Last month the California-based company deepened its association with Databricks, providing the full range of its AI-powered Intelligent Data Management Cloud capabilities within Databricks’ Data Intelligence Platform. The expanded partnership will enable joint customers to deploy enterprise-grade GenAI applications at scale, based on a foundation of high-quality, trusted data and metadata. That followed the unveiling of a similar association with Snowflake. It was also selected by Microsoft as the Independent Software Design (ISV) design partner for the software behemoth’s new data fabric product.

The frequency of the rollouts in recent months has been dictated by the rapidity with which Informatica’s financial institution clients are seizing on the potential of AI. Many are struggling to bring the technology into their legacy systems, while others have a vision of what they want to do with it but not the capability to implement it.

With the market also heavily weighted towards capitalising on the growing generative AI space, Informatica group vice president and head of EMEA North sales Greg Hanson said new developments and enhancements are on the cards for the near future.

“The critical foundational layer for companies is to get their data management right and if you look at the current state of most large organisations, their integration and their data management looks a bit like spaghetti,” Hanson tells Data Management Insight.

“They realise, though, that they have to pay attention to this strategic data management capability because it’s almost as fundamental as the machinery that manufacturers use to make cars.”

Rapid Change

Hanson says that the pace of innovation at Informatica is the fastest he’s seen in his two decades at the company because its clients understand the operational benefits to be gained from implementing AI-based data management processes. This “unstoppable trend towards AI” is being driven by board-level demand, especially within financial services, a sector he describes as being at the “bleeding edge” of technological adoption.

Many have had their appetites whetted by AI’s ability to streamline and improve the low-hanging fruit challenges they face, such as creating unique customer experiences and engagements. To embed and extend those AI-powered capabilities across their entire organisation, however, will take more effort, says Hanson.

“Their ability to harness data and exploit AI’s potential is going to be the difference between the winners and losers in the market,” he says. But the drive to get results quickly may lure firms towards rash decisions that could create more problems later.

“They need to think strategically about data management, but they can start small and focus on a small use case and an outcome that they can deliver quickly, then grow from there.”

Make it Simple

Among Informatica’s clients across 100 countries are banks such as Santander and Banco ABC Brasil, US mortgage underwriting giant Freddie Mac, insurer AXA XL and online payments provider PayPal. Among the services it’s providing such institutions are broad cost reduction by the optimisation of reference data operations and the simplification of their broader data processes.

This latter point is key to helping clients better use their data, says Hanson. Arguing that without good data inputs, AI’s outputs will be “garbage out at an accelerated pace”, he says that many companies have overcomplicated data setups that are hampering their adoption of the technology. By having separate tools to manage each element of their data management setup – including data access, quality, governance and mastering capabilities – large firms are strangling their ability to make AI work for them.

“But now complexity is out and simplicity is in,” Hanson says. “As companies modernise to take advantage of AI, they need to simplify their stacks.”

Enter GenAI

Informatica is helping that simplification through a variety of solutions including its own GenAI-powered technology for data management, CLAIRE GPT – the name being a contraction of “cloud AI for real-time execution”. The technology began life simply as CLAIRE seven years ago. Last year, however, it was boosted with the inclusion of GenAI technology, enabling clients to better control their data management processes through conversational prompts and deep-data interrogation.

Comparing the new iteration to Microsoft’s Copilot, Hanson says CLAIRE GPT now offers clients greater capabilities to simplify and accelerate how they consume, process, manage and analyse data.  Adding fuel to its firepower is CLAIRE GPT’s ability to enable individual clients to call on the combined metadata of Informatica’s 5,000-plus clients to provide them with smarter outputs.

While almost all of Informatica’s offerings are embedded with its new GenAI technology, the next step will be to ensure the company’s entire range of products benefits from it.

“Data management is complex and costly for many companies and it massively impacts the ability of the company to release new products, deliver new services and create more pleasing customer experiences,” he says.

“Our job with GenAI as the fundamental platform foundation is to offer more comprehensive services around that foundational layer of data management, and more automation and productivity around the end-to-end data management journey.”

The post Informatica Sees a Future of AI-Focused Innovation Releases appeared first on A-Team.

