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Data quality

How Schroders made lasting improvements to the accuracy of its investment data

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?

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Single Customer View: Is yours data quality focused?

Traditionally, organisations have tackled their SCV requirement through the deployment of an MDM platform. And yet, as Philip discusses in his paper, ‘MDM has always been complex, costly and time-consuming to implement’ and so not necessarily, therefore, in tune with modern business requirements. Layer in an increase in regulation and we have a perfect storm of reasons for organisations to seek an alternative route.

So, what options are there for organisations looking to keep costs to a minimum or take a more agile approach to developing an SCV?

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Our new Customer Support Hub – the best bits

At Experian I’m always looking for ways to improve our customer experience and give our clients what they need to exceed their data ambitions. With this in mind I was delighted to be part of the recent launch of our brand new Customer Support Hub. It’s the product of some really in-depth analysis around exactly how our customers want to interact with us and access support for our tools.

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Identifying the good, the bad and the ugly in the proliferation of data, and why it’s important to see the full picture

The proliferation of data provides us with both challenges, and, if managed correctly, great opportunities. So where to start? It’s all about being able to get a full and complete view of your data. Without the ability to bring relevant data elements together into a single view it’s simply impossible to ‘see the wood for the trees’.

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How to get buy-in to your Data Governance Framework

Everyone agrees that implementing data governance can be challenging, but not all parts of the process are as difficult as others. Designing a simple data governance framework to help your organisation proactively manage the quality of its data is not too hard to do. The problem most people face is getting their stakeholders to "buy in" to their data governance framework.

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Data Management Research – Consumer expectation versus business reality

The results are in. This year’s Global Data Management Research makes for some particularly interesting reading with a clear focus on why growing customer expectation and the forthcoming regulation should be driving the need for better data management.

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How to leverage a Lean Pilot and Roadshow with your next data quality business case

Dylan Jones

Dylan Jones

February 16, 2017

Data quality

In this second article in the series on tips for a data quality business case, I'm going to explain the importance of creating a ‘Lean Pilot and Roadshow’ for getting senior stakeholder buy-in.

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How can buyer triggers impact your data quality business case

Dylan Jones

Dylan Jones

February 6, 2017

Data quality

In this first of a two-part series, I share tips for increasing the success of your data quality business case.

This week I’m looking at personal buyer drivers, and how they can influence the decision to approve or reject your data quality business case.

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Overcoming the 3 obstacles of Open Data

Paul Malyon

Paul Malyon

January 19, 2017

Data quality

Open Data is growing in importance as commercial organisations see value in this data for their own purposes. However, with budgets under ever increasing strain, high quality Open Data has become harder to produce. During our recent study with the Open Data Institute, we identified the 3 key obstacles to creating high quality Open Data.

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Your favourite data topics of 2016

With 2017 underway, we decided to have a look back at what data subjects proved most popular with you during 2016. Below are our 5 most popular articles from last year covering data migrations – hurdles to avoid and approaches to consider; measuring data quality and the 6 key dimensions for assessment; data governance responsibilities for data quality success and of course GDPR, perhaps one of the most talked about data subjects of 2016.  See if you missed any of the key talking points. 

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