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.

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.

Even with the recent positive news of vaccine trials, COVID-19 is impacting the financial services industry and causing investment banking firms to act. 

Some banks are seeking to sell off failing operations and are changing their focus to business areas that are recording strong profits, such as asset management.

Goldman Sachs has been investing in new technology to keep up with the changing demands of the industry; JPMorgan in contrast has sought to embrace blockchain technology. 

In our recent panel at the Virtual Data Management Summit, Kieran Seaward unpacked how the most successful firms were building their “new normal” strategies on clean data, including those who had started to work with us on our dedicated solutions in Financial Services Data Quality & Matching. 

We’ve built this solution with banks, wealth, and asset managers in mind. The platform allows you to easily measure data to regulatory and industry standards, fix breaches and push into reporting tools, with full visibility and audit trail for Chief Risk Officers.  

Features: 

DEPLOY PRE-BUILT RULES & LOGIC 

Featuring rules for BCBS 239, FSCS, AnaCredit, IFRS 9 and more in a highly-customisable front-end for rapid deployment in financial services. 

VISUALISE & FIX YOUR DATA 

Native Data Quality Clinic application empowers those who know the data to fix the data through integrations with off-the-shelf visualisation dashboarding software. 

POWERFUL DATA MATCHING 

Map instruments, people and entities at scale to streamline KYC and onboarding. Consolidate & enhance reference data from multiple sources including Companies House, Bloomberg, Refinitiv, Dun & Bradstreet. 

MEASURE TO INDUSTRY STANDARDS 

Configurable data quality metrics enable compliance with industry leading standards such as DCAM from the Enterprise Data Management Council. 

INTEGRATE WITH ANY DATA STORE 

In-built connectivity to data lakes, silos and cloud sources, including Azure, Google Cloud Platform and AWS. 

AUTOMATE, SCHEDULE AND MONITOR WORKFLOWS 

Power automations enable rapid access to data systems and stores. Monitor AI algorithms and detail decisions reached with full in-built explainability. 

Contact Us 

To speak to us about your next step on your data management journey, please get in touch. 

The post How are Investment Banks responding to industry changes? appeared first on Datactics.

Investment banking is seeing its historical profit centres eroded by technology and regulations. Core processes are now being automated. Investment banks are becoming smaller and leaner in their approach. The current pandemic has brought the need to keep up with innovation but still capitalise on the opportunities COVID-19 has brought.

We can safely say that a significant number of core functions within traditional investment banking have come under siege.  For many banks, this has led to growth with increases in opportunities. These opportunities include dept-capital markets and M&As.

Despite this, there are many banks that will incur slower change. This will mean that banks may need to downsize and modify their growth ambitions. The answer is to reverse a slow decline by improving efficiency, measuring data to regulatory and industry standards and fixing breaches in bulk.

We have the solution for Investment Banks:

Financial Services Data Quality & Matching

This solution is built with the banks, wealth and asset managers in mind.

The platform allows you to easily measure data to regulatory and industry standards, fix breaches and push into reporting tools, with full visibility and audit trail for Chief Risk Officers.

At Datactics, we believe it’s time to see your data as an asset, not a liability.

Our pre-built and customisable rules measure, visualise and fix your broken data to take the headaches out of regulatory compliance for Investment Banks.

Regulation changes often lead to pressure to build appropriate models and data requirements which only leads to greater complexity. Our solution offers the ability to fix breaches and ensure that regulations are maintained.

You define your own rules, not us

Our Self-Service Data Quality platform has been specifically designed for business users to self-serve for data quality just as easily as they would for analytics. The platform powers the rapid delivery of data programmes led by the business without needing to put additional pressure on internal IT teams.

To speak to us about your next step on your data management journey, get in touch today.

The post What is the outlook for Investment Banks? 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.