A survey by data security provider Dasera found that 73% of institutions questioned said they had high levels of confidence in their ability to fend off ransomware attacks, data breaches and other unauthorised uses of data. Nevertheless, records of attacks showed that those firms were among the worst affected in 2023.

“The significant number of breaches contradicts high confidence in their security strategy, suggesting overconfidence in their security posture,” the report, entitled The State of Data Risk Management 2024, stated. “The sector remains a prime target for cyberattacks due to valuable data, indicating a gap between perceived effectiveness and actual vulnerability.”

The report compared the perceptions of companies in a range of high-profile data-focused sectors, including healthcare and government, with statistics on data breaches compiled by a variety of organisations and studies. These include the Verizon Data Breach Security Report, Kroll’s Data Breach Outlook Report and the Identity Theft Resource Centre.

Record Year

The Dasera survey said the combined conclusions of those studies showed that 2023 was a “record-breaking year” for breaches.

According to Verizon, the financial services industry suffered 477 data security incidents in 2023, compared with 380 for IT firms and 433 in the healthcare sector. Only government bodies suffered more, at 582. Kroll found that financial firms accounted for the largest proportion of attacks, at 27%.

Two-thirds of breaches originated externally. With the balance coming from internal “threat actors”, the financial services firms were among the least protected against attacks from within their own systems.

The report found that 77% of breaches within the sector came from basic web application attacks, miscellaneous errors and system intrusions.

“The survey underscores the importance of adopting integrated and automated data security strategies to address these challenges,” the Dasera report stated. “Reliance on outdated, manual processes and slow adoption of automated systems contribute to current vulnerabilities. Organisations must prioritise modern, proactive approaches, including regular audits, strategic use of technology, and external consulting, to effectively navigate the evolving landscape of data risk.”

The post Financial Firms Have Widest Data Security Perception Gap: Survey appeared first on A-Team.

**The latest results of the surprising snap French election are a timely reminder for asset managers to always expect the unexpected. The knock-on effects on their investments can create a metaphorical line at the door from anxious investors with a million questions on how their portfolios have been impacted.

Going into the run-off, the RN was widely expected to have a reasonable chance at gaining a majority. Instead, the leftist Nouveau Front Populaire bloc won the most seats in this most strange of elections, with Le Pen’s RN coming in third place. While the reaction from markets wasn’t as significant as it could have been, it still had impact on the French sovereign long-term borrowing rate against the German equivalent – a barometer of market sentiment towards French fiscal fortunes.

A major market event is often when an investment manager’s reporting and client servicing capabilities are tested to the limit. In the wake of election results, investors are desperate to get clarity on their portfolios, and how they are impacted. They are often on the phone, calling up their asset managers, requesting information on risk exposures, price impacts, and a plethora of other bespoke inquiries.

Outdate Data Systems

This is not just something that happens in isolation when market-affecting events occur though, it is part of a general trend for investors to become more demanding of the people managing their money. Perhaps this is reflective of the relatively easier access that they have to news in 2024 than they did 20 years ago.

As the news cycle intensifies, asset managers are struggling with outdated systems. Reliance on legacy infrastructure, combined with the piecemeal addition of new products, has made managing the growing volume and variety of data increasingly difficult. This information often isn’t centralised, and with thousands of different clients with varying servicing requirements, there is always a tendency to focus on repeatable client reports. There is also a reliance that requests are coming into the same place, but this often isn’t the case. If there is no central repository, the data that is being provided to clients will often be different – depending on what information that particular team has access to. When bespoke inquiries come in, they are incredibly difficult to deal with effectively. This is felt even more starkly if the incumbent data architectures are not interacting with a modern reporting solution.

Modern Architectures

While the fall-out from the French election does not seem to have been too severe on markets, it is a timely reminder, nonetheless. Client engagement is a key differentiator in an age where performance is squeezed increasingly by passive investing through exchange traded funds (ETFs)– it is important that clients remember who was able to put their minds at ease rapidly in the aftermath of surprise elections or other market-shaking events.

