectations of end users, including investment protection, low-risk AI on-ramps, and the ability to refresh, augment or replace current systems.

The paper will explore emerging technologies such as vector DBs and generative AI, including underlying models such as LLMs. The research will highlight the potential of augmenting existing infrastructures with AI/ML and explore various use cases, fast tracks for ROI, and low-hanging fruit. Finally, the paper will address skill gaps and organizational issues that may arise while adopting new AI technologies.

Key questions:

  • Advantages and disadvantages of highly integrated platforms for powering AI?
  • Additional/new technologies required for AI success
  • Strategies to lower risk and protect existing investments
  • Where to start, what to avoid, what to consider

The State of ESG & sustainability reporting 2024

Start: Q3 2023 / Q1 2024
Publication: June 2024
Region: Global

ESG reporting is often a highly disruptive process, and the typical motivation for undertaking it is not solely driven by legal requirements. It has become an essential tool for companies aiming to maintain a positive image among their customers, employees, and business partners.

Today, various organizational and technical approaches to ESG reporting implementation compete within companies, resulting in diverse experiences, challenges, and outcomes.

BARC’s study examines these developments and compares customer experiences across different geographic regions, organizational structures, and technical implementations. This enables the extraction of specific guidance and recommendations.

Building upon the study conducted in the previous year, the current state of a nascent software market will also be analyzed, providing an in-depth look at its current stage of evolution

Key questions:

  • What is the state of implementation and which department is driving the spearheading the effort?
  • How resource-intensive is the process of implementing ESG reporting, and what difficulties do end users encounter?
  • What software is utilized for ESG and sustainability reporting, encompassing data collection to result disclosure, and how do leading companies employ it?
  • What architecture do users favor for the implementation, and how does it align with the broader landscape?

The State of MLOps, DataOps & Observability - Trends, Challenges and Best Practices

Start: Q2 2024
Publication: Q4 2024
Region: Global

As companies expand the role of analytics and artificial intelligence in their business, they collide with a familiar problem: old, bad data and changing ML outputs. Overwhelmed data teams struggle to reliably deliver the high-quality inputs that modern BI and AI/ML projects require. MLOps and DataOps programs help by applying principles of DevOps, agile software development and total quality management to ML and data pipelines. Both Ops disciplines comprises testing, continuous integration and deployment (CI/CD), pipeline automation and data respecitvely ML observability.

This report by Eckerson Group and BARC assesses the state of MLOps, DataOps and data observability programs in the modern enterprise. It will examine adoption drivers, obstacles, challenges and best practices for success. By comparing best-in-class and laggard adopters, readers will learn proven techniques for optimizing ML and data pipelines  – and the business results they generate.

Key questions:

  • Common issues with data quality, ML  and pipeline performance
  • The scale and scope of DataOps and data observability programs worldwide
  • Why enterprises do and don’t adopt these programs
  • What business benefits they achieve
  • How MLOps, DataOps and data observability can fail
  • Typical use cases and lessons learned from them

Data Mesh & Data Fabric – From Theory to Application

Start: Q1/ Q2 2024
Publication: September 2024 (for BARC Event “The Heart of Data Mesh & Fabric)
Region: Global

Data Mesh, as an organizational paradigm, has rapidly captured global attention, with its promise to transform the way companies share and use data. Similarly, the concept of data fabric, as a technical capability, offers organizations unprecedented flexibility and agility. While the initial buzz around these innovations was overwhelming, businesses have now transitioned from mere fascination to hands-on implementation. This study delves deep into the trenches to understand not only the best practices and positive experiences but also the challenges and lessons learned as organizations embark on their journey towards crafting data products, fostering domain ownership, and instituting self-service data platforms along with federated computational governance. Our exploration covers both the organizational and technical dimensions of introducing and embedding the principles of data mesh and data fabrics within enterprises.

Key questions:

  • What are the fundamental drivers towards establishing Data Mesh and Data Fabric in enterprises?
  • What challenges did companies run into when establishing Data Mesh and Data Fabric?
  • What benefits were expected and could be achieved from implementing Data Mesh and Data Fabric approaches?
  • Where are companies in their journey and what are the goals and next steps?

The Most Impactful AI Use Cases in Finance and Controlling - Where and how AI makes its contribution to greater effectiveness and efficiency

Start: Q1 / Q2 2024
Publication: October 2024
Region: Global

The use of artificial intelligence (AI) and machine learning (ML) has been an important trend in finance and controlling for years. Recent BARC study results show that 3 out of 4 companies have already considered using AI/ML in finance and controlling or are planning to invest in it.

So, it is no longer just the early adopters trying to set themselves apart from the competition with innovative and intelligent use cases. AI today is applied in numerous business areas and in extremely diverse ways. Generative AI in particular is an emerging field that has progressed exponentially in recent months. More pervasive use of AI across all industries clearly has the potential to boost productivity.

With such enormous potential to support and automate business processes, the search for impactful use cases is emerging alongside new challenges. Companies are increasingly faced with the question of how they can leverage AI in finance and controlling and how to implement it successfully and build sustainable expertise. In this context, the support provided by software tools must not be underestimated and is certainly a key success factor.

This study follows the successful BARC study “Predictive Planning and Forecasting on the Rise – Hype or Reality?” and examines the most impactful AI use cases in the extended field of finance and controlling. The use of generative AI is a core aspect of the new study.

Key questions:

  • What AI use cases are being implemented in finance and controlling (now and in future plans)?
  • What is the role of generative AI in finance and controlling? What is the potential to boost efficiency and effectiveness? What are the expectations in the market?
  • How to successfully implement AI in finance and controlling? Lessons learned from leading organizations.
  • How do modern CPM tools support finance and controlling organizations in transitioning to increased automation through AI?

AI Use Cases: Hype vs. Reality

Start: Q1 / Q2 2024
Publication: November 2024
Region: Global

This study will examine the reality behind the hype: which AI use cases are proving successful, where the most successful investments are being directed, which areas companies are experimenting in right now, and the challenges AI pioneers have faced.

We will delve into specific areas, such as technologies, implementation costs, organizational embedding within companies, and the balance between in-house and outsourced approaches in AI implementation. Our aim is to uncover best practices and provide companies with a benchmark for implementing their own AI scenarios.

Key questions:

  • Characterization of AI use cases (in which areas/departments are they found? What are the aims/goals? Who is driving the initiatives?)
  • What did it cost? (rough estimate of budget, internal vs. external resources)
  • Which technologies were used?
  • Drivers and benefits – Why was the use case chosen? Was it successful?
  • Challenges encountered

Data, BI & Analytics Trend Monitor 2025

Start: Q3 2023
Publication: Q4 2024
Region: Global

BARC’s Data, BI & Analytics Trend Monitor 2024 reflects on the trends currently shaping the BI and data management market. We ask close to 2,500 users, consultants and vendors for their views on the most important trends in BI and data management. Numerous respondents reveal a comprehensive picture of regional, company and industry-specific differences and deliver an up-to-date perspective of the market.

The Data, BI & Analytics Trend Monitor provides detailed information on:

  • Which trends are strongly influencing the market and which ones represent only a niche market?
  • Which trends are most important in specific regions and industries?
  • What differentiates best-in-class companies from the rest?
  • Where do the opinions of users, consultants and vendors diverge?

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