Category Archives: Technical Spotlight

Matchability D Harmony

Technical Spotlight 6: D-Harmony – how suitable is your data for matching?

Posted on 23 November, 2017 by

Gaining a full, 360° view of your customers is a goal that many organisations aspire to. It can allow data scientists and data analysts to deliver business value in many areas, for example to find spending patterns (retail) or repeat offenders (policing). It is reasonably well understood that the...

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Wax on Wax off

Five data properties to consider for great MDM matching results

Posted on 25 October, 2017 by

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?

Posted on 17 October, 2017 by

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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Matching Thresholds

How to evaluate the performance of matching algorithms – top tips

Posted on 19 September, 2017 by

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

Posted on 13 September, 2017 by

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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Black Boxes

Why probabilistic matching is not a black box

Posted on 27 July, 2017 by

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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