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!

 

 

The post How You’ll Know You Still Have a Data Quality Problem appeared first on Datactics.

As we know FinTech is changing the way people bank and the way of course banks wish to operate. Our very own, Marketing and Partnerships Manager Matt Flenley contributed to a webinar discussing how to conquer data and drive automation in banking and financial services. The session sought to unpack the best practices for using technology and optimising technology to break new ground in banking. 

Matt was joined by Karen Bradbury of Invest NI, Brendan McCarthy of Analytics Engines and Andy Wallace, The Robot Exchange.  

We sat down with Matt to explore the rising employment of FinTech employment in the UK, why FinTech is rapidly changing people’s approach to banking, how organisations can combat messy data and what makes Datactics different. 

To kickstart, why do you think FinTech is changing people’s approach to banking? 

For an awfully long time, strength has been in established institutions having all the customers and that providing economies of scale for transactions. Back in the day, transactions made using your cash card would go over the link network and there are fees paid between the bank you have your card with to the bank that operates the ATM – meaning a massive amount of interchange. All of this money going between banks and card issuers creates a big market for the banks to make money. What has happened recently with open banking and PSD2 is that those services have been able to be offered by Revolut, TransferWise and Starling, who challenge the need for cash. They believe that digital payments are the way forward, previously they were slow but now due to internet technology and privacy standards being improved, there is now the ability to manage all digital payment activity through an application. These customer-led innovations are in effect disintermediating what the banks used to be able to only offer themselves, which has led a big drive towards FinTech solutions. For example, Loyalbe has created a means to pull all of your loyalty cards and accrue loyalty benefits at local shops in a way that beforehand would’ve been at the sole reserve of the bank or the card issuer. 

Why NI? Why has NI become such a prevalent FinTech hub? 

I think NI has the benefit of being a small, concentrated community. There is much lower cost of employment and cost of living but an extremely high standard of education. There is a perfect combination of an educated population with a low cost of living, and all the work that Invest Northern Ireland and others have done by way of FDI that has brought the likes of Citi, Allstate and Liberty to the area has allowed people to think they can train to be a software and have the option to work for a huge multi-national corporation right on the doorstep. There has been a sizeable amount of government money invested to secure those overseas firms but they haven’t just invested and ran away, they have invested and stayed. Which has given rise to a highly literate and highly capable financial services technology workforce based here. 

Just to bring Datactics into the mix. Messy data is everywhere – how can organisations combat this? 

Messy data is everywhere. Being able to fix it is a challenge that faces every institution. We focus on Financial Services because they are the ones with typically the most data literacy, they understand the criticality of data and how having better data quality data can help them to outflank competition. In many institutions there are many problems with errors of capture and out of date information creating negative customer experiences and regulatory fines.  

Where can they start and where can they fix it?  

We have led the way with business unit led data quality software, this doesn’t involve amassing all of your coders and programmers and asking them to hard code rules into their central IT systems. Your business teams know what the data should be, and they should be able to interact with that data and fix it as necessary. Then downstream be able to report it, use it and analyse it. We would suggest working with a company like us to be able to deploy that on a specific business problem, we work with commercial organisations that have business goals to hit, it’s now time to put the capability in those business teams too. 

Data formats, entries and elements often present different versions of the truth, some of these are important distinctions but others are issues of quality, including typos and outdated records – tell me more about this… 

There is a valid distinction. For example, it’s important to understand that different records need to be reflective of different time periods. I may want to write to one Jamie Gordon, but I may have 5 on my systems due to having multiple different banking systems. You don’t want to be constantly pestering Jamie, it’s also massively inefficient to have to send a high volume of data automatically as it is expensive. At a corporate level, it’s hard to understand who the beneficial owner of institutions that are similarly named. It’s really important for anti-money laundering and KYC. Being able to interpret those differences, understanding common elements is critical to eradicating the manual effort and ambiguity around KYC and AML. 

What would you say is Datactics’ biggest differentiator in the market? 

Business user focus is imperative – we are seeking to serve the business teams allowing them to self-serve for analytics and quality all at once. Our clients don’t want a year long process over their heads before any results are visible. We are keen to make our clients as literate of the platform as possible because we want them to be able to use the platform fully as soon as possible, we also support them initially and offer them 24/7 assistance if they so need it.  We are all about self-service. 

We thoroughly enjoyed being involved with this webinar. We love the fact that Invest NI has sought to connect the dots between vendors and markets. We have had many opportunities to pitch ourselves to new markets and work with huge brands. They have been a strong advocate for native Northern Irish companies and have built on the legacy as we move into an increasingly technologically driven future. 

If you would like to discuss the FinTech market in NI, how Datactics can help with messy data or how the world of banking is changing, please reach out to Matt Flenley today. You can also book a one-to-one consultation with him, if you’d like to explore our solution further. 