When you consider that those accounts that generally require the most bespoke treatment are the largest accounts, the ones that drive the majority of a firm’s revenue, it becomes clear why asset managers need to expect the unexpected, and prepare themselves with modern, interactive data architectures and reporting solutions.

The post French Election Reminds Asset Managers to Expect the Unexpected appeared first on A-Team.

The Adaptive Intelligent Document Processing product will enable financial institutions to automate the extraction of unstructured data for ingestion into their systems. The London-based company said this will let market participants automate a choke-point that is often solved through error-prone manual processes.

Duco’s AI can be trained on clients’ specific documents, learning how to interpret layout and text in order to replicate data gathering procedures with ever-greater accuracy. It will work within Duco’s SaaS-based, no-code platform.

The company won the award for Best Transaction Reporting Solution in A-Team Group’s RegTech Insight Awards Europe 2024 in May.

Managing unstructured data has become a key goal of capital markets participants as they take on new use cases, such as private market access and sustainability reporting. These domains are largely built on datasets that lack the order of reference, pricing and other data formats with which it must be amalgamated in their systems.

“Our integrated platform strategy will unlock significant value for our clients,” said Duco chief executive Michael Chin. “We’re solving a huge problem for the industry, one that clients have repeatedly told us lacks a robust and efficient solution on the market. They can now ingest, transform, normalise, enrich and reconcile structured and unstructured data in Duco, automating data processing throughout its lifecycle.”

The post Duco Unveils AI-Powered Reconciliation Product for Unstructured Data appeared first on A-Team.

SIX, a global data aggregator and operator of exchanges in its native Switzerland, as well as in Spain, has released a flurry of new data products since January, including a suite of ESG tools and two global equities index families that herald a plan to become a one-stop-shop for ETFs.

According to Leslie, the frenetic pace of partnerships, product releases and enhancements this year is just the tip of the iceberg. The Zurich-based, bank-owned organisation has more to come, all built around a trove of data and data capabilities it has built up over more than 90 years of operations.

At heart, it remains a global pricing reference data provider – that’s the “base data” that SIX “is built on”, says Leslie. But the company is putting in place ambitious plans to leverage that core data competency to meet the increasingly complex demands and use cases of financial institutions.

“I believe that the fundamental data set – having really good-quality reference data and pricing data – allows us to create new value-added services and insights to our clients, and that remains the same whether we’re talking about GenAI or good old fashioned master reference,” Leslie tells Data Management Insight from SIX’s offices in London. “Unless you’ve got those basics you can’t really make sensible decisions, let alone produce reliable analytics.”

Expansion Plans

Leslie says SIX sees its USP as the ability to leverage that core data product to create applications for a multiplicity of use cases. Already it is using its fundamental datasets as the backbone of regulatory, corporate actions, tax, sanctions and ESG products for its banking clients.

A slew of recent acquisitions, investments and partnerships have been similarly guided by SIX’s programme of creating services that can tap into its core offering. The purchase of ULTUMUS in 2021 and the deepening of a long-standing association with BITA earlier this year were part of a plan to forge the company’s ETF-servicing business, each deal enhancing SIX’s indexing capabilities.

In ESG too, it has been aggressively striking deals to help burnish a slate of new sustainability offerings. Products unveiled in the past year by ESG product strategy and management head Martina MacPherson all benefit from supply deals struck with vendors including Sustainalytics, MSCI, Inrate and the CDP, as well as new partnerships with companies including Greenomy. Among the ESG products launched recently is an SME assessment tool, which MacPherson said will bring thousands of smaller companies into the ESG data ecosystem, into which banks and investors might otherwise have had no visibility.

Working Data

SIX’s ESG provisions illustrate what Leslie describes as the company’s dedication to making data work for companies.

“Organisations need to figure out how they’re going to incorporate data and how they’re going to make it relevant,” she says. “Well, the only way you can make it relevant is if it’s got something to hook on to, and that’s where you get back to those fundamental data sets.”

Leslie explains that one of the driving forces behind the company’s vigorous expansion plans is the changing demands for data among banks. No longer can any part of the industry rely on end-of-day pricing data, or monthly and quarterly reports. Ditto for risk managers and compliance teams.

