Cookie policy

By pursuing your navigation on our website, you allow us to place cookies on your device. These cookies are set in order to secure your browsing, improve your user experience and enable us to compile statistics. For further information, please report to our cookie policy.

Article (88/240)
Data as a service
Data as a service

Data as a service


The big-data innovations of the tech giants are transforming the landscape for data as a service. Increasingly, clients expect to be able to access and manipulate their data directly

The “big tech” companies such as Google and Amazon have set the bar high when it comes to data responsiveness. Consumers are used to receiving instant responses as they query vast amounts of data, and such expectations are becoming increasingly more evident in the business world. In the securities services arena, providers have some catching up to do as their clients require more instant – and tangible – benefits from the big-data revolution.

“The asset-servicing industry is migrating from one based on service-led offerings to one that is based on data- and technology-led services,” says Craig Bell, global head of Strategic Operating Model – Middle Office at BNP Paribas Securities Services. “Data is now the service.” Custodians and asset servicers are moving up the value chain to provide new value-added services, such as data as a service (DAAS), he added. Building on the concept of software as a service, DAAS enables data to be provided on demand to a user, regardless of any geographic or organisational separation between provider and consumer. The development of open-source technology means the platform on which the data resides is irrelevant. In Data-as-a-Service: Evolution  and Future Opportunities, digital consultancy D3M Labs cited two common themes driving interest in DAAS products: 


Customers are looking to their providers to supply actionable insights on data so that they can solve their problems and improve their performance. Beyond key performance indicators, customers want their providers to be partners in education, and in making better, more informed decisions.

Data literacy

The rise of the citizen data-scientist, of data visualisation, and of the resulting democratisation of insights has given a broad base of users a reason to want to attain data literacy.

“Now there is a growing importance to understand the universe of data available, as well as how to manage and build strategies for data and insight generation,” the report states. “Your customers probably don’t want to become data scientists or statisticians, but they do want to understand how to use and manage the data that is important to them professionally and personally.”

The creation of open-source technology infrastructures is enabling financial institutions to generate the same value from data as the big techs, says Bell. “The Corporate and Investment Bank at BNP Paribas has built a Big Data framework, which Securities Services can now utilise. This enables us to build, develop and create new experiences and add value for our clients in a more agile way.”

Self-service data capabilities

Among those new experiences BNP Paribas can now, crucially, offer clients self-service data capabilities, says Bell’s colleague, Daniel Doyle, manager, Investment Reporting and Performance at BNP Paribas Securities Services. “Self-service is about providing clients with the right tools to make it easier for them to extract data and generate the information they need.”

To do this effectively, securities servicers must understand individual clients’ businesses and how they work. Once a servicer gains a deeper perspective on a client’s business, they can provide more intelligent, value-added services on top of the DAAS self-service capabilities.

Self-service is not about the client doing all the work. Rather, it empowers them with control and the capability to gain greater and  more personalised real-time insight. A dimension of this is the co-creation opportunities that exist for banks to develop bespoke self-service capabilities with strategically important securities services clients. For example, using machine learning, a securities servicer could automatically generate the regulatory output an investment management client would require for specific investments.

“We can say to a client, ‘We know you are investing in your AI capabilities to achieve these regulatory outputs, but we can take that burden off your hands, and what’s more deliver additional value-added intelligence.’ By taking advantage of our significant investment and advances in this space, we can not only support our clients’ data reporting requirements but also make suggestions as to additional data services that would add value to their firm,” says Doyle.

A service encompassing both self-service and intelligence

Traditionally, asset servicers have delivered many services to clients, including middle-office functions and fund accounting. The medium through which those services are delivered is data, says Bell. “We are now creating a service in its own right, based on data. It encompasses everything about self-service and intelligence.”

DAAS has strong parallels with asset servicers’ traditional outsourcing offerings: it facilitates cost reduction, increases revenues, and allows for greater risk governance, future-proofing and scalability. The key to success, says Doyle, is to implement the right governance and framework structures. “Firms mustn’t take siloed services and create more silos in a big-data framework. They must bring data together and standardise it, prioritising different data needs and finding synergies to create new data services from a single governance framework.”

The main driver behind DAAS and self-service is clients who have lost their patience with current practices, according to Dayle Scher, senior analyst at Tabb Group. “Clients want everything in the one place so they can understand all of their exposures,” says Scher. “They don’t want to wait for their asset servicer to pull data from different systems. They want to be able to log in, pull data and create their own reports. They want data and they want it now.”...

Meeting these demands is challenging, however. Until recently, most custodians maintained multiple databases for clients, usually separated by asset class. It was rare for them to give clients access to a centralised database of publicly traded and alternative investments. Building a business intelligence layer to sit on top of the legacy databases enables asset services to provide consolidated reports to clients.

The downside of this, says Scher, is that most asset managers have multiple banks, and must access different systems and formats to gain an insight into their data. The aggregation of client data across providers will be “inevitable”, she says, as asset managers and owners demand a single source of data for their investments.

Aggregated DAAS will be a focus for BNP Paribas Securities Services, says Bell. “Today, clients get data from separate sources, which is difficult to pull together. They could aggregate it themselves, but the trend will be towards institutions such as us taking data from other financial institutions, standardising it and enriching it with additional market data, applying technologies such as machine learning and artificial intelligence, and delivering it with a powerful UX interface.”

DAAS in asset servicing has taken a similar trajectory to traditional outsourcing. In other words, clients want to know what can be commoditised and what should stay in-house, says Bell. “Today, our clients are looking to extract value out of data, so they can create a differential in their decision-making versus their competitors. They are asking whether they need to invest in a big-data framework, in machine learning, artificial intelligence and data scientists to stay competitive.”

Asset servicers such as BNP Paribas Securities Services can do the “heavy lifting”, says Bell, enabling clients to focus on generating alpha. “Asset servicers can offer all the technology and data, both structured and unstructured, so that clients can work on that data – probably with their own data scientists. They won’t need to make the huge investments that are required in a big-data infrastructure. We aren’t quite there yet, but that is definitely the direction of travel.”

Read the full article: