Datactics has secured the ‘Most Innovative Technology for Regulatory Compliance’ award at this year’s A-Team Group RegTech Insight Awards USA.

The Belfast-based firm, which has made its name specializing in innovative solutions to challenging data problems, developed its Data Readiness solution in response to the prevalence of data-driven regulations across both sides of the Atlantic. 

Data Readiness grew out of Datactics’ close work with banking customers in the UK, primarily around data used to identify and reporting on customer deposits in line with stringent regulation from the Financial Conduct Authority (FCA) and Prudential Regulatory Authority (PRA).  

In 2024, Datactics developed Data Readiness as a specific defined solution to measure and prepare data to specific regulatory standards, offering the solution with a range of deployment options, including as-a-service.  

Behind the solution

Matt Flenley, Head of Marketing at Datactics, noted, “This award is brilliant news for all our talented developers and engineers back home. We’ve a long record of working alongside customers to help them with specific problems they’re encountering, and for which the risk of bad data affecting their ability to demonstrate compliance is often significant.

“With our experience in developing rule-sets for customers seeking to comply with UK depositor protection regulations, alongside international standards such as BCBS 239, we felt the time was right to offer this solution as its own piece of regulatory compliance technology.

“We’d like to thank the A-Team panel and all those who saw fit to recognise the approach we’ve taken here – thank you all so much!” 

Angela Wilbraham, CEO at A-Team Group, and host of the 4th annual RegTech Insight Awards USA 2024, commented, “Congratulations to Datactics for winning the Most Innovative Technology for Regulatory Compliance award in this year’s A-Team Group RegTech Insight Awards USA 2024.

“These awards celebrate providers of leading RegTech solutions, services and consultancy and are uniquely designed to recognise both start-up and established providers who are creatively finding solutions to help with regulatory challenges, and span a wide range of regulatory requirements.

“Our congratulations for their achievement in winning this award in a highly competitive contest.” 

For more on Datactics, visit www.datactics/get-data-readiness 

For more on A-Team Group, visit https://a-teaminsight.com/category/regtech-insight/  

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It’s October, the Northern Hemisphere nights are drawing in, and for many it’s time when things take a scarier turn. But for public sector leaders exploring AI, that fright need not apply to your data. It definitely shouldn’t be something that haunts your digital transformation dreams.

With a reported £800m budget unveiled by the previous government to address ‘digital and AI’, UK public sector departments are keen to be the first to explore the sizeable benefits that AI and automation offer. The change of government in July 2024 has done nothing to indicate that this drive has lessened in any way; in fact, the Labour manifesto included the commitment to a “single unique identifier” to “better support children and families”[1].

While we await the first Budget of this Labour government, it’s beyond doubt that there is an urgent need to tackle this task amid a cost-of-living crisis, with economies still trying to recover from the economic shock of COVID and deal with energy price hikes amid several sizeable international conflicts.

However, like Hollywood’s best Halloween villains, old systems, disconnected data, and a lack of standardisation are looming large in the background.

Acting First and Thinking Later

It’s completely understandable that the pressures would lead us to this point. Societal expectations from the emergence of ChatGPT, among others, have only fanned the flames, swelling the sense that technology should just ‘work’ and leading to an overinflated belief in what is possible.

Recently, LinkedIn attracted some consternation[i][2] by automatically including members’ data in its AI models without seeking express consent first. For whatever reason, the idea that people would just accept this change was overlooked. It took the UK’s Information Commissioner’s Office, the ICO, to intervene for the change to be withdrawn – in the UK, at least.

A dose of reality is the order of the day. Government systems are lacking integrated data, and clear consent frameworks of the type that LinkedIn actually possesses seldom exist in one consistent way. Already lacking funds, the public sector needs to act carefully, and mindfully, to prevent their AI experiments (which is, after all, what they are) from leading to inaccuracies and wider distrust from the general public.

Streamlining Data and Consent in One Single View

One solution is for Government departments to form one, holistic set of consents concerning use of data for AI, especially Large Language Models and Generative AI – similar to communication consents under the General Data Protection Regulation, GDPR.

