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.

Selecting the right data technology is one of the most foundational decisions a data or IT leader can make. Data management systems directly influence how effectively an organisation integrates, governs, cleanses, and leverages data across the business. From master data management and data integration to data quality, cataloging, and governance, the right technology enables trusted, accessible data that powers analytics, operational systems, and strategic decision-making. The

Data Integration and ETL software is foundational to any organisation seeking to connect systems, move data reliably, and build trustworthy analytics, reporting, and AI capabilities. As data volumes grow and architectures shift toward cloud, hybrid, and real-time environments, the choice of integration platform has become increasingly important. If your organisation is exploring new Data Integration or ETL software in 2026, this guide outlines the major solutions available an

Who are Dremio? Dremio is a data analytics software company that provides a cloud-native lakehouse platform designed to simplify and accelerate data access for analytics and AI. Founded in 2015 and headquartered in Santa Clara, California, the company enables organizations to query, analyze, and activate data directly from cloud and on-premises sources without the need for complex data movement or duplication. The company was founded by Tomer Shiran and Jacques Nadeau, both p