�鶹������Head of AI,��,��recently joined a panel at Data Management Summit USA Virtual discussing the science of data discovery and the art of implementation of business value. This panel sought to unpack the greatest data discovery challenges that have surfaced as a result of increased data volumes,��and financial institutions making more use of cloud environments.����

Fiona was joined by��,��,��, and .��

The��panellists��focused on:��

• The��merits of��leveraging external data management tools��
• What exactly a robust DQ program or tool can do��for��your data��
• The importance of creating a golden record or single version of the truth, and��
• Implementing��applications of AI/ML to automate some of the data discovery processes��to��generate better quality data across the enterprise.��

Key takeaways from the discussions were:��

1. There are many benefits of data discovery��

At���鶹����, Fiona pointed out that we often see a spectrum of capabilities in terms of where institutions are in their maturity and data journey.��Fiona stated that��data discovery helps at all stages of implementing a Data Governance Framework, uncovering relationships and making use of things like metadata��catalogs, but that��at all times��it is vital to understand the importance of embedding context��to aid downstream applications and use cases.��

Fiona said that��it is vital to consider where you are with your Data Governance Framework, asking yourself the question: are you at the start of your journey,��or are you well established within it?��Do you have ownership and responsibility of the data, an understanding of security and accessibility; do you fully understand the regulations, legal implications and security of your data?����

Data discovery is a key tool to be��used at all times��in these processes, enriching data with context that will help in the long run, whether mature or just starting out in Data Governance.��

2. Leading organisations are embedding a��culture of data responsibility and understanding within an organisation.����

These organisations��have��C-Suite executives right��through��to data engineers on the technical side��all engaged on their roles and requirements when it comes to data as a prized business asset.��

Firms looking to exploit this will need to focus on��skilling up and training because this area is constantly changing, regulations are updating and adapting. For��example,��Fiona mentioned the new AI regulations from the EU that are coming down the line. This will require keeping an eye on best practices.����

3. Security is always moving!��

Security is always moving and keeping up to date has never been more important. Accountability comes from assigning those roles��and key responsibilities so that the key owner of this one single data state is known.��There are many policies around data privacy, ethics and security,��and of course quality so having a holistic view of those and then finishing off with best practices��that then highlights��well��in terms of having your standardised data model across a company.��One example is in��building data catalogues, where��it is important to categorise your data both in terms of risk and regulation.��

4. There is a��wide range of��tools��and solutions that can help with data discovery��

It depends on where you are on your data discovery��and��how much expertise and technology you have in house. Fiona stated there is a real range from data lineage and visualisation tools. There is also a lot of evidence that these tools are being augmented, for example, data lineage is being augmented with data quality metrics as it flows through an institution��and��technology to aid for example with metadata management.����

The rise in ‘no-code’��platforms, such as���鶹����’ Self-Service Data Quality,��helps��subject matter experts with things like measuring and monitoring their data quality, improving their data quality right through to things like graphs and graph analysis to uncover potential links and��relationships between data as systems get larger and more complex by nature.��

Fiona rounded off the discussion by stating��that it is extremely encouraging��to see us moving along in terms of augmenting the data discovery process with Machine Learning. This will��address those��more��manual,��time-consuming��tasks��that data stewards, analysts and business users end up saddled with.������

About Self-Service Data Quality��

Our no-code��platform allows users to create complex rule logic with a drag and drop interface with no specialist programming skills required.���鶹������Self-Service Data Quality platform empowers business users to self-serve for high-quality data, saving time, reducing costs, and increasing profitability.��

In the highlight video below, you can watch Fiona discuss��these areas in greater depth.

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Yesterday, CTO at �鶹���� discussed the role that data quality management and governance play in delivering well-structured and de-duplicated information into business analytics alongside Sarah Belsham, RSM; Suemee Shin, Northern Trust Corporation and Martijn Groot, Alveo Technology.  

During the session there was one particular poll asking: 

‘What are the challenges of achieving accurate, timely, and meaningful data analytics in your organisation?’ 

The results of the poll reiterated that 72% of people claimed that legacy systems and data siloes were a key challenge behind achieving accurate, timely and, meaningful data analytics. 61% of people claimed poor data quality is one of the key challenges behind achieving accurate, timely, and meaningful data analytics.

The discussion saw agreement in the fact that organisations believe there is a danger that the ‘Big Data Concept’ can get you lost, and thus are adopting more strategic data approaches, leading to being more selective about what data they are bringing in. Overall, the panel agreed that there needs to be a bigger focus on aligning data to strategic objectives.  

‘Data driven strategic approach is what we are seeing organisations employing’, Sarah Belsham, RSM. 

Sarah unpacked that historically there have been a lot of IT led projects that are not always as successful as they should be, as there isn’t business buy in. Increasingly in the last 12-24 months, however, there has been a trend that stakeholders are calling the shots and being much more involved. There now is a far more prominent emphasis on business stakeholders commenting and continually improving models and outputs. 

