Every organisation moving data between systems eventually hits the same wall: spreadsheets and point-to-point scripts do not scale, and the cost of unreliable data pipelines shows up everywhere from broken dashboards to failed AI initiatives. In 2026, the category has been reshaped by two of its biggest consolidation moves in years - Salesforce completing its acquisition of Informatica, and Fivetran and dbt Labs merging into a single data infrastructure company - both aimed s

Workato is often filed under the same heading as other integration platform as a service (iPaaS) vendors, but that label no longer captures what the company is building. Over the past two years Workato has repositioned itself around a distinction borrowed from network architecture: the control plane and the execution plane. The control plane governs what AI agents are allowed to do inside a business. The execution plane is where that work actually gets carried out, across the

Boomi has been one of the defining names in integration platform as a service (iPaaS) since the category first took shape, and it remains a capable, widely deployed platform today. But the integration market has moved on a great deal since many current Boomi deployments were first put in place, and a new generation of platforms has emerged, some built cloud-native from the ground up, some specialised in a single dimension of integration, and some designed around a different c

Choosing the right integration platform starts with knowing who is actually in the market. This directory lists 21 iPaaS software vendors, organised alphabetically, to give IT and integration buyers full visibility of the options available before narrowing down to a shortlist. Viewpoint Analysis is a Technology Matchmaker, helping enterprise and mid-market buyers find and select the right technology fast with no bias. This directory complements our iPaaS Software Options 2026

UK organisations have always generated more data than they can reliably use. The gap between data collected and data trusted - clean, governed, integrated, and ready to inform decisions - remains one of the most persistent and commercially costly problems in enterprise technology. A growing number of UK-headquartered data technology vendors have built products specifically to close that gap, and they are worth serious consideration before defaulting to the large US platforms.

The volume and variety of application integration inside most UK enterprises has grown well beyond what point-to-point custom code can manage. Every SaaS platform added to the technology estate creates new connections that need building, monitoring, and maintaining. Every cloud migration creates a hybrid environment where on-premises systems still need to talk to cloud services. And every AI initiative - whether that is a chatbot, an analytics model, or an autonomous agent -

Organisations deploying AI at scale are facing a question that did not exist five years ago: how do you govern something that makes decisions, produces outputs, and influences outcomes in ways that are not always predictable or explainable? The EU AI Act has moved from regulation in principle to regulation in practice, and boards, risk committees, and regulators are all asking harder questions about how AI models are validated, monitored, and controlled. For many enterprises,

Data volumes are growing faster than most organisations can handle, and the pressure to turn raw data into reliable business insight has never been greater. Choosing the right data warehouse platform sits at the heart of that challenge - get it wrong and every downstream analytics, reporting, and AI initiative suffers for it. In 2026, the data warehouse market is being reshaped by the rise of the cloud lakehouse architecture, the commoditisation of columnar storage, and the e

Enterprise data teams have long faced the same frustration: too much time cleaning and preparing data, not enough time acting on it. Alteryx was built to solve that problem, and over nearly three decades it has grown into one of the most widely used analytics automation platforms in the enterprise market. With more than 8,000 customers worldwide and over $1 billion in annual recurring revenue, it sits firmly at the serious end of the data technology market. Whether you are ex

Customer data has never been more abundant, and customer intelligence has rarely been harder to act on. Most organisations now collect behavioural, transactional, and demographic data across dozens of touchpoints - website, app, email, in-store, call centre, CRM, loyalty programme - but that data sits in separate systems, owned by separate teams, producing separate views of the customer that cannot easily be reconciled. The result is the personalisation gap: knowing in theory

The original data lake promise was simple: store everything, figure out what to do with it later. In practice, that promise aged badly. Organisations that invested in first-generation data lake architectures through the 2010s frequently ended up with what the industry quietly called a "data swamp" - a vast, cheap repository of raw data that was technically accessible but practically unusable, because without structure, governance, or performance-oriented tooling, data enginee

AI initiatives stalling in pilot. Dashboards showing contradictory numbers. Regulatory submissions being questioned. Operational decisions made on data nobody quite trusts. These are the symptoms of a data quality problem - and in 2026, they are more visible and more consequential than ever before. As organisations attempt to scale AI, automate more of their operations, and meet increasingly stringent data governance requirements, the quality of the underlying data has become

For most organisations, digital assets - images, videos, brand guidelines, campaign materials, product visuals, and creative files - are multiplying faster than the processes used to manage them. Creative teams waste hours searching shared drives and email threads for the right version of a file. Brand consistency breaks down when regional teams publish outdated assets. Marketing launches slow when approvals and distribution rely on manual workflows. The DAM market has mature

Managing product data across multiple channels, markets, and systems has become one of the more underestimated operational challenges in modern commerce. For businesses running large or complex product catalogues - whether in manufacturing, retail, distribution, or direct-to-consumer - the cost of inconsistent, incomplete, or out-of-date product information shows up quickly: as lost sales, poor customer experience, slow time to market, and expensive manual data work. The PIM

Data is the foundation of every modern enterprise. Whether you are consolidating fragmented systems, building a single source of truth, or unlocking predictive intelligence, the software you choose to manage, move, store and analyse data has never mattered more. This guide provides an independent overview of the data software market in 2026. It is designed to help IT and data leaders understand their options across the full data stack, from ingestion and integration through t

Data governance has moved from a back-office compliance concern to a strategic priority for organisations of every size. Driven by escalating regulatory requirements, the demands of AI adoption, and growing executive awareness of data quality as a business risk, investment in data governance software is accelerating. This guide offers an independent overview of the leading data governance software options available in 2026 - from established enterprise platforms through to mo

Master data management has long been one of the most technically demanding and commercially consequential problems in enterprise IT. When data about customers, products, suppliers and locations is fragmented across dozens of systems, every downstream process suffers: AI outputs become unreliable, sales reporting breaks down, and regulatory compliance becomes harder to demonstrate. Reltio was built to solve exactly this problem, and for over a decade it has been one of the mos

There are a few core components of a data platform - Master Data Management (also known as MDM) is one of the most important ones. Bad data costs money. When customer records are duplicated across systems, product information is inconsistent between your ERP and your website, or supplier data is held in a dozen different spreadsheets, the downstream effects reach further than most organisations realise - into billing errors, failed CRM reporting, poor analytics, and costly ma

ThoughtSpot are one of the key data analytics vendors. In this blog, we look at who ThoughtSpot are, what they do, and why they should be considered fo your project.

ServiceNow's AI, Data, and Analytics strategy explained - how they are moving from workflows to the AI platform for business transformation to change the market.