The consequence has been a shift in the workloads of the front-, middle- and back-offices. No longer is research the premise of middle-office teams, Leslie offers as an example; the front office needs those insights quicker and so it has made sense for banks to embed data access and functionality within asset managers own analytical workflows.

“Asset managers see that the speed of data is increasing all the time and so the buy side, which was perhaps in the past much more built around end-of-day or less immediate requirements, is moving much more into real-time and intraday needs,” she says. “That requires, therefore, real-time market data, and that is expected by regulators, it’s expected by customers, and its therefore expected by market participants.”

AI Challenge

Jokingly, Leslie likens data operations to raising a child: it needs constant attention and feeding to grow and thrive. The simile is just as true for banks’ data management needs too; they are constantly changing and growing, influenced by internal needs and external innovations. That’s exemplified by the race to integrate artificial intelligence (AI) into processes and workflows.

Recent SIX research found that more than nine out of 10 asset managers expect to be using AI within the next three years and that half already do. Driven by its own clients’ need to understand what AI will mean to them, SIX has begun looking at how it can enhance its products with the various forms of AI available.

It has taken a structured approach to the programme and is looking at where AI can help clients improve efficiency and productivity; examining how it can improve customer experience and support; and, testing how it can be incorporated into products. For the latter, SIX is experimenting with off-the-shelf GenAI technology to identify aberrations in trading patterns within a market abuse solution.

On this subject, too, Leslie stresses that SIX can only think about such an evolution because it is confident that it has a solid foundational data offering.

“Our role is to make sure that we’re providing data that is fit for purpose and enables our clients to do business in a competitive way,” she says. “So that will include, as it always has, providing trusted, reliable data that the client knows is fit for purpose and on which they can make decisions. And that’s as true if it’s going to an AI model as if it’s going into a client digital wealth platform or portfolio reporting or risk solution.”

Values Align

Leslie took up her latest role at SIX in 2020 and also is a member of the board for the SIX-owned Grupo BME, Spain’s stock exchange, previously holding roles at LSEG and Thomson Reuters.

She is proud to be part of an organisation whose stakeholders are banks – about 120 of them – and not shareholders “trying to race to hit a quarter result”. She feels a very strong alignment with its values, too.

“It’s an organisation whose purpose is to enable the smooth functioning of the economy and has consistency and trust at the very core,” she says. “When half the world is voting this year, this stuff’s important, and when we’re talking about AI, or we’re talking about market failures then the thing that brings trust and progress is the data that sits behind it. To be a trusted provider in this day-and-age is a critical service.”

The post Building Future Growth Around a Foundational Data Core: SIX’s Marion Leslie appeared first on A-Team.

The vLEI Technical Advisory Board will help stakeholders seeking technical, governance and developmental support. Its membership is drawn from expertise within companies including Accelerate, Esatus and Prosapien and will be chaired by GLEIF IT head Christoph Schneider.

The vLEI is a cryptographically secure digital representation of LEIs, the internationally recognised 20-digit code associated with companies around the world. A vLEI enables automatic verification without the need for human checks. GLEIF created the vLEI to help financial and other institutions organise entity-specific data within their systems.

“We have assembled the vLEI Technical Advisory Board to provide this new ecosystem with the best possible chance of succeeding,” said GLEIF chief executive Alexandre Kech. “By connecting the experts and creating the strategic partnerships that will evolve the vLEI’s supporting infrastructure, we aim to establish this system as the fundamental enabler of digital trust across the many value chains that underpin our global economy.”

System Growth

GLEIF said the new board would help accelerate the growth of the vLEI ecosystem by promoting its scalability and technical interoperability. It would also help to promote new use cases and build partnerships with the Open Source community. The board will meet once a month.

The vLEI is built on a chain of trust that is rooted back to GLEIF to provide verified proof of an entity’s identity. The vLEi infrastructure supports blockchain, self-sovereign identity and other decentralised key management systems.

“The strong, global and scalable governance framework of GLEIF combined with the vLEI technology finally offers a once-in-a-lifetime opportunity to establish a digital trust layer for a variety of enterprise use-cases across multiple industries,” said Vasily Suvorov, a member of the advisory board and chief technical officer of Accelerate.