The panel reiterated that there must be a focus on 3 aspects when seeking to optimising the role of data quality and data governance: 

The technology –  

• Do you have have a data repository in place?  
• Does it include what you need?  
• How do you get the data out? 
• If you don’t have a data warehouse what is your IT strategy? 

The people required –  

• What tool is right for the organisation?  
• What is the internal data literacy like?  
• Is a centralised model the way forward? 

The process in place –  

• How well governed is your business process?  
• How can you access your data? 
• Do you require more central coordination to ensure you set up the data in the right way to make sure people aren’t making poorly informed decisions from your data? 

Alex Brown, CTO at �鶹���� presented three major key takeaways: 

• Data Quality doesn’t have one level. It is vital to understand that data governance and data quality are different but when you have them both together you have a very powerful tool. 
• Self-Service data quality is essential to a business & data aligned strategy – which, funnily enough, is what we at �鶹���� specialise in! Self-Service data quality empowers data stewards to measure the data themselves without the bottleneck of requiring IT function. 
• There will be tighter regulation across AI in the not-so-distant future. On a business level, you want to show a business user why a business decision has been made. (for more reading on this, please see commentary on the ). 

If you want to discuss further how to optimise the role of data quality and data governance, reach out to  

�鶹���� adds rapid value to any data strategy programme. Our home-grown self-service software features many hundreds of thousands of person-hours of development in the fundamentals of data quality and matching. We provide fast results, full training, and guidance, empowering business users who understand the data to measure, fix, and fully leverage their data assets.  

 Our platform empowers business users to self-serve for high-quality data, saving time, reducing costs, and increasing profitability. �鶹���� solutions are customisable and built on our award-winning software platform. If you want to discover how we can accelerate your data quality journey, reach out to  

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Join us at DMS USA Virtual 2021!

The Data Management Summit USA Virtual brings together the North American data management community to explore the latest challenges, opportunities and data innovations facing sell side and buy side financial institutions.

We’ll explore how data strategy is evolving to drive business outcomes, support agility and speed to market in changing times. The challenges of managing ever increasing data volumes, unstructured, alternative and completely new data sets like ESG, plus the mainstream adoption of cloud, AI and ML technologies, require new approaches, capabilities and tools for data management. Join us online to hear from leading data practitioners and innovators who will share insights into how they are pushing the boundaries with data to deliver value with flexible but resilient data driven strategies.

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The fourth annual Global RegTech Summit was hosted between 11-13^th May 2021, as a virtual event to connect the global RegTech community. We attended the event which allowed us to hear debates and presentations from key RegTech leaders and innovators leading the shift to digital.  

As the recent by Erwin and FIMA demonstrated, one of the biggest headaches for data leaders in financial services is the sheer amount of manual work involved in Anti-Money Laundering (AML) and Know Your Customer (KYC). We took some time to put together our thoughts from the highly relevant panel discussion: Enhancing customer experience by automating KYC and onboarding.  

One of the strongest statements we heard was that “Bureaucracy is not tolerated in the same way in this day and age, especially with more advanced technological solutions”. Panellists agreed that if your business is caught up with outdated, laborious, and lengthy processes, in any context within financial services, customers will leave you. Getting that competitive edge in terms of speed and efficiency helps not only in cost saving but also with customer recruitment and retention.  

However, banks are reluctant to implement radical change in these often-outdated processes due to heavy consequences if things go wrong. It was striking that the commentators noted that tight regulations globally on compliance with AML to prevent terrorist financing and other money laundering activities mean that banks consider the risk ”just not worth taking yet”.  

All involved agreed that there needs to be more of a synergy between regulators and the financial services industry to facilitate a discussion around what best practice looks like to help improve compliance, reduce rework and – most importantly – reduce crime.  

Something that was quite surprising was to hear that it is more and more apparent that the regulators, often looked upon as being the guardians of all things virtuous and ‘error-free’, face the exact same problems as those entities they regulate: having access to good quality, standardised internal and external data. As the custodians of ‘good practice’, starting from first principles and ensuring they have accuracy, completeness, consistency, and validity associated with their data is foundational to maintaining the respect and authority in their respective industry.  

The panel ended concluding that AI and Machine Learning are a vital component in the tool to resolve the quandary. A certain level of scepticism and hesitancy certainly exists in deploying AI with sensitive data and data that is being reported to the regulator. This is why there needs to be a concentrated effort in working with the sector to ensure that Machine Learning practices are deployed in the right way, maximising accuracy and efficiency, deriving a better customer journey and having data that is accurate and reliable.  

This agrees with the findings being worked through by the AI Public Private Forum, run jointly by the Bank of England and industry, and of which our Head of AI, Fiona Browne, is a founder member. 

If you want to know more about how the �鶹���� solution can help resolve a lot of the issues with internal and external data matching for customer onboarding, then please reach out. 

For more news about �鶹���� you can visit��us here. Or find us on��,����or��.

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