The adoption of a flexible consent management policy, one which can be updated and maintained for future developments and tied to an interoperable, standardised single view of citizen (SCV), will serve to support the clear, safe development of AI models into the future. The risks of building models now, on shakier foundations, will only serve to erode public faith. The evidence of the COVID-era exam grades fiasco[3] demonstrates the risk that these models present to real human lives.

Of course, it’s not easy to do. Many legacy systems contain names, addresses and other citizen data in a variety of formats. This makes it difficult to be sure that when more than one dataset includes a particular name, that name actually refers to the same individual. Traditional solutions to this problem use anything from direct matching technology to the truly awful exercise of humans manually reviewing tens of thousands of records in spreadsheets. This is one recurring nightmare that society really does need to stop having.

Taking Refuge in Safer Models

Intelligent data matching uses a variety of matching algorithms and well-established machine learning techniques to reconcile data held in old systems, new ones, documents, even voice notes. Such approaches could help the public sector to streamline their SCV processes, managing consents more effectively. The ability to understand who has opted in, marrying opt-ins and opt-outs to demographic data is critical. This approach will help model creators to interpret the inherent bias in the models built on those consenting to take part, to understand how reflective of society the predictive models are likely to be – including whether or not it is actually safe to use the model at all.

It’s probable that this transparency in process could also lead to greater trust in the general public to take part in data sharing in this way. In the LinkedIn example, the news that data was being used without explicit consent, raced around like wildfire on the platform itself. This sort of outcome cannot be what LinkedIn anticipated, which in and of itself is a concern about the mindset of the model creators.

It Doesn’t Have to Be a Nightmare

It’s a spooky enough season without adding more fear to the bonfire; certainly, this article isn’t intended as a reprimand. The desire to save time and money to deliver better services to a country’s citizens is a major part of many a civil servant’s professional drive. And AI and automation offer so many opportunities for much better outcomes! For just one example, NHS England’s AI tool already uses image recognition to detect heart disease up to 30 times faster than a human[4] . Mid and South Essex (MSE) NHS Foundation used a predictive analytical machine learning model called Deep Medical to reduce the rate at which patients either didn’t attend appointments or cancelled with short notice (referred to as Did Not Attend, or DNA). Its pilot project identified which patients were more likely to fall into the DNA category, developed personalised reminder schedules, and through identifying frail patients who were less likely to attend an appointment, highlighted them to relevant clinical teams.[5]

The time for taking action is now. Public sector organisations, government departments and agencies should focus on the need to develop systems that will preserve and maintain trust in the AI-led future. This blog has shown that better is possible, through a dedicated desire to align citizen data and their consents to contact. In a society where people have trust and transparency in the ways that their data will be used to train AI, the risk of nightmare scenarios can be averted and we’ll all sleep better at night.

[1] https://www.ropesgray.com/en/insights/viewpoints/102jc9k/labour-victory-the-implications-for-data-protection-ai-and-digital-regulation-i

[2] https://etedge-insights.com/in-focus/trending/linkedin-faces-backlash-for-using-user-data-in-ai-training-without-consent/

[3] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7894241/#:~:text=COVID%2D19%20prompted%20the%20UK,teacher%20assessed%20grades%20and%20standardisation.

[4] https://www.healthcareitnews.com/news/emea/nhs-rolls-out-ai-tool-which-detects-heart-disease-20-seconds

[5] https://www.nhsconfed.org/publications/ai-healthcare

[i]

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The oak-panelled setting of historic Toynbee Hall provided the venue for the 2024 Datactics Summit, which this year carried a theme of ‘Data-Driven Return on Investment.’

Attendees gathered for guest speaker slots covering:

• Danske Bank UK’s Lyndsay Shields presenting a ‘Data Management Playbook’ covering the experiences of beginning with a regulatory-driven change for FSCS compliance, through to broader internal evangelisation on the benefits of better data;

• Datactics’ own data engineer, Eugene Coakley, in a lively discussion on the data driving sport, drawing from his past career as a professional athlete and Olympic rower with Team Ireland;

• and Renfrewshire HSCP’s Bobby McClung explaining how automation and the saving of person-hours or even days in data remediation was having a material impact on the level of care the organsation is now able to deliver to citizens making use of its critical services.

