I was recently invited to take part in a ‘Chat with Channeliser’ about data quality. I always welcome any opportunity to talk about data quality because it’s a topic often dismissed as quite operational, but one which has big implications for organisations that don’t take it seriously. You can watch the full interview here but to whet your appetite, here’s a few key questions that we covered.
If you are a data quality professional then you have more than likely heard the terms Data Lake, Data Swamp, Data Ocean and even Data Pond and Data Puddle. In fact, stick the word ‘data’ in front of any word used to name a body of water and you’ve more than likely found a commonly used term in the industry (although I have yet to hear of a Data Paddling Pool’). As the gatekeeper of our ever-growing Glossary section, I have picked out some of the most commonly mistaken terms – and with help from our team of experts, I’ve explained how we define them.
Data quality leaders often struggle to connect the dots between the businesses’ goals - increasing revenue, cutting costs, optimising performance - and managing data quality more effectively. This makes building a business case for a data quality programme difficult.
To counter this, we’ve teamed up with Dylan Jones, data quality guru, and put together a guide to walk you through some practical steps you can employ (at zero or minimal cost) to build a case for data quality investment.
The GDPR - we all know it’s coming and we all know we need to do something about it. What isn’t always obvious however is what to tackle first when the elements can be overwhelming -particularly with just 9 months left.
As Head of Propositions for Experian UK&I, I’ve been involved in conversations with many organisations and it’s clear that consent is front of mind. Whilst that is indeed critical for GDPR, it shouldn’t be at the expense of thinking about how you’re going to manage all your existing personal data assets. Having the right processes in place for dealing with data quality is fundamental to ensuring you can address all the actions stipulated in the regulation.
As the head of user experience at Experian Data Quality, I’m often tasked with improving a website, an app or a product. This process often involves solving various challenges, such as “How can we interpret our customers’ behaviours so that we can provide them with a better experience and a more relevant service?”
So how do we do that and what’s it got to do with data?
I love online shopping. I wish I had a pound for every hour I’ve ever spent browsing shopping apps or online stores, looking for deals or unique items; I’d spend those pounds on more shopping. We are now less than a month away from Black Friday, the busiest shopping event of the year where millions of Brits will be jumping online to snap up limited-time-only offers.
Having accurate data. Or more specifically, making sure the data you already have is accurate and up to date and that the new data you’re collecting – address, email and mobile – is captured accurately.
Businesses today continue to see data gaining in importance. As more and more organisations work to harness the power that their information can afford them, their underlying data is affecting every aspect of their operations. Departments like customer service, digital commerce, finance, compliance, operations, and more are all working to figure out how they can use data to better serve their customers, reduce risk, and become more efficient in their operations.
Innovation isn’t anything new to Experian, in fact we’ve been listed in Forbes Magazine’s Top 100 “World’s Most Innovative Companies” for the past four years. I must admit however to being slightly sceptical when the idea of introducing a ‘robot’ into our team came up. Here’s some insight into what we learnt.
Performance may mean different things to different organisations but essentially it’s a measure of success that needs to be monitored, maintained and improved. In my role as Sales Operations Manager I spend a lot of time thinking about how we can improve our own performance. In my case that’s about how well our solutions help our customers to meet their requirements and in turn deliver for their own customers. When it comes to driving better performance, I would go as far to say that I am obsessed with it. I read blogs, books, am top mates with TED and even have my own website dedicated to it.
Many of us are aware of the benefits that high-quality data can bring to an organisation including improvements in operational efficiencies, better decision making and avoidance of risk. It’s getting started that can often be the biggest road block. By that I mean, if you can’t quantify the tangible returns that investment in data quality can bring, how do you get buy-in for investment in it?