For other news about Datactics please visit this page. Alternatively, you can contact us by clicking here.  

Or find us on Linkedin, Twitter or Facebook.

The post “How to conquer data and drive automation in banking and financial services” | An interview with Matt Flenley appeared first on Datactics.

We’re delighted to have been selected to join a virtual roadshow to the US: DIT’s RegTech Roadshow.

We will be joined by innovative regulatory technology companies such as: Acin AMPLYFI, AutoRek, ComplyAdvantage, Finreg-E, FNA, Solidatus, Suade Labs, and TAINA Technology Limited.

This roadshow will provide this group of leading UK RegTechs with the opportunity to meet directly with US corporate innovation and compliance teams, receive guidance and training from DIT partners on how to set up and grow a physical presence, and hear from industry experts including regulators about the key challenges facing the US market and how their technology can help solve them.

If you would like to get in touch, please reach out to Kieran Buchanan or Matt Flenley who will introduce you to our US-based case studies within the Financial Services sector and help you understand where Datactics brings value.

The post DIT RegTech Mission to US | 22/02 – 26/02 appeared first on Datactics.

Datactics is a part of the cohort of companies joining the DIT Virtual RegTech themed roadshow this week.

The DIT’s RegTech Roadshow takes place virtually with the aim of providing ten innovative UK-based RegTech companies with the opportunity to meet industry stakeholders, regulators and potential partners. 

Datactics will be visiting New York virtually alongside Acin AMPLYFI, AutoRek, ComplyAdvantage, Finreg-E, FNA, Solidatus, Suade Labs, and TAINA Technology Limited. The delegation this year is one of the most diverse ever, with representation from every region across the UK, including three female-founded companies! On top of this, the delegation has serviced nearly every Tier 1-2 financial institution and raised a staggering $100 million in funding combined. Finally, each of the 10 companies offers a unique solution built to help firms conduct their business safer, easier, and more equitably.  

We thought we would sit down with Kieran Buchanan, Business Development Executive at Datactics, to find out more about the roadshow and why Datactics is delighted to be involved. 

This event being a US RegTech roadshow will have US-based industry stakeholders, regulators and potential partners – this is a market Datactics is keen to establish themselves even further in, isn’t it? 

It is most certainly; I have seen some large banking organisations have a presence at this roadshow so it’s our prerogative to introduce our services as we already have some use cases in the US market. This will allow them to know what we provide in the space of data quality and the data management infrastructure as a whole. US is a significant market bursting with opportunities to invest time and effort into. 

What conversations do you hope to have with the industry stakeholders, regulators, and potential partners? 

There are a lot of innovation-based attendees so I think there will be a natural introduction to who Datactics is and how we can bring value. We’re particularly looking forward to demonstrate how our solution will help organisations that are keen to develop a good data quality culture. Our Self-Service Data Quality (SSDQ) platform will be fantastic for those organisations that want to get ahead of the curve by ensuring they have the proper building blocks in place when it comes to the quality of their valuable data assets.

Throughout the week, what would be the best way for attendees to get in touch with you? 

I have a meeting set up on the platform, Meetaway, so I’ll be contactable via video call using that platform. My profile is on there, fully populated! You can also get in touch with me via email: kieran.buchanan@datactics.com and of course, via LinkedIn.  

There are some great panel talks happening this week! Are there any talks you are keen to go to? 

There’s a talk on the topic of the future of financial services, Regtech and Compliance which I am keen to attend. Another interesting talk would be ‘How US regulators are adapting to new technologies‘ as it reflects the work we do with SSDQ. We are currently helping business users at financial services organisations get their data right for a wide range of regulatory reports, whether that be BCBS 239 or MiFID II, or country-specific deposit guarantee scheme reporting.

If you are attending the event, feel free to reach out Kieran at any time, via email, LinkedIn or Meetaway.

The post Meet Datactics at the DIT New York RegTech Roadshow! appeared first on Datactics.

FinTech is rapidly changing the way people bank and the way banks wish to operate.

But for the majority of financial institutions, their desire to deploy modern digital, AI, machine learning, and data solutions (either enterprise-wide or for specific functions) is usually quashed by a familiar culprit: the antiquated and often disparate core processing systems that lay at the back end of most organizations.

In this webinar, we will discuss real case studies with other leading FinTech/RegTech companies hivera and Vox Financial Partners. This group will explain how they help banks accelerate digital transformation, overcoming the significant challenges presented by ancient legacy systems. Together they will provide CIOs, CISOs, CDOs, and CROs valuable insight and best practices for using technology and optimizing data to break new ground in banking.

The post InvestNI with American Banker | FinTech on Main Street: Trading Legacy Systems for Digital Transformation | 25/03 appeared first on Datactics.

