Figure 1: Making data an asset and driving value for our clients
Data as an asset Data centric
One copy of data, many applications
A data-centric architecture
establishes shared data models with data asset management standards in the cloud, and enables disparate teams to
work on the same structured and semi-structured data.
Secure and governed access A set of data sets governed with a secure entitlements engine and data catalogue.
Source: Deutsche Bank Democratic
Accelerated innovation
Accelerated innovation A modern data
architecture supported by advanced cloud
capabilities facilitating the delivery of data-centric client products.
including the development of standards, patterns and guidelines to support the migration of data assets to the cloud, as well as a content platform and dashboard that users could easily navigate.
• Data quality. This meant ensuring that the data flowing through the bank was of high quality, with controls in place so that any problems are addressed at source rather than downstream.
• Data lineage and lifecycle management. The ability to see what status the data flows are at for critical applications and reporting outcomes. Is the data ‘as built’? Or is it awaiting an update?
• Data analytics. This involved the establishment of a group-wide data portfolio for a consolidated view on all data-relevant projects (including and beyond governance projects), development of best-in-class privacy tools, right- time analytics and real-time processing capabilities to support priority business- use cases such as payments innovation, transaction monitoring, know your customer and 360 view of the customer, all of which is underpinned by best-practice frameworks and standards.
90%
of the top 50 banks around the world are using advanced analytics
(McKinsey) 86
Corporate Bank implementation When David Gleason joined Deutsche Bank as the Corporate Bank’s Chief Data Officer in September 2018, not only had the Data Quality Platform got under way, but at the same time there was a wider bank data governance overhaul where the Chief Data Office was setting up and driving some of the tooling and processes. “A lot of work was in place around understanding the data requirements of risk, finance, the treasury and anti-financial crime, making sure we knew where to get that data and putting controls around it,” he reflects. “My goal coming in was not only to continue and grow that work, but also to introduce the ‘offensive’ (proactive) side of the equation to that,” (see Figure 2 on page 87). This meant taking everything the bank had already been doing around data and
process improvement and using this to create better capabilities.
In terms of maintaining data integrity and quality, a focus confined to what Gleason calls “defensive problems” means that “once the fires are out there is less incentive for investment”, kicking off a feast and famine cycle.
In this defence-only world, every time a data analyst performs a new analytic capability they have to solve all the problems associated with obtaining, understanding and preparing the data needed for that enquiry. Then another analyst will come along with a different enquiry calling on the same dataset and have to tidy up all over again. Gleason cites the received wisdom that the average data scientist across all industries spends 85% to 89% of their time finding and fixing data, “with only 5% of the time spent on the tasks you are paying them for”.7
His vision for a balanced offence-defence approach is to harness all the work that goes into “defensive” data governance and make it available for proactive analytics work. This would mean less time is spent solving the same problems in isolation over and over again and the investment is channelled into repositories and architecture that works proactively as well.
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