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Brace for the New Wave of Today’s Fintech Ecosystem Powered by DataOps

By Sharon Bell posted 10-22-2019 09:00:00 AM

Banks need to speed their move into
Brace for the New Wave of Today’s Fintech Ecosystem Powered by DataOps
the digital future. But organizations must get a better handle on their data in order to anticipate the needs of customers, make banking easier, and pursue the right partnerships to increase capabilities and scale.
This article was originally published on the Delphix website here August 13, 2019.

Peek into the future of banking, and you’ll see the emergence of more fintech startups disrupting the banking industry with new digital services. By and large, traditional banks have held onto their market share, but global investment in fintech ventures has tripled to nearly $3 billion since 2008.

As hundreds of fintech startups work on bringing brand-new alternatives to long-established banking services, a diverse ecosystem of traditional and non-traditional players is transpiring. But while the benefits of emerging technology have never been greater, large behemoth companies struggle with the mishmash of legacy systems and tools that prevent them from innovating at the speed of challenger brands. 

True disruption in banking can only happen when organizations are willing to challenge their own business models and adopt a modern approach to developing a winning digital strategy.  So how should the incumbents position themselves in the new landscape? Traditional banks must redefine how they leverage data to build better software. 

A Battle Royale in Banking

Many banks have already entered into partnerships with fintech companies. The relatively low cost and fast access to cloud environments have allowed hundreds of challenger banks and non-financial companies to quickly bring banking solutions to market. Examples include Venmo’s peer-to-peer payments and Uber’s full-service banking app for drivers. For heavily regulated incumbent banks, collaborating in the fintech ecosystems while complying with data privacy regulations can be a daunting task.

Fintech startups focus on developing innovative, customer-oriented solutions that include a variety of services, including payments, peer-to-peer lending and trading, mobile banking, asset management systems, partner banks, integrators, aggregators, and more. They bring disruptive technology solutions to the ecosystem while established banks provide market expertise and a customer base. As a result, the collaboration can strengthen the competitive position of established banks by shortening the time it takes for new products and services to reach their customers, while the startups benefit from greater access to market expertise.

Cloud Ecosystem in Banking
Example of bank ecosystem (Source: Oliver Wyman)

The Imperative: Banks Must Act Quickly

In order for fintech and traditional banking to collaborate and build mutually beneficial partnerships, all members of the ecosystem need a way to share data for developing and testing innovative applications in a fast, secure manner.  

Take, for instance, a Silicon Valley-based commercial bank that wanted to better compete with technology-centric companies, including Square and Apple. In order to gain a competitive edge, the bank needed to migrate application development and testing from a 15-year-old legacy system to a hybrid-cloud environment that included AWS. They adopted a dynamic data platform that allowed its software development teams to virtualize data and mask sensitive information, working in tandem with DevOps tools like Jenkins, to achieve cloud agility and easily share data for development and testing with their partner ecosystem, including fintech software developers, credit providers, and payment processors.

But as enterprises strive to store and process greater volumes of data across multiple data platforms and multiple locations – both on-premises and in the cloud – the complexities of data management and data security increase.

Adopting A New Way of Managing Data 

Most banks will need to aggressively adopt new technology paradigms of digitally native companies, and this is certainly true when it comes to data. DataOps, which is the rapid, automated, and secure management of data, can enable banks and financial services organizations to collect, virtualize, manage, and provision terabytes of data on-demand. While DataOps was only introduced to Gartner Hype Cycle last year, the trend is moving fast as more global enterprises look to significantly increase investment in DataOps technologies.

This modern approach reduces the complexity of data provisioning and empowers developers with self-service access to data, accelerating the development of data-driven applications and data-driven decision-making to meet the rapidly changing requirements of the business.

Because not only is the use of legacy technology and processes slowing banks down, but outdated IT systems can pose a significant security challenge, especially if the software is no longer supported by the vendor. The onus is on the bank to fix security holes, and this is a huge burden on the business as cyber threats advance faster than security teams’ ability to detect and resolve issues. Meeting regulatory requirements, such as the GDPR and CCPA, also require that the technology be supported or face fees and penalties if you experience a data breach and your software out of compliance.

In other words, as your business grows, you’ll need to evolve your technology stack because your software must be able to keep up without inhibiting business scalability, growth, and regulatory compliance. 

While new technologies provide rapid data transformation, here are two critical aspects of an effective DataOps platform: 

  • Self-service access to personal data environments that are available in minutes with full control over bookmarking, branching, sharing, refreshing, and restoring data. 
  • Integrated masking capabilities to identify sensitive information, such as names, addresses, and credit card numbers contained in the data and automatically mask the information according to security policies and data privacy regulations.

Data Masking Example

The cloud increases agility of data sharing throughout the fintech development ecosystem, and a DataOps platform can facilitate the management and securing of data in a cloud environment. Because today, a data breach is among the worst fears of any bank CEO. With the steady stream of high-profile breaches, like the latest news with Capital One, it’s imperative that banks sustain and even accelerate the pace of innovation while balancing that with data security and privacy regulations. Considering that a majority of sensitive data is contained throughout non-production development and testing environments, ensuring that customer information is always obfuscated in this data, has a tremendous impact on reducing risk.  

The old-fashioned way of offering financial services is no longer an option. Digital is undoubtedly changing customer expectations and behaviors, and an ecosystem that allows customer data to be shared with both external and internal parties in a fast, secure way will ultimately facilitate the best customer experience and engagement. In order words, when incumbent brands can create an innovation engine through DataOps, they’re better positioned to take advantage of their wealth of expertise, industry knowledge, and treasure trove of data to seek out new markets, business models, and partnerships in today’s digital world. 

Download our customer success story booklet to learn how financial services organizations, including Fannie Mae and Metro Bank, are driving data agility and data security in development, testing, fraud detection, and data integration. 

1 comment



11-19-2019 11:37:45 PM

hi team, i have a general question about delphix.

1. I undestand,  because Delphix provides VDB option, it gives a capability to capture data for a timeframe ,but does it do subsetting??? 
by subsetting i mean if i want to create a vdb from Source giving some conditions in select query, does delphix do that? if yes,  please share the link where i can see how. 

2. dsource is full copy of production data, how about dsource to vdb, is it full copy always or i can take some part of data in VDB to load in target environment.

3. if VDB creation is a substitute to subsetting a chunk of data to put it in target environment, can we say this this feature itself is subsetting in VDB??