Belfast-based Regtech company Datactics, a leading data quality software provider to global financial services firms, has been selected by the UK Government’s Department for International Trade (DIT) to showcase the best in British innovation during a US roadshow.

The DIT’s RegTech Roadshow takes place virtually 22-26 February 2021.

It will provide ten innovative UK-based regulatory technology companies with the opportunity to meet industry stakeholders, regulators and potential partners. They will also receive guidance and training from DIT partners on how to set up and grow a physical presence and hear from experts on the key challenges facing the US market and how their technology can help solve them.

Datactics was selected following a highly competitive recruitment process. To qualify for the roadshow, each company had to meet the following criteria set by DIT: 10 or more employees, a minimum annual turnover of £1 million, an enterprise-ready solution, and an existing base of clients in the UK and US markets. The companies selected have all achieved great success both domestically and globally, cultivating a strong international client base and achieving a reputation of excellence in helping revolutionize the way the financial services industry manage risk, compliance, and regulatory change. The cohort represents diversity and female leadership within the UK industry, with each firm offering a unique and innovative solution that is revolutionizing regulation compliance and all the companies combined service nearly every Tier 1 and Tier 2 financial institution.

Kunal Khatri, Director for DIT North America, said:

The UK has been at the heart of the global financial services innovation for decades, and in 2021 we will continue to lead the global FinTech and RegTech revolution. We’re excited to showcase the talent and expertise that UK companies have to offer. This roadshow is a great opportunity to deepen our bilateral engagement on financial services with the US and encourage private sector collaboration to make the world a safer, easier, and more equitable place to do business.

Commenting on the selection, Stuart Harvey, Datactics CEO, said:

This is a great opportunity for Datactics to showcase our best-in-breed data quality and matching solutions to the US financial market. We have proven success in the UK and Europe and already work with clients in the US. We are looking forward to helping financial institutions transform their data-driven business, manage risk and regulatory compliance.

The UK leads in the field of regulatory innovation thanks to its high concentration of financial services firms and the institution of the Financial Conduct Authority’s regulatory sandbox. British RegTechs are well-positioned to add value and help revolutionise the way US companies approach compliance.

In addition to the 10 companies, DIT is excited to welcome media partners and advisers who helped to develop the program, including the A-Team Group’s RegTech Insights, and the COMPLY Conference, host of one of the largest RegTech industry events in the US.

For other news and awards about Datactics please read this page. Alternatively, you can contact us by clicking here.

The post Datactics is selected by the UK Department for International Trade to showcase best in British Innovation appeared first on Datactics.

Datactics is proud to be one of 37 UK tech firms that were selected across a wide range of sectors and verticals, to showcase our solution at the UK FinTech Mission.

The virtual event aimed to offer participants, attendees, and partners an inspiring network and learning opportunity.  

For those attendees that were from Austria and Switzerland, the company profiles of the UK companies were made available, as well as the possibility to learn more about the products and solutions on the marketplace that they are offering or extend the talks via 1:1 meetings.

Our representative, Jordan Wray, described his experience at this virtual event here. To keep up to date with future events that Datactics is attending, visit out events page here.

The post UK FinTech Mission to Austria & Switzerland appeared first on Datactics.

So where should business and data leaders start to confront this problem?

Many firms start off by putting in place Data Governance strategies. The objective is clear: if I have a set of policies and standards, then people will find it easier to conform and drive up the quality. However, in practice this is unlikely to overcome a problem right now; with no stock answer to “how long will this Data Governance programme take?” it makes sense to attack it in a different way at the same time. Below, we’ve curated a “top 3” list to help you get started.

What are the steps to creating a data quality driven culture?

1. Pick a quick win that means a lot to your business
2. Get your business teams on-board
3. Focus on measuring quality in a demonstrable way

Firstly, then: picking a quick win. What’s the biggest impact to your business operation right now? As leaders, you’ll doubtless have a big list, but there’s going to be one that sticks out. Maybe it’s a piece of data that’s tripping everyone up when it comes to risk reporting. After all, nobody wants to have to complete risk returns unnecessarily! Perhaps it’s a problem with how customer addresses are formatted across different systems? Whatever the quick win is, get the business teams around the table and agree a rapid timeframe for delivering a step change in how that data is perceived.

That brings us to our next point: getting business teams on-board. In the old days, keeping business users away from data management might have made sense from the point of view of reducing errors in how data was recorded or standardised. But in today’s world, if business teams only know how to complain about data rather than play an active part in managing it, the problem is only going to get worse! If you can get a few internal teams to commit to resolving the thorniest of problems that also happens to be the lowest of all the hanging fruit, then your chances of further success are made all the greater. But they’ll need tooling that doesn’t require them to retrain as programmers, so be mindful of that when it comes to how you’re actually going to manage it.

