McKinsey & Company

Attribution Limits B2B Growth, Urges Broader Investment

Chris Bagnall, CEO & Founder of Transmission, argues that an overreliance on tidy attribution metrics (especially last-click) constrains B2B marketing growth by diverting budget toward immediately measurable touchpoints and away from brand, community, and creator work that drives future demand. The piece cites research from Gartner, Forrester, McKinsey and Brand Finance to show most of the B2B buying journey occurs out of view of dashboards and involves multiple decision-influencers. Bagnall recommends broader measurement — including account-based attribution platforms (e.g., Dreamdata), incrementality/holdout tests, marketing-mix modelling, brand tracking, and self-reported attribution — and argues businesses should fund activities they cannot fully trace.

Sentra Launches Shadow AI DLP to Prevent Data Leakage

Sentra announced Shadow AI DLP, a new capability in its AI Data Readiness Platform that discovers employee use of browser-based generative AI tools and enforces data-aware controls in the browser to prevent sensitive data from being shared with AI applications. The feature combines Shadow AI Discovery and AI Browser DLP to classify content locally, apply policy by application and data class, and create audit trails without routing original content through Sentra. The announcement cites analyst research (Gartner, McKinsey) on the importance of data readiness for AI and includes Sentra's market estimate for AI data readiness. Sentra says Shadow AI DLP complements existing DLP investments and partners with next-generation DLP providers.

Acceldata Adds AI Observability to xLake Platform

Acceldata announced AI Observability as a native capability of its xLake Data & AI Platform, providing unified observability and governance for LLM and agentic AI applications across hybrid, on-premises and multi-cloud estates. The solution traces prompts, model calls, tool invocations and retrieval steps, continuously evaluates outputs, and links agent behavior to data quality, lineage and pipeline health already monitored by the platform. Delivered via SDK and framework integrations, AI Observability centralizes execution traces, data traceback, continuous evaluation, human feedback promotion into datasets, cost and performance visibility, and runtime safety controls. Acceldata says this approach helps enterprises safely operate agentic AI against distributed data without centralizing it. The announcement cites a survey Acceldata ran via GLG showing widespread hybrid architectures and multi-platform use at large enterprises.