“This will enable a new era of IT innovation targeting solutions that can automate any intercompany business process that requires regulatory, risk, and policy compliance certainty.”

• GLEIF CEO Alexandre Kech will be among panellists at Data Management Insight’s next webinar, which will discuss how financial institutions can maximise the use of data standards and identifiers beyond compliance and in the interests of the business. Click here to register for the event, which will be held on 18 July.

The post GLEIF Creates vLEI Advisory Board to Support Digital Verification Technology Use appeared first on A-Team.

The London-based company raised the money in a Series B funding round led by venture capital firm Highland Europe and AXA Venture Partners (AVP). It follows a £15m cash raise in 2021 and the granting of a £30m debt facility from Kreos Capital last year.

Finbourne’s cloud-native offering, which is built on its SaaS-based LUSID platform, enables financial institutions to manage the data they need for front-to-back office processes, including portfolio and risk management. Its network of functionality and data is boosted by AI and helps institutions streamline workflows and increase revenue, Finbourne said.

“Over the past few years, Finbourne has built a revolutionary SaaS platform that is enabling many of the world’s biggest financial institutions to move from legacy, siloed solutions to a modern data architecture, allowing full, real-time visibility and optimal decision making,” said Highland Europe partner Tony Zappala.

The company plans to use the new funds to expand its sales, product and marketing capabilities in the UK, the US, Ireland, Singapore and Australia.

The Series B investment follows Finbourne’s announcement that it has deepened its engagement with post-trade operational workflow specialists Tasksize. The partnership, which began in 2021 when Tasksize was integrated into the LUSID platform, will see its expertise deployed to improve management of Finbourne clients’ post-trade processes.

The post Finbourne Raises £55m to Fund Overseas Expansion appeared first on A-Team.

Data Virtuality’s Tammie Coles believes that the key ingredient to any successful data mesh implementation is getting the human element right. From boardroom buy-in to data team acceptance, the pain points for financial institutions that still struggle with their transformation can be traced back to change management policies, Coles tells Data Management Insight (DMI).

“A lot of clients that already have initiated something on data mesh realise that the idea of it and the reality of it are very difficult to bring together,” says Coles, who heads the company’s global sales team. “Data culture can kill a data strategy as well as bring it to life; you need to get the people within the organisation open for change and open to new things. As obvious as that is, it remains a big stumbling block.”

The data mesh concept was crystalised in 2019 by former ThoughtWorks technology director Zhamak Dehghani as a solution to increasingly complex data management challenges, especially as enterprises ingest ever-greater volumes of information from outside sources.

Decentralised Specialisation

Dehghani, who has since gone on to found and head Nextdata, envisaged a mesh as a decentralised framework in which data is treated as a product owned within corporate domains but shared across an enterprise through a self-service setup under a federated governance structure.

Along with other proponents, she sees meshes as a means of making data more accessible, available, discoverable, secure and interoperable. With so much data within modern systems, this federated architecture should enable teams to query data and to translate insights into faster times-to-value.

Coles, however, says that many firms have failed to grasp the preconditions necessary to make a mesh work.

“I would argue that the ones who say they tried to put data mesh in place and haven’t succeeded predominantly have not done so because of the [corporate] political thing,” she says. “It might excite a couple of people in an organisation, but then other people might be too afraid of change.”

Ill Defined

Another reason for failure has been an inability to fully understand the concept. Coles says she is aware of companies that have tried to implement a mesh-based strategy only to find that they had interpreted the principles incorrectly. Dehghani also suggested as much, arguing that companies had redefined “federated governance” to mean giving non-technical people access to all data.

Managing change is difficult and Coles sympathises with companies that embark on such a programme. Any data management transformation can be long and arduous, she explains with reference to the experience of Commerzbank in relation to a separate data-related project. The German bank managed to get the project in place within a year, but it took another six months to get the technical implementation of the project right.

She likens the governance change necessary to implement a data mesh to the transition of a country from a monarchy to a democracy; when users are first given autonomy over how they use and manage their data, “they are lost” and unsure of how to behave, how to explore and how to make best use of their data.

Similarly, proposals to decentralise control over data are often rejected by those who command access to it.