Lastly, someone is going to have to sign off on your project spend and agree with you that this was a worthwhile use of everyone’s time. Sure, there are standard data quality metrics that all data people know to measure, but what are the business measures that will help prove that this collaboration project has been worth it? Remember, while the business teams have been used to complaining about bad data, they’ve rarely had a chance to be a part of the solution. Maybe it’s a measure of speed to fix data quality problems, together with a proportion of those now being fixed by business teams? If you can show that the programme has shifted the responsibility and the capability to business teams, you’ll be able to demonstrate how the culture of the organisation is becoming more data mature.

What about you? How have you managed to get your culture data-savvy? Or maybe you’re really struggling to get people involved? Send me a message on LinkedIn and we’ll keep the conversation going!

Where can Datactics help you on your data management journey? We know your time is precious so if you’d like to find out how we can help deliver self-service data quality to your business, simply book 15 minutes of Brendan’s time by choosing a slot here.

To learn more about how how Data Quality fits into modern Data Ops strategies, catch our webinar as part of 2020’s Data Management Insight event (or read a blog post version here).

The post 3 Steps To Create A Data-Driven Culture: How To Empower Everyone To Be A Data Citizen appeared first on Datactics.

Nobody is doubting the ambitions of business teams when it comes to doing more with their data, but being hampered by duplication, inconsistencies and gaps really takes the wind out of their sails.

Is it any wonder, when in a 2017 study Deloitte reported that 92% of financial institutions relied on faulty information to better understand their customers? At a cost of nearly $15m annually per firm, as reported by Gartner, poor quality data is a major drag to their progress.

In this blog post, we’re going to give those data people some hope, from the other side of successfully implementing a data quality improvement plan, with three unexpected benefits of being able to trust in your data.

Hint: it’s all about your people; so our unexpected benefits are going to major on your data people, showcasing these benefits from a human perspective.

First up – empowering business teams

McKinsey reported that over half a million days of managers’ time is potentially wasted when people can’t make decisions quickly and efficiently. No doubt, in these data-driven times, that this is in no small part down to the data they have at their disposal.

There are three factors behind something called “agency” – the ability to make decisions, have all the resources at your disposal to do a good job, and feel competent in your role – which is recognised as being one of the most effective drivers of productivity. But perhaps just as importantly, it makes people feel good at their job. Being able to trust in data improves morale for that much-needed analytics programme, the efficiency-delivering AI project, or for customer services teams operating on the front lines.

As we’re focusing on your people, let’s delve deeper into ‘self-help for your data people’ by looking at our second benefit: de-stressing your data stewards

Sometimes the type of data, what it represents, and simply how untidy it is, causes a lot of stress. The stat from Forbes in 2016 about 80% of data scientists’ time is spent doing something that nearly the same percentage find unfulfilling is a major worry for having effective people working effectively.

If the data was clean, they wouldn’t be wading through duplicates. If it was in order, they could for example use their skills in building models based on sentiment analysis instead of having to manually extract sentiment from poorly maintained datasets.

The poor data janitor will have to trawl through others’ misery and trying to pull all the conclusions together before the next reporting period is due. Get the data sorted, and those data people can put their clever brains into making a difference instead.

And then they won’t want to leave you, which leads us nicely on a related note to our third unexpected benefit: retaining good people.

Data scientists are hard to come by, and good data scientists even more so. That’s before you consider the cost of replacing them, which for roles that average upwards of $120k can be well over the 33% listed by Forbes.

Trusted data gives them a major reason to stick with your company and not defect to the nearest competitor who, on the face of it, looks like they’re running the latest tech. Not every firm can simply snap their fingers and get the latest tech infrastructure in place, but with data scientists’ salaries and packages regularly approaching that of hardcore developers it’s clear that the work should go into retention rather than a revolving door of constant replacement.

In summary

We’ve been working with plenty of firms whose business goals definitely include dashboards, metrics, all the usual features of data quality and data governance. But we’ve found that culture is as much a driver and a beneficiary of having clean data. These unexpected benefits might not be what you’re thinking of when you’re embarking on a data programme, or even when you’re just trying to figure out how to make things better, but they’re definitely going to power your company’s approach to the data-driven future.

Maybe you’ve more unexpected benefits of cleaning data? You can book a 15 minute non-committal chat with Kieran Buchanan here, or by clicking the image below! He would be happy to chat through any questions, queries, or steps you wish to take. Bad Data can’t wait for IT; save time, reduce costs, increase profitability and reach out to Kieran today.

Click here for more by Datactics, or find us on Linkedin, Twitter or Facebook for the latest news. 

The post Three Unexpected Benefits Of Cleaning Your Data appeared first on Datactics.

Many data management professionals have some form of tooling or platform to support their business initiatives but often find it hard to get buy-in for why they still need to invest time and resources in this area.

