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What’s been happening in the data and analytics sector over the last 3 months?
Our Buyer Intelligence platform has been continuously tracking first-party buyer signals across our database, bringing you timely insights in the form of our latest data and analytics intent report.
Our mission? To help tech vendors keep their demand generation strategies tightly aligned to real market demand.
Published quarterly as part of our category-specific intent reports, our latest blog takes a deeper dive into the shifts most likely to impact your data and analytics — and how you can action these insights to drive success.
Ready to level up your strategy? Let's go...
This quarter vs last quarter: what’s changed?
Compared with the previous quarter, intent signals show a clear maturation in how buyers approach data and analytics decisions. The focus is no longer just on acquiring tools; it’s on governing, operationalising, and extracting sustained value from analytics investments.
Four key shifts shaping Q1 2026:
Governance and security have intensified, not eased
Concerns have evolved from data quality alone to broader issues around data silos, regulatory compliance, and secure foundations for analytics at scale.
Skills shortages remain a persistent blocker
Despite rising interest in advanced analytics and machine learning, the lack of skilled analysts continues to constrain progress across buyer groups.
Evaluation has shifted from tools to platforms and strategy
Buyers are researching analytics platforms and data strategy development rather than point solutions.
Decision-stage behaviour now centres on execution and performance
Buyers are focused on implementation quality, change management, and ROI tracking rather than contract negotiation alone.
What this means: data and analytics buying is entering an execution-first phase, where credibility is earned through delivery frameworks, operating models, and proof of value—not feature lists.
Market snapshot
Here's what our latest networking and comms intent report reveals about the current market:
- Interest is strongest in the technology, finance, and healthcare industries.
- Traction is highest among enterprises with 1,000–5,000+ employees.
- Data analyst, data scientist and business analyst functions show the greatest engagement with data and analytics topics.
From insight to impact, here's how to apply our insight into your campaigns:
Targeting and segmentation action points:
1. Segment enterprise organisations in surging industries and geos into priority tiers.
2. Within these tiers, filter intent signals among trending job functions to inform topic clusters.
Why?
Segmenting your market into priority tiers helps to focus go-to-market teams on the accounts most likely to convert. Further segmentation techniques such as topic clusters opens up opportunities to tightly align content to specific audience segments. The result? Relevancy at scale.
Research patterns at account level
5 hot topics dominating research in the data and analytics space (over the last 3 months), are:-
Machine learning models – Interest continues to accelerate as buyers explore predictive and automated decisioning beyond descriptive analytics.
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Predictive analytics – Teams are increasingly focused on forward-looking insights that support planning, forecasting, and risk mitigation.
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Cloud data services – Scalable, cloud-native data foundations are a priority as organisations modernise analytics architectures.
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Data visualisation – Demand reflects the need to make complex data accessible and actionable for non-technical stakeholders.
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Business intelligence – Core BI remains critical as organisations look to consolidate reporting and improve decision confidence across the business.
The topic showing the greatest decline in popularity is:
- Data warehousing with a 20% MoM decrease
What do these insights mean for your content strategy?
To keep your strategy aligned to market demand, we recommend:
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Prioritise content that maps machine learning models and predictive analytics to real business use cases, showing how organisations move from descriptive reporting to forward-looking decision support.
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Build a clear narrative around cloud data services as the foundation for scalable analytics, focusing on modern architectures, interoperability, and performance rather than individual tools.
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Use data visualisation and business intelligence content to demonstrate how insights are operationalised across teams, with examples that highlight decision impact, speed, and confidence.
Pro tip: Use AI/NLP tools (topic modelling, entity extraction, semantic clustering) to audit your content library against competitor coverage. Flag underserved subtopics and build differentiated assets where demand is growing fastest.
Buyer group analysis
As data volumes grow and analytics environments span cloud platforms, distributed teams, and multiple business units, buyers are being judged on how effectively data can be trusted, accessed, and turned into insight. The focus has shifted to whether analytics foundations support predictive use cases, scalable cloud data services, and consistent decision-making across the organisation. Our report shows how the emphasis on these factors shifts by role and seniority.
What does this mean for your persona development?
Tailoring content to buyer role nuances is critical for engagement and conversion. Here's how you can translate these insights into accurate personas.
Persona A: Influencers and researchers (such as data scientists or data analysts)
- Concerns: data security, data silos, and lack of skilled analysts are shaping early-stage research, reflecting anxiety around control and feasibility.
- Content preferences: they want educational content that clarifies emerging analytics approaches, governance best practices, and how advanced analytics can be adopted safely.
Persona B: Mid‑level decision makers (such as heads of data and analytics or BI managers)
- Concerns: regulatory compliance, data silos, and operational complexity dominate, signalling responsibility for making analytics work day to day.
- Content preferences: they want practical guidance, implementation frameworks, integration strategies, and examples of analytics in real operational contexts.
Persona C: Senior budget holders (such as CDOs or CIOs)
- Concerns: data silos, compliance risk, and skills shortages are viewed through the lens of cost, scale, and organisational impact.
- Content preferences: they want strategic justification, ROI evidence, and confidence that analytics investments will deliver sustained value.
Action point: Create a persona matrix aligned to job functions (e.g. network engineer vs. CTO). Map their pain points against the funnel stages they are most likely to participate in. For example, analysts may engage early (implementation and best‑practice content), while senior leaders will respond to BOFU tools like ROI models and rollout risk frameworks.
Recommended reading: How to map content to the new buyer journey
3 data and analytics demand gen plays:
Our AI Buyer Intelligence platform is able to interrogate research patterns by buyer stage. Here's how to action these insights into a full-funnel demand gen strategy aligned to real buyer behaviour:
Tip: Keep the exact keyword phrases in page titles, H2s, alt text, and internal links so performance teams can tie content to intent spikes and prioritise syndication/remarketing accordingly.
Ready to activate these insights?
Why not try our demand gen planning framework? It’s a collection of templates and planning aids, including:
- Buyer group intent mapping
- Multichannel campaign blueprint
- Funnel‑aligned strategy shortcuts
Everything you need to turn category intent insights into campaigns that convert.
