Now when you upload your files you can identify your columns as either DUNS numbers or Global DUNS numbers for inclusion in your matching and De-duping projects.

No longer do you need to worry about numeric conversions or correctly formatting your DUNS numbers, Match2Lists will manage this for you including dealing with the infamous leading zeros which Microsoft Excel is so adept at removing.

Then I think you’ll welcome the new Merge2Lists feature we have just launched within Match2Lists.

With the Merge2Lists feature you can now match & merge your new data against your existing master file, removing duplicates, filling blanks and combining fields with different names to create your new master list, all with the usual simplicity you expect from Match2Lists.com

The data matching process uses our ground breaking Match Visualiser as normal, but you will be presented with new options to customise and select how you would like to merge the 2 lists and determine the structure of your new master file when you choose the download option.

We are currently in the process of putting together a video tutorial on this fantastic new feature, in the mean time you will find guidance on screen when merging your data, Feel free to ping us any questions, comments.  Thank you for continued use and support of Match2Lists.

Just one update to the site to mention – but a good one!

• New De-Dupe Merged Export

When you export a De-Dupe matching project 2 results files are now created for you to download.

File 1.) This is your main cleansed de-duped  list containing records that were not matched and the merged enhanced derived record from each de-duped group of matches.

File 2.) The “Master Grouping” file. This file contains all the matched/duplicate records grouped together by a unique Master Group ID.  It allows you to see those records that where matched and grouped together.

Let me explain a bit about the merged/enhanced de-duped records in File 1.

For each group of matched records, which are in essence the duplicates you found and selected using the Match Visualiser, we create one master record for that group. This master record is derived from the most common values found within the group on a column by column basis.

So if some of the matched records didn’t have a value for some columns and other records in that same group did, then the most common value for that column would be used. This creates a more complete record to use.

In the case of an address Match2Lists will choose the most complete address from one of your de-duped records and use this address, this avoids issues with fictitious addresses being created.

Screenshot showing the 2 results files ready to download

Use it, try it out and let us know what you think. Leave feedback here or send us as email, we love reading feedback from our users.

Until the next time, happy matching.

P.S. The next upgrade will allow you to define your own logic to determine how to choose which value to incorporate in your master record.

The purpose of the Match weighting is to allow you to determine the importance that you want to place on each data description.

The match score is calculated by adding together the score of all the data descriptions and dividing by the number of descriptions.  The weighting determines what percentage of the score for each data description is applied to the match score.

i.e.  (The example below will show how the match weighting affects the match score:)

• Company Name – Weighting 100%
• Address – Weighting 100%
• City – Weighting 50%
• Postcode – Weighting 100%

Each description adds to the match score as below:

• Company Name contribution to MatchScore = (100% / 350% = approx 29%)
• Address contribution to MatchScore = (100% / 350% = approx 29%)
• City contribution to MatchScore = (50% / 350% = approx 14%)
• Postcode contribution to MatchScore = (100% / 350% = approx 29%)

Therefore by adjusting the match weighting you can determine the importance and contribution that each data description adds to the match score and can therefore adjust these weightings to help find new matches emphasising different elements of your data.

• Company ID
• Address ID
• Contact ID

You will find these descriptors at the bottom of the option list, when describing the contents of the lists you upload.

Please note that no transformation logic is applied to these data descriptors, so you will need to apply any transformations or validation before loading your data into Match2Lists.

You will only be able to identify exact matches with these data descriptors.  If you would like more data descriptions options feel free to reply or contact us directly.

A few minor bug fixes this update and one or two design/user feedback tweaks. The Match Visualiser has also had a few updates to boost the whole matching user experience.

• Design Lists/Projects now display “Queued” when first created instead of misleading 0%
• Change Quicker visual response to Accept/Reject match results in the Match Visualiser
• Change Improved matching algorithm on Company Name transformations
• Bug/Fix Some Lists displayed 0% when processing had completed successfully
• Bug/Fix List processing briefly displayed 0% incorrectly before displaying as complete

Thanks again for all your feedback and suggestions on Match2Lists, keep it coming – reply to this post or send us an email using the contact form on the site – honestly, we discuss and read every.

Until the next time, happy matching

This happens because a match has already been approved for your Source List using different criteria, but the approved match is not shown because it is outside of the current matching criteria that you have selected.

I hope this helps, as always your comments welcome.

The simple answer is accuracy, but let’s not leave it at that, I’d like to share with you some of the nuances that we see on a regular basis to help you understand a little of the work that has gone on behind the scenes, to ensure you get the best matching results possible.

In the coming months I will look to discuss country specific nuances in deeper detail and share the learnings and experiences that we have had matching data for large multi-national organisations across the globe. And show some of the approaches we have taken to tackle these issues at Match2Lists.

Needless to say, it’s not a case of one logic rule works for all, different elements of an address need to be processed and interpreted in different ways and country nuances can and do have significant impact on these rules.  By specifying which country your list is related to helps Match2Lists use the most appropriate rules and logic to achieve the best matching results for you.