In a recent post by data management blogger Henrik Liliendahl on the passing of Larry English, he refers to English as having “pioneered the data quality – or information quality as he preferred to coin it – discipline.” Liliendahl looks at three main concepts that underpin all data and information quality technologies, and in a moving tribute, inspires us to “roll up our sleeves and continue what Larry started.”

So, for anyone involved at any stage in information management, it’s therefore worth taking the time to consider the impact that English had on the industry and understand what lessons remain today.

Those three concepts are:

1. Quantify the costs and lost opportunities of bad information quality
2. Always look for the root cause of bad information quality
3. Observe the Plan-Do-Check-Act circle when solving the info

Firstly then, on the costs and lost opportunities.

The “Data Doc” Tom Redman has a neat test that data managers can conduct on a Friday afternoon – maybe unsurprisingly called the “Friday Afternoon Measurement.” Instead of repeating it in detail here, head over to Harvard Business Review and take a look.

In short, assemble like-minded people who know the data and can quickly tell if it’s right or not, open a beer or two, take four quick steps and a small bit of cost estimation and – hey presto – your cost of bad information quality is right in front of your eyes.

What else could you have done with that money? Anyone in financial services knows that it’s harder to get budget for something than it is to eliminate an operational cost, but as this very simple business case will show, it’s not actually that hard to demonstrate after a beer on a Friday afternoon with the Data Doc.

Secondly, the root causes.

One of the best ways of discovering the root cause is the “five whys” pioneered by Toyota (there’s a good guide to it here).

Maybe after your Friday Afternoon Measurement, pick some of your data problems and set your people the task of asking “why” five times, with the clear instruction to get the causes as specific as possible. Summarise, prioritise and then look for ways to eliminate those problems.

Lastly, the “Plan-Do-Check-Act” (PDCA) loop

(Again, a super guide here)

… was pioneered by Dr William Deming as a way of uncovering why some processes or products are underperforming. In data and information management, being able to measure what impact you’ve made is a critical feature both as a reporting avenue to senior stakeholders and as the first stage of the next PDCA loop.

Simply offering up dashboards and visualisations to show what’s happened or happening is just one part of it. The slickest PowerBI dashboard might well showcase where the data quality is, but unless it’s also demonstrating where the next priorities and best-recommended actions are, it’s not actually a loop of continuous improvement.

In summary, it’s worth looking back at what Liliendahl said about Larry English: he “…pioneered the data quality – or information quality as he preferred to coin it – discipline.” Fundamentally it’s about being disciplined, but in the right areas!

How could reimagining data quality make a difference to your organisation?

Where can Datactics help you on your data management journey? We know your time is precious so if you’d like to find out how we can help deliver self-service data quality to your business, simply book 15 minutes of Kieran’s time by choosing a slot below.

To learn more about how how Self-Service Data Quality is the best approach to developing a next-gen data management strategy, catch our webinar from 2020 with key input from CTO, Alex Brown (or read a blog post version here).

Click here for more by Datactics, or find us on Linkedin, Twitter or Facebook for the latest news. 

The post Data Quality, Didn’t We Do That Already? appeared first on Datactics.

We are delighted that our very own Fiona Browne has helped to co-curate AI CON 2020. 

The second annual AI CON is taking place virtually on Thursday 3rd and Friday 4th December and will be hosted by Kainos and Aisling Events as per last year. This year’s event will be recorded live from the AI CON studio in Belfast over two days.  The event will bring together world-leading technology professionals and business leaders to discuss and examine how AI is continuing to change our world. This year’s gathering will discuss: 

• AI ML in Fintech 

• AI in the Public Sector 

• Impact of AI on Society, Arts, and Culture 

• Applied AI/Supporting AI Startups 

• AI Research and Innovation 

• AI in the Screen Industries 

AI CON will once again be a brilliant opportunity to listen and engage with professionals that range from developers to business leaders, who have led on adopting AI as a tool to build better services, products, or business operations.  

The conference is free to attend but delegates must register ahead of time. 

Find us on Linkedin, Twitter, or Facebook for the latest news or click here to contact us. 

The post AI Con 2020 | 3rd – 4th December appeared first on Datactics.

Join us for a week of unmissable content and unparalleled networking at the 6th edition of FinTech Connect. Digital transformation has accelerated over the last few months and we will take this opportunity to be at the centre of this paradigm shift by hearing from, networking and engaging with all the industry’s leading players.

For more details about FinTech Connect 2020 and to register for free, click here.

Datactics Self-Service Data Quality platform empowers business users to self-serve for high-quality data, saving time, reducing costs and increasing profitability.

This is where Datactics 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. 

Get your personalised quote here or download our whitepaper below:

Find us on LinkedIn, Twitter or Facebook for the latest news. To have a conversation about how the self-service data quality platform can help you to manage your data, contact our Head of Sales, Kieran Seaward today.   

