Data Management Insight Blogs The latest content from across the platform
Data Lineage is Finally Getting the Attention it has Always Deserved, Says Solidatus
For Philip Dutton, chief executive of data lineage specialist Solidatus, operating a business whose data dependencies have no robust lineage controls is like boarding a flight on an aeroplane that has no blueprints. “If they told you they didn’t know how the plane was put together, I’m not sure you’d step on board,” Dutton tells Data…
AI in Capital Markets Handbook 2026: From Experimentation to Governed Execution
Capital markets firms are under pressure to convert AI experimentation into sustainable business value. The challenge is not simply finding use cases but demonstrating that AI can support regulated workflows through approved data, accountable ownership, measurable outcomes and defensible evidence. A-Team Group’s AI in Capital Markets Handbook 2026 examines that shift across the trade lifecycle….
Institutions’ Data Governance Capabilities Strengthening Amid AI Adoption
Financial institutions are leading the way in strengthening their data governance capabilities as artificial intelligence reshapes the industry, research by the Enterprise Data Management Association (EDMA) found. The study, published in the international organisation’s annual Global Data Management Benchmark Report, found that financial organisations scored the highest, and beat all all other industries, in their…
Private-Market Investors Don’t Need to Wait for ‘Perfect’ AI Data, says JMAN
The shorter investment lifecycle of private-market investments has made it necessary for participants to access analytics and other data-led processes at speed. The obvious focus in achieving that has been on developing artificial intelligence applications. But piloting initiatives on evolving models can take time. Organisations want to test their applications to know they will work…
Clean Data Is Not Enough to Power AI
By Shai Popat, managing director, product and commercial strategy, financial information, SIX. Agentic AI projects are beginning to roll out across the financial industry. Many firms are testing AI’s feasibility by assigning it relatively simple tasks, such as summarising information or retrieving data and documents from internal databases. Two maxims are often cited when discussing…
LexisNexis Q&A: Ensuring Data Trust, From News to Governance
Since the 1970s, LexisNexis has been providing a variety of data services to financial institutions. Data Management Insight spoke to Danielle McCormick, vice president of product, Global Nexis Solutions, to discuss how financial institutions are approaching AI, trusted data and the future of enterprise intelligence. Data Management Insight: Hello Danielle, when were LexisNexis’ data operations…
Data Reconciliation Hurdles Seen Holding Back Innovation
End-of-day reconciliation processes create data challenges that are constraining buy-side firms from achieving efficiencies from new technologies and AI initiatives. The challenges posed by reliance on long-established reconciliation processes come as the buy-side undergoes a transformation of its operating models to accommodate new data management, investment and settlement strategies. This challenge was highlighted in a…
73 Strings QnA: Solving Post-Investment Data Challenges for Private Markets
Paris-based startup 73 Strings was established to modernise the data and valuation infrastructure for private market participants. Data Management Insight spoke to founder and chief executive Yann Magnan about the company’s operations and its ambitions. Data Management Insight: Hello Yann, when was 73 Strings created and how does it serve financial institutions? Yann Magnan: We…
AI In Financial Services: Where The Real Challenges Are Starting to Emerge
By Joe Norburn, chief executive of TCC and Recordsure. Across financial services, AI is now embedded in day?to?day activities, from fraud detection and onboarding to credit assessment and customer interaction. The UK Treasury Select Committee’s recent inquiry reflects just how widespread that adoption has become, especially among larger institutions. What stands out is not that…
12 Leading Vendors Operationalising AI & ML with Robust Data Pipelines
The transition of artificial intelligence and machine learning (ML) models from experimental sandboxes to production environments remains a persistent operational friction point. While quantitative researchers and data scientists can often demonstrate alpha in isolated backtesting environments, the institutionalisation of these models requires a level of data pipeline robustness, latency control and regulatory auditability that research…