“The rising demand for data analytics and the need to increase automation using AI/ML technologies mean there has never been a greater need for having a solid foundation of accurate and timely data in order to ensure accessibility, consistency, and �������ܰ�������”.&�Բ�����;      

On 29th April, Kieran Seaward, Head of Sales took part in a panel discussion entitled “Future proofing your data strategy for today’s complex regulatory environment” at the Data Management Summit 2021. He spoke alongside Malavika Solanki, Member of the Management Team, DSB (Derivatives Service Bureau), Robert Wallos, Chief Innovation Officer, West Highland, Haider Mannan, Regulatory Specialist, SIX and Phillip Dutton, Co-CEO and Co-Founder, Solidatus. 

The panel gave the contributors the opportunity to explore:  

– Reviewing the regulatory landscape and regulations causing the biggest data management challenges 

– Approaches to solving these data management problems 

– How new and emerging technologies like AI/ML, business process automation and cloud can help 

– �³󲹳�’s next on the regulatory horizon and how financial institutions can prepare 

We caught up with Kieran about the panel and delved into the critical topic of fully understanding the need to future proof your data strategy for a complex regulatory environment.  

To start us off, if you had to name one regulation that is presenting your clients with the biggest data management challenges, what would it be? 

Firstly, I would like to point out that I have seen a positive trend in the market over the past year with organisations building foundational data management and governance from the ground up for the right reason, not just reacting. Regulatory compliance is often one of the outcomes but is no longer the sole driver, which I believe is a positive shift moving forward. 

Some of our clients are still wrestling with the challenges associated with risk data aggregation, in fact, we are working with Phil Dutton and the team at Solidatus on a joint opportunity in Asia where the program driver is explicitly BCBS239.  

However, I would say the regulation that is still creating the biggest data management challenges is Know Your Customer (KYC).   

The identity verification requirement of KYC is still a hugely manually intensive process that suffers from a lack of automation, which means it is not only expensive but also prone to significant ‘fat-finger’ related data quality errors. Firms are handling multiple sources of corporate entity information, for example: internal legal entity master, vendor sources (D&B, BvD) and company registration info such as UK Companies House, all of which have slightly different versions of the truth. Not to mention the hierarchies associated with those organisations. 

�³󲹳�’s next on the regulatory horizon and how should financial institutions prepare? 

The Bank of England, PRA and FCA are conducting public forums on the safe adoption of AI & Machine Learning, discussing topics such as ethics, explainability and automated decision making. It will be interesting to see what develops out of that. (Our colleague Fiona Browne is a founder member of this Forum). 

The regulatory reporting landscape associated with ESG is getting increasingly complex and demanding. 

How can firms prepare? They must build foundational management practices and governance programs from the ground up. This will make organisations better equipped to adapt to new regulatory demands and reporting standards. And it’ for more than just regulatory requirements; the rising demand for data analytics and the need to increase automation using AI/ML technologies mean there has never been a greater need for having a solid foundation of accurate and timely data, in order to ensure accessibility, consistency, accuracy and – probably most importantly – build trust in the data both internally and externally.  

This will also make firms seek to more easily integrate alternative data with their internal data and systems; a “bottom-up” preparation and standardisation of this information is a key thing that firms can do to prepare. 

To have further conversations about future-proofing your data strategy for today’s complex regulatory environment, reach out to , Head of Sales. Or, if you wish to find out more about how Ģ������Ƶ can help you future proof your data strategy, you can book a one-to-one consultation with Kieran Buchanan.  

Click for more by Kieran Seaward, or find us on ,  or  for the latest news from Ģ������Ƶ.   

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Yesterday, 28^th April, Kieran Seaward, Head of Sales took part in a panel discussion entitled ‘Establishing a strong data management foundation for trusted and connected data to drive science and AI’. He spoke alongside Andrea Smith, BNY Mellon; Stan ��›Ჹ������, Invesco US; Malavika Solanki, The Derivatives Service Bureau, and Robert Wallos, West Highland. The panel was then moderated by Niresh Rajah. 

The panel gave the contributors the opportunity to explore: 

– The role of data lineage and governance in sourcing, mapping, and managing alternative and unstructured data. 
– How to apply AI/ML and data discovery tools to help monitor, automate, and discover new data assets, enrich the existing data foundation, connect data silos, manage privacy risk. 
– How to ensure high quality and trusted data across the organisation. 
– How to pull these disciplines together to drive data science and AI/ML and deliver business results. 

We��caught up��with Kieran about the panel and delved into��the critical topics��of��building confidence in the quality of the data,��data management and the challenges of unstructured and alternative data.��

To kick off, Kieran, business users are reliant on high quality data in order to generate insights. How can we trust the quality of our data and how are you building confidence in the quality of your data? 

A number of our client firms have implemented what we term a ‘Data Quality Firewall’ to ensure the integrity of information at ingestion stage, typically from regional or divisional offices, from external partner providers or from vendors. 

Regarding ‘building trust or confidence’, I would say measure it from the bottom up using industry standard dimensions of data quality, and in doing so involve data stewards and subject matter experts from the various business lines for two reasons: 

1) They will provide the business context and understanding of the data to help define the rules to measure the data; 

2) They will also come along as part of the journey and fully understand the reasons for doing this, making it easier to help them ‘buy-in’ to the process. 

Once that accurate measurement is in place, you can provide data stewards and business users with a live picture of the quality of their data via data quality dashboards – again building confidence. You can go one further than just reporting on data and empower these users to remediate / correct the data for which they are responsible. 

This all takes time, but it does work in building confidence in the data at all levels of the organisation. 

What are the challenges of unstructured and alternative data, and how can they be integrated into data platforms and used beneficially by the business? 

Firms in the alternative investment space such as Private Equity and Hedge Funds absolutely rely on alternative data as the basis of some of their research. They need to enrich their standardised internal data with alternative data sources. As an example – getting an accurate picture of unlisted firms by utilising company registration office data, such as UK Companies House, or vendor sources such as Pitchbook / Crunchbase. No common identifier exists so you have to map or match that data by different means. This is still proving a major challenge for these firms. In our experience, a centralised approach to standardisation rather than Data Scientists in different areas of the business doing their own thing enables / ensures a harmonised view across the enterprise. 

Of course, the accuracy of alternative or unstructured data can be questionable. Anyone who has worked with UK Companies House data will testify to that! However, the benefits of the insights you can generate from these sources is worth overcoming the technical and reliability challenges associated with integrating them with your internal data / systems. Unified, connected, harmonised data is the panacea. 

To have further conversations about building confidence in the quality of your data and understanding fully the challenges of unstructured and alternative data, reach out to , Head of Sales. If you wish to find out more about how Ģ������Ƶ is using Artificial Intelligence to achieve faster and more accurate analytics, you can reach out directly to Dr , Head of Artificial Intelligence.  

Click��for more on Data Management, or find us on��,����or����for the latest news from Ģ������Ƶ.����

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