The post FinTech Connect 2020 | 30/11/20 – 04/12/20 appeared first on Datactics.

Datactics is pleased to announce that its Head of AI, Dr Fiona Browne, has been invited to participate in the Artificial Intelligence Public-Private Forum, joining 20 other experts from across the financial technology sectors as well as academia, along with the observers from the Information Commissioner’s Office and the Centre for Data Ethics and Innovation.

The purpose of the Forum, launched by the Bank of England and the Financial Conduct Authority, is to facilitate dialogue between the public and private sectors to better understand the use and impact of AI in financial services, which will help further the Bank’s objective of promoting the safe adoption of this technology.

The AI Public-Private Forum, with an intended duration of one year, will consist of a series of quarterly meetings and workshops structured around three topics: data, model risk management, and governance.

Commenting on the initiative’s launch, the deputy governor for markets and banking at the BofE, David Ramsden said:

The existing regulatory landscape is somewhat fragmented when it comes to AI, with different pieces of regulation applying to different aspects of the AI pipeline, from data through model risk to governance. The policy must strike a balance between high-level principles and a more rules-based approach. We also need to future-proof our policy initiatives in a fast-changing field.

The specific aims of the Forum are: firstly, to share information and understand the practical challenges of using AI in financial services, identify existing or potential barriers to deployment, and consider any potential risks or trade-offs; secondly, to gather views on areas where principles, guidance, or regulation could support safe adoption of these technologies; and finally, to consider whether once the forum has completed its work ongoing industry input could be useful and if so, what form this could take.

The knowledge, experience, and expertise of the Forum’s members and observers will be invaluable in helping us to contextualise and frame the Bank’s thinking on AI, its benefits, its risk and challenges, and any possible future policy initiatives.

Fiona Browne, Head of AI at Datactics, said:

I’m really excited and honoured to be part of such a timely forum. AI/ML services touch our everyday lives from recommending what we watch to groceries that we buy.

Within financial services, ML can offer efficiency benefits reducing manual time-consuming tasks, to saving customers money in suggesting best financial products to bespoke customer service solutions and fraud detection. These solutions need to sit within a legal and regulatory environment in the financial sector and are not without their risks and challenges.

I hope to offer the forum insights and experience of the practical implementation of ML-based on the areas of data quality and fairness through to transparency and explainability in the process and model predictions through to the monitoring of models in production. Excited to focus and tease out potential guidance and best practice on how to safely adopt and deploy such solutions.

What is the AI Public-Private Forum?

The BOE working with FCA have established the AIPPF (AI Public-Private Forum). This forum launched in October 2020 and consists of members reflecting a variety of views who applied to be on the forum bringing with them their expertise in the area of AI/ML. The AIPPF will:

• Share information and understand the practical challenges of using AI/ML within financial services, as well as the barriers to deployment and potential risks. 
• Gather views on potential areas where principles, guidance or good practice examples could be useful in supporting safe adoption of these technologies. 
• Consider whether ongoing industry input could be useful and what form this could take (e.g. considering an FMSB-type structure or industry codes of conduct). 

More information about the Forum can be found here.

The post Datactics contributes to Bank of England and FCA’s AI Public-Private Forum appeared first on Datactics.

With Statista.com reporting that 59 zetabytes of data has been captured, created, copied and consumed worldwide since 2010, it’s easy to see the problems that arise when even a fraction of this is incorrect. 

The chaos that can – and does – arise can seem totally insurmountable, creating a problem that’s as unappealing to solve as it is difficult. It can also make it hard for data leaders to really get their people inspired about the art of the possible in finding out what’s wrong, what good should look like, and how to make a difference. 

This post, then, is designed to help energise, excite and encourage your data people in three easy-to-implement ways that will help deliver your next data management programme and truly change your data culture. 

Firstly: You don’t need to rip & replace your expensive tech stack 

The major enterprise data management firms are already in situ at many of the world’s biggest firms, yet people are still complaining about the quality of the data they’re working with. Any approach to solve this problem can lead C-suite executives to think that buying more software to replace it is just far too costly and risky to achieve. 

Selecting vendors who can work alongside the Informaticas and IBMs of this world is clearly a pragmatic opportunity to independently measure and improve the quality of data right from the business teams. It puts the platform in your hands, so that you and your teams can play an active part in the data flow in the organisation without disrupting the stable enterprise technology stack. 

(And what’s more, we’ve done this many times before).

Secondly, boil a kettle, not the ocean! 

“Boiling the ocean” is a really evocative phrase when it comes to prioritisation and the approach to take – especially with something as central and fundamental as data quality. Everyone needs high-quality data, even those who are guilty of kidding themselves that they don’t! Heads of Innovation and Change are discovering that they won’t be able to innovate or change anything unless the data is right. 