The Datactics Customer Awards in full

In recent months, the team at Datactics has worked to identify notable achievements in data in the past year. Matt Flenley, Head of Marketing at Datactics, presented each with a specific citation, quoted below.

Data Culture Champion of the Year – Lyndsay Shields, Danske Bank UK

“We’re delighted to be presenting Lyndsay with this award. As one of our longest-standing customers, Lyndsay has worked tirelessly to embed a positive data culture at Danske Bank UK. Her work in driving the data team has helped inform and guide data policy at group level, bringing up the standard of data management across Danske Bank.

“Today’s launch of the Playbook serves to showcase the work Lyndsay and her team have put into driving the culture at Danske Bank UK, and the wider culture across Danske Bank.”

Data-Driven Social Impact Award – Renfrewshire Health and Social Care Partnership

“Through targeted use of automation, Renfrewshire Health and Social Care Partnership has been able to make a material difference to the operational costs of local government care provision.

“Joe Deary’s early vision and enthusiasm for the programme, and the drive of the team under and alongside Bobby, has effectively connected data automation to societally-beneficial outcomes.”

Data Strategy Leader of the Year – RBC Brewin Dolphin

“RBC Brewin Dolphin undertook a holistic data review towards the end of 2023, culminating in a set of proposals to create a rationalised data quality estate. The firm twinned data this strategy with technology innovation including being early adopters of ADQ from Datactics. They overcame some sizeable hurdles, notably supporting Datactics in our early stages of deployment. Their commitment to being an ambitious, creative partner makes them stand out.

“At Datactics we’re delighted to be giving the team this award and would also like to thank them for being exemplars of patience in the way they have worked with us this year in particular.”

Datactics Award for Partner of the Year – Solidatus

“Solidatus and Datactics have been partnered for the last two years but it’s really in 2023-2024 that this partnership took off.

“Ever since we jointly supported Maybank, in Malaysia, in their data quality and data lineage programme, we have worked together on joint bids and supported one another in helping customers choose the ‘best of breed’ option in procuring data management technology. We look forward to our next engagements!”

Datactics Data Champion of the Year – NHSBSA

“For all the efforts Tony, Nick and team have made to spread the word about doing more with data, we’d like to recognize NHS Business Services Authority with our Datactics Data Champion of the Year award.

“As well as their advocacy for our platform, applying it to identify opportunities for cost savings and efficiencies across the NHS, the team has regularly presented their work to other Government departments and acted as a reference client on multiple occasions. Their continued commitment to the centrality of data as a business resource is why they’re our final champions this year, the Datactics Data Champion 2024.”

Gallery of Winners

Toasting success at Potter & Reid

The event closed with its traditional visit to Shoreditch hot spot Potter & Reid. Over hand-picked canapés and sparkling drinks, attendees networked and mingled to share in the award winners’ achievements in demonstrating what data-driven culture and return on investment looks like in practice. Keep an eye out for a taster video from this year’s event!

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What is Data Quality and why does it matter?

 

Data Quality refers to how fit your data is for serving its intended purpose. Good quality data should be reliable, accurate and accessible. 

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Our quick ADQ in three minutes guide can be downloaded from the button below the graphic. Happy reading! As always, don’t hesitate to get in touch if you’re looking for an answer that you can’t find here. Simply hit ‘Contact us’ with your query and let us do the rest.

Wherever you are on your data journey, we have the expertise, the tooling and the guidance to help accelerate your data quality initiatives. From connecting to data sources, through rule building, measuring and into improving the quality of data your business relies on, let ADQ be your trusted partner.

If you would like to read some customer stories of how we’ve already achieved this, head on over to our Resources page where you’ll find a wide range of customer case studies, white papers, blogs and testimonials.

To get hold of this infographic, simply hit Download this! below.

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Tia Cheang, Director of IT Data and Information Services at Gallagher, recently delivered an interview with Tech-Exec magazine drawing from her knowledge and experience in shaping the future of the insurance industry at one of the world’s largest insurance brokers. You can read the article here.

Tia is also one of DataIQ’s Most Influential People In Data for 2024 (congratulations, Tia!). We took the opportunity to ask Tia a few questions of our own, building on some of the themes from the Tech-Exec interview.