“If you are the monarch you’re not likely to want to give up your power,” she says. “They think ‘there’s no way I’m handing it over’.”

Still Necessary

The charm of a data mesh, Coles says, is that it doesn’t require organisations to “rip out” the technologies and products that they already use. Instead, a mesh can be moulded to sit above tech stacks. This makes them a valuable proposition for bringing more effective data use to organisations without the expense of rearchitecting their infrastructural setups.

Making that easier now, she argues, is artificial intelligence. Machine learning (ML) and large language models (LMMs) can do more than transform the data monarchy, she argues – it will instigate a full-on revolution.

“In future, I believe that AI will enable the less technical to take full advantage of the data – thereby challenging the data monarch,” she says. “This revolution will have an impact on our day’s data management concepts, including data mesh and data fabric.”

In the meantime, data meshes have a valuable role to play for organisations if they establish the interdepartmental connections to make it work before they embark on implementation.

“It’s about awareness, about picking people up and generally pointing out what the advantages are, what the company wants to achieve and how it benefits most of the people,” she says. “I don’t think that gets clearly communicated. There must be trust in the companies to do that, really, and not necessarily lead people to believe that changing to a data mesh will mean they will have to do more work.”

The post Data Mesh and The People Problem appeared first on A-Team.

Snowflake has been investing in GenAI and end-to-end ML capabilities to help customers build and deploy high-impact AI use cases that maximise the value of their data. In particular, the company has invested in Snowflake Cortex AI, a fully managed generative AI service, and Snowflake ML, a set of capabilities for training, deploying and running predictive models.

The company’s agreement to acquire TruEra and its AI observability platform, which provides capabilities to evaluate and monitor LLM apps and ML models in production, will provide deeper functionality that will help organisations drive AI quality and trustworthiness by evaluating, monitoring and debugging models and apps across the full lifecycle, in both development and production.

TruEra’s technology evaluates the quality of inputs, outputs and intermediate results of LLM apps. This expedites experiment evaluation for a wide variety of use cases, including question answering, summarisation, retrieval-augmented generation-based applications, and agent-based applications. It also provides detailed, actionable insights to improve ML model performance and accuracy by revealing anomalies in model metrics and providing specific root cause analysis for rapid debugging.

TruEra AI observability also helps identify LLM and AI risks such as hallucination, bias, or toxicity, so that issues can be addressed quickly, and organisations can demonstrate compliance with AI regulations.

Snowflake states: “TruEra’s capabilities complement the AI and ML data governance functionalities we already provide in the AI Data Cloud. Snowflake provides deeply integrated capabilities to ensure the accuracy and trustworthiness of data used to supplement and train models. The observability technologies developed by TruEra will complement and round out that story for AI.”

TruEra’s co-founders – CEO Will Uppington, president and chief scientist Anupam Datta, and chief technology officer Shayak Sen – will join Snowflake along other TruEra engineers and executives.

The post Snowflake Reaches Agreement to Acquire TruEra AI Observability Platform appeared first on A-Team.

The aim of Alation Workflow Automation, according to the company, is to eliminate the need to expand data steward teams, provide more reliable decision-making and ensure cost-effectiveness while delivering trusted and AI-ready data across the organisation.

“Workflow automation is critical in today’s complex data environment,” says Junaid Saiyed, chief technology officer at Alation. “By leveraging Alation Workflow Automation, organisations can automate mundane data management tasks, enabling data stewards to focus on strategic initiatives that propel business impact. This shift boosts metadata quality and ensures adherence to regulatory standards more efficiently.”

Alation Workflow Automation is underpinned by a number of bots including a completeness bot that ensures all titles and descriptions are accurately filled out and properly categorised; compliance bot that streamlines data governance tasks to improve data consistency and compliance, identify data classified as Personally Identifiable Information (PII) and support custom rules to ensure compliance with regulations; a current content bot that includes rules to identify whether data is current and up-to-date; and a metadata management bot that ensures data stewards and data owners receive timely alerts when new metadata is added to key databases.

The post Alation Workflow Automation Deploys AI Bots to Optimise Data Governance appeared first on A-Team.