Picking something that will make a real difference for someone with access to the big purchasing levers is clearly a great strategy. If a general desire to improve data quality feels like “boiling the ocean”, then how about getting customer data right ahead of a new product launch instead? Fixed dimensions of success, a six-week delivery timeframe and a lower-than-you-think budget for a “time & materials” type licence can go a long way to getting that senior stakeholder buy-in for the bigger dreams you have in mind.

Lastly, now witness the power of this fully self-service system 

The last thing your team wants to be doing is manually cleansing, standardising and matching data. There’s no quicker way of taking the wind out of a data analyst’s sails than by giving them manual “dirty data” work. And with up to 80% of their time currently being spent doing exactly that, it’s clear that this problem isn’t simply going to go away in the morning. Automated routines and processes that will do this for the analyst are like coming in with a double espresso with extra espresso to start the day; they’ll be flying at the data in a fit of pure delight. Solving one critical problem in a way that’s designed for business users to self-serve makes perfect sense. Especially if it’s scalable, repeatable and easily accessible, using automations and pre-built logic to save time and effort, it will liberate your data analysts to attack data-driven problems all over the enterprise and truly transform the culture of your organisation. 

How could rethinking data quality make a difference to your organisation? Hit me up on LinkedIn and let’s continue the conversation!

To learn more about how how Self-Service Data Quality is the best approach to developing a next-gen data management strategy, catch our webinar from 2020 with key input from CTO, Alex Brown (or read a blog post version here).

The post Easy Ideas To Get Your People Excited About Data Quality appeared first on Datactics.

In the same way that you know when you’re not feeling totally on top of your game, your data has tell-tale signs that not everything is working exactly as it ought. So, without further ado, here are three easy ways to perform a data quality check up, and move from feeling as though something’s not right to pinpointing exactly what you need to do.

1) Find and fix the weeds  

Any gardener can tell you that for good crops to grow, you need to keep on top of the weeds. After all, if you let the weeds take root, it can lead to loss of the whole crop. Likewise, even the smallest discrepancies, inaccuracies, or duplicates can throw your data off balance. This means that re-evaluating potential weaknesses and seeking to correct them is key.

But in just the same way as crops and weeds aren’t necessarily easily distinguishable without some green-fingered expertise, you need to involve the people who know what good looks like to address a data quality challenge.

Tom Redman, the “Data Doc”, has a super-handy method for figuring out just how big the data quality problem is. Head on over and have a read here, pick a Friday, schedule a Zoom call, open a beer or two and get cracking!

Once you’ve got your business teams to assess the theoretical size of the problem, you’re already in better shape. A data checkup will help you figure out whether improving the data could be achieved by removing data that isn’t useful, or filling gaps where data is limited, or making sure that your reports are fine-tuned on the problematic data elements, business areas or teams.

2) Talk to your front line  

Ultimately, the people who deal with your data day-in, day-out, are the ones at the coal face, the front line, who are capturing data at its source and updating data records at a phenomenal rate.

If you were to poll your people on the quality of data, and whether they understand who is responsible for data quality testing, what would the result be? If it doesn’t bear thinking about, then that is pretty much the answer to your question! Find out how you can empower people in your business process to be as engaged in the data quality story as they are in analytics, winning business and managing customer experience (to name but three). A good starting point is to look at your organisations data quality management processes, including data governance. Within this, data stewards can perform tasks such as data profiling and data monitoring using the data quality dimensions of accuracy, timeliness etc. This will be a useful benchmark for members of the data team to measure the quality of their data long term.

Often, accurate data is a result of trained and competent employees. However, the ever-changing nature of data and the increasing rate of regulations has meant that a manual approach isn’t enough anymore to achieve high quality data. However, it is a good start for the data team to analyse the data at hand. Recognising there is a data quality issue is the first step towards giving your data a health check. Once you have established there is an issue, a data quality solution can be sourced to solve the problem.  

3) Talk to your customers!   

Of course, quite apart from your internal discussions on whether things are in a good place or not, there’s nothing quite like hearing it from people who rely on you to manage their customer data.

Ask how many poor customer experiences resulted from bad data, or processes that didn’t align with how the information was to be used. Find those situations where a customer was contacted despite actually having died, or a policy was closed without the customer being aware.

Conducting a “five whys” process into poor experiences is a great start. Ask “why was this a result of poor data quality?” five times, coming up with five different answers, and follow those rabbit holes to the source of the problem. Focus on the problems that matter most to customers and build a comprehensive business case that demonstrates just how customer-centric you really are (and what to do next!). In the long term, this will help establish data integrity within your business.

To find out more about what we offer in the data quality and machine learning fields, drop me an email or connect with me on LinkedIn and let’s continue the conversation!

The post 3 Easy Ways To Give Your Data A Check-Up appeared first on Datactics.