In the article with Tech-Exec, you touched on your background, your drive and ambition, and what led you to your current role at Gallagher. What are you most passionate about in this new role?

In 2023, I started working at Gallagher after having an extensive career in data in both public and private sectors. This job was a logical next step for me, as it resonates with my longstanding interest in utilising data in creative ways to bring about beneficial outcomes. I was eager to manage a comprehensive data transformation at Gallagher to prepare for the future, aligning with my interests and expertise.

I am responsible for leading our data strategy and developing a strong data culture. We wish to capitalise on data as a route to innovation and strategic decision-making. Our organisation is therefore creating an environment where data plays a crucial role in our business operations, to allow us to acquire new clients and accomplish significant results rapidly. The role offers an exciting opportunity to combine my skills and lead positive changes in our thinking towards data and its role in the future of insurance.

The transition to making data an integral part of business operations is often challenging. How have you found the experience? 

At Gallagher, our current data infrastructure faces the typical challenges that arise when a firm is expanding. Our data warehouses collect data from many sources, which mirrors the diverse aspects of our brokerage activities. These encompass internal systems, such as customer relationship management (CRM), brokerage systems, and other business applications. We handle multiple data types in our data estate, ranging from structured numerical data to unstructured text. The vast majority of our estate is currently hosted on-premise using Microsoft SQL Server technology, however, we also manage various other departmental data platforms such as QlikView. 

“…we want data capabilities that provide flexibility and agility, to enable us to quickly react to new market opportunities.”

A key challenge we face is quickly incorporating new data sources obtained through our mergers and acquisitions activity. These problems affect our data management efforts in terms of migration, seamless data integration, maintaining data quality, and providing data accessibility. 
To overcome this, we want data capabilities that provide flexibility and agility, to enable us to quickly react to new market opportunities. Consequently, we are implementing a worldwide data transformation to update our data technology, processes, and skills to provide support for this initiative. This transformation will move Gallagher data to the cloud, using Snowflake to leverage the scalability and elasticity of the platform for advanced analytics. Having this flexibility gives us a major advantage, offering computational resources where and when they are required.

How does this technology strategy align with your data strategy, and how do you plan to ensure data governance and compliance while implementing these solutions, especially in a highly-regulated industry like insurance?

Gallagher’s data strategy aims to position us as the leader in the insurance sector. By integrating our chosen solutions within the Snowflake platform, we strive to establish a higher standard in data-driven decision-making. 

This strategy involves incorporating data management tools such as Collibra, CluedIn, and Datactics into our re-platforming efforts, with a focus on ensuring the compatibility and interoperability of each component. We are aligning each tool’s capabilities with Snowflake’s powerful data lake functionality with the support of our consulting partners to ensure that our set of tools function seamlessly within Snowflake’s environment.

“…we are contemplating upcoming AI and automation regulations and considering how to futureproof our products and approaches…”

We are meticulously navigating the waters of data governance and compliance. We carefully plan each stage to ensure that all components of our data governance comply with the industry regulations and legislation of the specific region. For example, we are contemplating upcoming AI and automation regulations and considering how to futureproof our products and approaches to comply with them.

The success of our programme requires cooperation across our different global regions, stakeholders, and partners. We are rethinking our data governance using a bottom-up approach tailored to the specific features of our global insurance industry. We review our documentation and test the methods we use to ensure they comply with regulations and maintain proper checks and balances. We seek to understand the operational aspects of a process in real-world scenarios and evaluate its feasibility and scalability.

Could you expand on your choice of multiple solutions for data management technology? What made you go this route over a one-stop shop for all technologies?

We have selected “best of breed” solutions for data quality, data lineage, and Master Data Management (MDM), based on a requirement for specialised, high-performance tools. We concentrated on high-quality enterprise solutions for easy integration with our current technologies. Our main priorities were security, scalability, usability, and compatibility with our infrastructure. 

By adopting this approach, we achieve enhanced specialisation and capabilities in each area, providing high-level performance. This strategy offers the necessary flexibility within the organisation to establish a unified data management ecosystem. This aligns with our strategic objectives, ensuring that our data management capability is scaleable, secure, and adaptable.

