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Swedbank embraces advanced analytics through new Azure-Databricks-Immuta powered platform

Gary Flood Profile picture for user gflood April 7, 2023
Summary:
The third largest Nordic bank, Swedbank, sees data access as door to a whole new way of working with information

An image of Swedbank’s head office
(Image sourced via Swedbank website)

The use of technology for improved automation and security data access is being hailed as key to a successful cloud migration at a major Scandinavian bank.

Integration with Databricks and Microsoft Azure to create a completely new EAP (Enterprise Analytics Platform) has also been key, says the internal IT team at Swedbank, for being able to adapt to new business requirements.

The combination is being hailed as allowing the organization to start building the right data analytics to drive new banking-specific machine learning models.

Largest banking group in Sweden

Swedbank says that it was able to migrate all its critical analytics workloads to a secure cloud environment in less than a year, with the first policy implemented just two months after go-live.

The data security tool has so far tagged over 100 terabytes of data across 2500-plus sources for policy authoring - and plans to integrate with an external data catalog are now seen as possible.

Headquartered in Stockholm, Swedbank is the largest banking group in Sweden and the third largest in the Nordics. It has eight million retail banking users and over 500,000 business customers.

Vineeth Menon, Head of Data Lake Engineering at Swedbank, sums up the contribution of the EAP project and says: 

The cloud migration process was a huge transformation that now allows data science and analytics teams to access the data they need when they need it.

Menon explains the business motivation for the project as stemming from the fact that older on-prem solutions were seen as too inflexible to help it curb increased operational costs.

Specifically, he says, its existing IT stack replicated data across platforms, had limited capabilities to separate storage from compute, and lacked support for deep learning and AI capabilities.

A particular problem with that last issue was that Menon and his team wanted to be able to look at data across all silos and build a new wave of analytics to drive its machine learning models.

“A solution that would enforce trust”

To remove all these limitations, Menon and his colleagues decided that it was time for a move to a new cloud-based data platform that could support deep learning, AI, secure data sharing, and fast analytics.

He says:

We needed to build a solution that would enforce trust in our security, management, and access to data internally, while protecting our customers’ assets and data.

We knew we needed to change our culture, processes, and platforms to create resilience for the future.

First step was basing all Swedbank’s IT on cloud in the shape of Azure, which would allow the compute-storage separation that was seen as so desirable.

But beyond cloud, Swedbank also wanted to unify all its previously disparate data sources into a single data lake so that analytics could be run over that data and achieve insights and speed up the time to market of potential new data-driven products.

To achieve this, Databricks was selected as the basis for a new approach to data at the company - complemented by purchase of a tool called Immuta.

This is a data security application that offers Menon a range of data discovery options like auto-classification, authentication, auditing, user, and tag syncing.

The product’s support for both role- and attribute-based access control was also key, he says.

As a result of implementing this tech, he adds, the Swedbank’s data engineering team can now run data models “compliantly and securely” on Databricks while consistently enforcing all purpose-driven access and usage.

For Menon, this is a model that is better than traditional role-based access control, enabling what he dubs “a whole new paradigm of trust” in every data-driven team in the organization. 

He adds that Swedbank wanted to end up with an easy-to-use platform with the right capabilities for different business users.

Therefore, close integration of the EAP and data access ensures the right people have the right access to the right data at the right time, he claims:

Trust is now built into our processes, data users have a purpose, and access is both need-based and secure.

He adds:

The key goal for everyone working on the Enterprise Analytics Platform is to scale this capability across the organization to enable the bank to be even more data driven.

This means, he says, ensuring and building a path on how to drive continuous value through analytics - which in turn means faster decision making and better execution.

Synergy of cloud, analytics, and data access

Tech on its own isn’t the only reason all this is being achieved, he stresses.

The new approach to data and trust was also achieved through what he calls the “full collaboration and investment” of the bank’s compliance, policy security, and technical teams.

In addition, better use of data isn’t the only positive outcome of this synergy of cloud, analytics, and data access, says the bank.

For example, Swedbank has also achieved significant process improvements and time savings, including a 3X time saved setting up data security and self-service policy authoring.

Menon claims that there has also been a doubling in data-based use cases and projects a five-fold improvement in process efficiency and a “100%” improvement in data access for compliance.

Menon sees his EAP and data access control work as central to the organization’s ongoing digital transformation—noting that better use of data absolutely complements the wider strategy.

He concludes:

The main focus for our Enterprise Analytics Platform project was to build a resilient and scalable infrastructure to enable widespread availability of advanced analytics while streamlining the analytics process to achieve operational efficiency.

That will continue to be a focus for the team this coming year.

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