From left: Allen Martin, Partner Kernel Capital; Stuart Harvey, CEO Datactics; Mick Foster, COO Datactics; William McCulla, Director Corporate Finance Invest NI & Gavin Kennedy, Head of Business Banking NI Bank of Ireland UK.

Belfast, Northern Ireland, 21^st June 2018

Kernel Capital are pleased to confirm that it has led an investment into Datactics Limited, a Belfast-based provider of RegTech solutions for the banking and financial services sector. Full details of the investment round, which includes investment from the Bank of Ireland Kernel Capital Growth Fund (NI), have not been disclosed.

Datactics has developed a suite of software products which profile, cleanse, score and match information for data quality and regulatory compliance. Over the last 12 months, Datactics has doubled their revenue and added a number of global financial institutions to its customer base including the Bank of Ireland and Danske Bank.

RegTech is a rapidly growing market. With new regulations and compliance requirements driving change it is expected to grow to $118Bn by 2020. Moreover in recent years, governments and regulators worldwide have introduced a series of tight regulations with stringent penalties for non-compliance, resulting in banks paying over $300Bn in regulatory fines since the 2008 financial crisis.

“Banks understand that compliance is only as good as the data that it’s built on, placing an urgent need on fast, reliable and accurate data. Datactics sits directly in this space, providing our clients with continuous and measurable improvements to their data. We are delighted to have received this round of funding from the Bank of Ireland Kernel Capital Growth Fund (NI), which will allow us to accelerate our growth into the global financial sector, expand our team and invest further in R&D.”

Stuart Harvey, CEO Datactics.

 “Datactics, led by Stuart Harvey (CEO), has a strong and experienced team with a diverse skill set, sectorial knowledge and strong networks within the global financial sector. The Bank of Ireland Kernel Capital Growth Fund (NI) is pleased to support another innovative, export focused NI company, with the potential to become a global leader in the RegTech space.”

Allen Martin, Partner Kernel Capital.

 “Datactics is an ambitious local company with an excellent reputation and a highly innovative product. I am delighted that the company has availed of the Bank of Ireland Kernel Capital Growth Fund (NI) to access the finance it needs to scale more quickly. Over the last number of years, Invest NI has also offered Datactics a range of support including R&D and marketing support to help it take its predictive analytics platform to the next stage of development. All of this support is helping Datactics to further its growth aspirations and achieve business success.”

William McCulla, Invest NI’s Director of Corporate Finance.

 “We are delighted to see this investment in Datactics, a fantastic RegTech company with a strong team, skills, experience and a software solution supporting organisations in mining, refining and applying the right quality datasets required to adapt to new regulation and standards. This is another great step forward in enabling them to achieve their growth potential in the global financial services sector.”

Gavin Kennedy, Head of Business Banking NI Bank of Ireland UK.

The Bank of Ireland Kernel Capital Growth Fund (NI) was designed to help SMEs in Northern Ireland to accelerate their growth. Invest Northern Ireland has committed £15 million of funding to this fund which is part financed by the European Regional Development Fund under the EU Investment for Growth and Jobs Programme 2014-2020.

 Invest NI’s R&D support is part funded by ERDF under the EU Investment for Growth and Jobs Programme 2014-2020.

Additional Notes:

 About Datactics https://www.datactics.com/

Datactics has a strong background in market-leading data quality software, tackling common issues in banks largely arising from their reliance on multiple IT systems of varying ages, and the need to make their data compliant with emerging financial regulations. Its suite of high-performance fuzzy-matching tools help firms in a wide array of critical business activities, from regulatory reporting and Anti-Money Laundering to business intelligence.

About Kernel Capital www.kernel-capital.com

Kernel Capital is an independent manager of the Bank of Ireland Kernel Capital Venture Funds, comprising of £165m / €210m of funds raised through six separate venture capital funds in the years 2002, 2006, 2007, 2009, 2013 and 2015. These Funds are supported by Bank of Ireland, Invest NI, Enterprise Ireland, University of Limerick Foundation, NUI Galway Foundation and New York State Common Retirement Fund. Kernel Capital is actively investing through the Bank of Ireland Kernel Capital Growth Funds targeting deal size for Kernel Capital’s element in the region £0.5m to £2m in NI and of €1m to €5m in the ROI, either as sole investor or larger deal size in syndication. These 10 year Growth Funds were raised in 2013 and 2015 and invest in Northern Ireland and the Republic of Ireland respectively. Invest NI’s investment in the Bank of Ireland Kernel Capital Growth Fund is part financed by the European Regional Development Fund under the EU Investment for Growth and Jobs Programme 2014-2020. Kernel Capital is a MiFID regulated firm.

For further information please contact Helen Norris, Kernel Capital +44 28 9042 8367

The post Kernel Capital Growth Fund leads investment in Datactics appeared first on Datactics.