Regarding the technologies we have selected, Collibra increases data transparency through efficient cataloguing and clear lineage; CluedIn ensures consistent and reliable data across systems; and Datactics is critical for maintaining high-quality data. 

“As we venture into advanced analytics, the importance of our data quality increases.”

In Datactics’ case, it provides data cleansing tools that ensure the reliability and accuracy of our data, underpinning effective decision-making and strategic planning. The benefits of this are immense, enhancing operating efficiency, reducing errors, and enabling well-informed decisions. As we venture into advanced analytics, the importance of our data quality increases. Therefore, Datactics was one of the first technologies we started using.

We anticipate gaining substantial competitive advantages from our strategic investment, such as improved decision-making capabilities, operational efficiency, and greater customer insights for personalisation. Our ability to swiftly adapt to market changes is also boosted. Gallagher’s adoption of automation and AI technologies will also strengthen our position, ensuring we remain at the forefront of technological progress.

On Master Data Management (MDM), you referred to the importance of having dedicated technology for this purpose. How do you see MDM making a difference at Gallagher, and what approach are you taking?

Gallagher is deploying Master Data Management to provide a single customer view. We expect substantial improvements in operational efficiency and customer service when it is completed. This will improve processing efficiency by removing duplicate data and offering more comprehensive, actionable customer insights. These improvements will benefit the insurance brokerage business and will enable improved data monetisation and stronger compliance, eventually enhancing client experience and increasing operational efficiency.

Implementing MDM at Gallagher is foundational to our ability to enable global analytics and automation. To facilitate it, we need to create a unified, accurate, and accessible data environment. We plan to integrate MDM seamlessly with our existing data systems, leveraging tools like CluedIn to manage reference data efficiently. This approach ensures that our MDM solution supports our broader data strategy, enhancing our overall data architecture.

“By including data quality activities in our approach, we anticipate significant benefits from the MDM initiative.”

Data quality is crucial in Gallagher’s journey to achieve this, particularly in establishing a unified consumer view via MDM. Accurate and consistent data is essential for consolidating several client data sources into a master profile; we see it as essential, as without good data quality the benefits of our transformation will be reduced. By including data quality activities in our approach, we anticipate significant benefits from the MDM initiative. We foresee a marked improvement in data accuracy and consistency throughout all business units. We want to empower users across the organisation to make more informed, data-driven decisions to facilitate growth. Furthermore, a single source of truth enables us to streamline our operations, leading to greater efficiencies by removing manual processes. Essentially, this strategic MDM implementation transforms data into a valuable asset that drives innovation and growth for Gallagher.

Looking to the future of insurance, what challenges do you foresee in technology, data and the insurance market?

Keeping up with the fast speed of technology changes can be challenging. We are conducting horizon scanning on new technologies to detect emerging trends. We wish to include new tools and processes that will complement and improve our current systems as they become ready.

“We prioritise the security of our data assets and our clients’ privacy because it is essential for our reputation and confidence in the market.”

Next is ensuring robust data security and compliance, particularly when considering legislation changes about AI and data protection. Our approach is to continuously strengthen our data policies as we grow and proactively manage our data. We prioritise the security of our data assets and our clients’ privacy because it is essential for our reputation and confidence in the market.

Finally, we work closely with our technology partners to leverage their expertise. This collaborative approach ensures that we take advantage of new technologies to their maximum capacity while preserving the integrity and effectiveness of our current systems. 

Are there any other technologies or methodologies you are considering for improving data management in the future beyond what you have mentioned?

Beyond the technologies and strategies already mentioned, at Gallagher, we plan to align our data management practices with the principles outlined in DAMA/DMBOK (Data Management Body of Knowledge). This framework will ensure that our data management capabilities are not just technologically advanced but also adhere to the best practices and standards in the industry.

In addition to this, we are always on the lookout for emerging technologies and methodologies that could further enhance our data management. Whether it’s advancements in AI, machine learning, or new data governance frameworks, we are committed to exploring and adopting methodologies that can add value to our data management practices.

For more from Tia, you can find her on LinkedIn.

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What are Large Language Models (LLMs) and GPTs?

In today’s rapidly evolving digital landscape, two acronyms have been making waves across industries: LLMs and GPTs. But what do these terms really mean, and why are they becoming increasingly important? 

