Data Changemakers: Cliff Gibson, Owner, DBR Data

by Guy Bradshaw on 14 November, 2017

Cliff Gibson has worked across Electronics, Automotive & Life Sciences over the past 20 years, always looking to drive value out of information and people.  Recent achievements include an end- to-end implementation of a Master Data Management solution to support Identification of Medicinal Products (IDMP), delivering Single View of...

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Data Changemakers: Hilary Simpson, Founder, Sleuth Co-op

by Guy Bradshaw on 07 November, 2017

Hilary Simpson has spent more than 28 years in local government and won the Information Age Public Sector Deployment of the Year Award 2015. She is a pioneer of the ethical use of data to break down silos, ensuring that joined up information is available to help professionals choose early interventions...

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7 key considerations for a successful PIM initiative

by Vivek Bhatnagar on 06 November, 2017

Challenges caused by bad product data Effective management of Product Information or PIM is one area of Enterprise Information Management that can make a real difference to the bottom-line of a company. It should absolutely be part of any digital transformation initiative and is particularly important for companies with...

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Data Changemakers 1: James Wilkinson, Managing Director UK, VIQTOR DAVIS

by Steve Parry on 31 October, 2017

Please describe a little about your own background and you ended up working with data? Getting into working with data was a happy accident. I had been working for VIQTOR DAVIS, originally Entity Group, since 1989 when they were a leader in the introduction of e-commerce solutions to the...

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Five data properties to consider for great MDM matching results

by Matthew Harris on 25 October, 2017

The importance of understanding your data This blog, the fifth in our series is going to try and spark your imagination when it comes to data, invite you to start to think about fundamental aspects of that data, and how you could use those fundamentals to your advantage. It...

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Comparing algorithms – how to decide which one is better?

by Matthew Harris on 17 October, 2017

Evaluating Matching When implementing an MDM solution, or any matching algorithm, you may be replacing a previous matching algorithm. You will need to be really sure that the new algorithm will be an improvement on the old one. However, knowing which algorithm is better, or worse, is a challenging...

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How to evaluate the performance of matching algorithms – top tips

by Matthew Harris on 19 September, 2017

It can be difficult to evaluate the performance of matching algorithms. However there are several ways to determine how accurate they are and the actionable insight that could be gained by applying them to the data related to your business challenges. In a technical sense, there are 2, and only...

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How to optimise probabilistic matching performance on massive data sets

by Matthew Harris on 13 September, 2017

Within a probabilistic matching* engine (PME) there are 3 key stages: Standardization Candidate Selection Comparison *For an explanation of probabilistic matching see my previous blog here. These 3 key stages, when implemented together, allow for very efficient performance on massive data sets, both in terms of speed of matching...

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Why probabilistic matching is not a black box

by Matthew Harris on 27 July, 2017

What is probabilistic matching and who cares? If you are a developer then you probably (!) already know that it is possible to trace back everything that happens in a probabilistic algorithm and why it happens. If you are a business lead interested in business outcomes only then perhaps...

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A six-step action plan for EU GDPR consent mastering

by Jason Edge on 20 March, 2017

Why GDPR is relevant to all of us All EU citizens, as data subjects, have a vested interest in industry getting GDPR compliance right because it can impact our trust in the organisations who hold and process our most personal information. If you are a Data Controller or Data...

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