TECHNOLOGY: DATA ANALYTICS
Enter the Data Quality Platform and the Data Factory.
The Data Quality Platform Targets were set to increase data quality transparency and reduce ad-hoc adjustments for critical processes such as credit risk, balance sheet and liquidity management. This was something of a mammoth task given the Corporate Bank’s multinational and multidivisional structure where data is captured differently across a number of systems. It was agreed that a successful solution had to (in addition to meeting regulatory requirements): • Create a positive experience for the Corporate Bank’s internal customers;
• Improve data quality to reduce ad-hoc adjustments;
• Deliver significant operational efficiencies; • Enhance decision-making across financial and non-financial risk; and
• Enrich customer and risk insights that improve new products and services.
The first phase of getting the Data Quality Platform off the ground was to understand the data issues raised by the following areas of the Corporate Bank: • Businesses (for example Cash Management, Trade Finance and Lending, Securities Services); and
• Finance, Treasury and Risk support functions (consumers of data).
When issues raised by these two groups are resolved, this reduces problems raised by external auditors.
Quality issues discussed included: • Timeliness – was the data available when needed?;
• Completeness – did it fulfil expectations on
comprehensiveness? Did the user receive all the transactions in an ‘End of Day’ file? Were all the required fields/attributes populated?; and
• Accuracy – how does the data reflect reality? Is the client on a transaction the right client?
Once in place, this phase one technology (DQ Direct) provided the ability to report data issues for further analysis, theming and remediation.
The second phase of the Data Quality Platform was the inclusion of detective controls or ‘Data Contracts’ that act as a bridge between the Corporate Bank (the data producer) and the consumers (the Group’s Treasury, Risk and Finance teams). Launched in late 2019, this proactively used governance tools to identify and help remediate potential quality issues – going back to Gleason’s two-pronged vision of securing defensive and proactive data.
The Data Factory Data lives in the applications where it is first collected and used, generated and utilised by the businesses processing cash and performing transactions. But the secondary usage of the data for analytics and financial reporting is just as important.
When pulling data from multiple countries and systems, there could be local nuances to the way that data has been captured and stored. Codes used to describe account statuses might not be universally agreed and there could be different business rules on how the data is captured. While this works perfectly in those systems at a local level, the difficulty arises when the data scientist tries to aggregate it globally. At this point, universal definitions are needed – for example, what an active account looks like, or what channel is acceptable for onboarding a client.
A data-driven culture is about upskilling and making data a core part of everyone’s role
Tom Jenkins, Group Head, Data Quality & Governance, Deutsche Bank
The Corporate Bank’s Data Factory is a work in progress, configured as a hybrid solution with components housed on the physical Deutsche Bank platform, as well as components built on the public cloud. “This cloud capability is critical. Hosting data in the cloud would allow us to grant our clients access to analytic workspaces where we can collaborate with them,” says Gleason.
Towards competitive advantage “Our strategy is to provide and use the right common data, skills and tools for everyone
Visit us at
flow.db.com
Offence Data analytics
Figure 2: Offensive and defensive data strategy
Both The Data Factory
Defence Data quality
and governance
Source: Deutsche Bank
to make decisions and enable innovative solutions that create value for clients,” says Deutsche Bank’s Tom Jenkins, Group Head, Data Quality & Governance. We will achieve this, he says, by making it possible to find, access and use data in a secure and governed manner.
“This is what we mean by ‘democratising data’; we make data a core part of everyone’s role, not just the data scientists. We ensure the right people in the right roles have access to the right data to carry out their work. This is what will give us the competitive edge, supported by advanced cloud capabilities with our partner Google, heralding a new age of data-centric client products and services.”
Cian OMurchu is Head of Data Control Solutions at Deutsche Bank
Sources 1
See
https://bit.ly/2MlPmGd at
ec.europa.eu 2 See
https://mck.co/3jfr0d9 at
mckinsey.com 3 See
https://bit.ly/2YCDD8y at
cib.db.com 4 See
https://bit.ly/2MTaqDz at
cib.db.com 5 See
bis.org/publ/bcbs239.pdf
6 See
https://bit.ly/2Mn7dN3 at
flow.db.com 7 See
https://bit.ly/39Hl21l at
forbes.com
87
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36 |
Page 37 |
Page 38 |
Page 39 |
Page 40 |
Page 41 |
Page 42 |
Page 43 |
Page 44 |
Page 45 |
Page 46 |
Page 47 |
Page 48 |
Page 49 |
Page 50 |
Page 51 |
Page 52 |
Page 53 |
Page 54 |
Page 55 |
Page 56 |
Page 57 |
Page 58 |
Page 59 |
Page 60 |
Page 61 |
Page 62 |
Page 63 |
Page 64 |
Page 65 |
Page 66 |
Page 67 |
Page 68 |
Page 69 |
Page 70 |
Page 71 |
Page 72 |
Page 73 |
Page 74 |
Page 75 |
Page 76 |
Page 77 |
Page 78 |
Page 79 |
Page 80 |
Page 81 |
Page 82 |
Page 83 |
Page 84 |
Page 85 |
Page 86 |
Page 87 |
Page 88 |
Page 89 |
Page 90 |
Page 91 |
Page 92 |
Page 93 |
Page 94