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• Security best practices.
• Fully documented Rest APIs.
• Secure file sharing.

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• With their deep knowledge and expertise, our team can build and configure robust and scalable solutions that seamlessly integrate with your existing infrastructure

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• Visualise and analyse DQ results using your preferred dashboarding platform or leverage our ready-to-use templates for popular tools like Power BI to gain valuable insights from your data.
• Additional built-in dashboards are a feature of Augmented Data Quality, delivering insights into data health, trends and further analysis.

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• Hypervisor and Cloud agnostic.
• We deploy on premises (physical or virtual machines) or on cloud (public or private).
• We offer flexible deployment options to best suit your needs. Customers have three options:
  • Datactics professional services to handle the deployment
  • Follow the deployment guides to deploy it themselves
  • Choose a guided installation where Datactics professional services assist while the customer takes the lead.
• Typical deployment of our platform can be completed in 1-2 days, depending on preference.

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Installations require:

• Database management system (e.g Microsoft SQL Server)

• Our platform requires a thick client for our rule building studio, FlowDesigner.
• This client is typically installed on a Windows machine
• End-user’s laptop
• A virtual desktop (VDI)
• A DQM server if remote desktop protocol (RDP) access is permitted
• An application server (i.e. Citrix).

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Despite a seemingly healthy green glow in your dashboards and exemplary regulatory reports, you can’t help but sense that something is amiss with the data. If this feeling rings true for you, don’t worry – it may be an indication of bigger issues lurking beneath the surface.

 

You’re not alone. In this blog we’ve taken a look at some of the most influential factors that indicate you’ve got a data quality problem. Why not use these handy pointers as a starting point to dig deeper?

1. You’re getting negative feedback from your internal business partners.

Data is the backbone of any business, so it’s no surprise that a lack of satisfaction from internal partners can often be traced back to data issues. From ensuring quality datasets are delivered at scale, through to solutions aimed towards empowering your colleagues with access to necessary information and context – there are many proactive steps you can take when aiming for better performance in this area. Taking action now will ensure everyone has what they need; fuelling success and transforming negative feedback into positive progress.

2. People keep sending you data in Microsoft Excel.

Now, we all love Excel. It’s brilliant. It’s made data handling a far more widespread expectation at every level of an organisation. But it does not give any way of source or version controlling your datasets, and is massively prone to its inherent limitations in scale and size. In fact, its ubiquity and almost unilateral adoption means that all your fabulous data lake investments are being totally undermined when things like remediation files, or reports, get downloaded into an Excel sheet. If you’re seeing Excel being used for these kinds of activities, you can bet you’ve a data quality problem (or multiple problems) that are having a real effect on your business.

3. Your IT team has more tickets than an abandoned car.

If your business teams aren’t getting the data they need, they’re going to keep logging tickets for it. It’s likely these tickets will include:

• Change requests, to get the specific things they need;
• Service requests, for a dataset or sets;
• Issue logs because the data is wrong.

More than an identifier that the data’s not great, this actually shows that the responsibility for accessing and using the data remains in the wrong place. It’s like they’re going to a library with an idea of the plot of the story, and the genre, but they can’t actually search by those terms so they’re stuck in a cycle of guessing, of trial and error.

4. Conclusion

What these indicators have shown is that identifying data quality issues isn’t just for data teams or data observability tools to own. The ablity to recognise that something isn’t right is something that sits just as importantly within business lines, users and teams. 

What to do next is always the key question. Ultimately, data quality can be improved if the right processes and tools are put in place to collect, cleanse, and enrich data. There are several challenges that need to be overcome when dealing with bad data. These challenges include:

• Identifying data quality issues,
• Deploying adequate resources and time to resolve them, and
• Investing in advanced analytical tools.

To do this effectively, enterprise-wide data governance is essential as it provides an actionable framework for businesses to continuously manage their data quality over time. Although implementing changes across an organisation may seem daunting at first, there are a few simple steps which organisations can take today that will help them quickly improve their grip on data quality.

A very important first step is the establishment of a data quality control framework, and helpfully we’ve written about this in the following blog. Happy reading!

 

 

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