What would you do with an extra 15 minutes in your day? How about a few extra hours?
Give yourself the time to achieve more – fly through your data matching like a superhero with these quick tips:
- Use Match2Lists! Take a free trial (or login) to match data, merge lists and resolve duplicates lightning fast in the cloud.
- Be prepared. Save valuable time by having the lists you want to use ready. Upload them all to Match2Lists in text file format now, and you won’t need to hunt for them one-by-one later on.
- Give your Project a descriptive name that you’ll easily remember. ‘2011 Q1 Customers vs. 2010 Q1 Customers’ is more helpful than merely ‘Customers’. When you’ve got several active Projects, you’ll be glad you named them wisely and know which is what!
- It usually makes sense to designate the shorter of your 2 lists as your Source List, because it’s quicker to match a long list against a short list than vice versa.
- Use the Match Visualiser Auto Approve facility to batch validate results that don’t require individual scrutiny, or Auto Reject results with less than your selected minimum match score.
- Collaborate with colleagues and third parties to accelerate your project over the finish line. Upgrade to an Enterprise Account to share your Match2Lists data and projects online.
We hope you find these quick tips helpful. Until next time, happy matching!
P.S. Have we missed out your favourite trick for super-fast data matching, merging and deduping with Match2Lists? Leave a comment to add your top tips to the list.
Today I read a very interesting post from Ken O’Connor on the concept of “an undertaking-wide common understanding of data quality”. Does your organisation have one of those? Are you sure?
For the first time, this has become a requirement for many organisations. Insurance organisations must now prove to industry regulator Solvency II that their calculations are based on the most complete, relevant and accurate data possible and that everyone involved is working from the same data quality textbook (metaphorically or literally). The Pensions Regulator (tPR) has imposed similar data quality requirements on pensions companies in the UK.
Even if there’s no specific obligation for your company to toe that line, it’s sound advice for any business.
Reaching a Shared Understanding of Data Quality
How is “Data Quality” defined and measured in your work? The words are so simple that everyone assumes they know what data quality means. But if everyone’s reaching their own definitions, how do you know they’re working in the same direction?
Clear goals and key performance indicators are vital, as is a shared terminology. It’s also essential that members of the board or data governance committee have the same understanding of data quality as the members of the DQ team.
When there are problems, everyone needs to grasp why. When proactive data quality controls are designed and implemented, the brief must match the requirements of the end users as well as the commissioners. When there’s a win, the board must understand what was won, why it matters and what degree of effort and dedication was involved.
Quality in All Things (Especially Data)
The greatest benefits and returns on investment are earned not only through an undertaking-wide understanding of data quality, but by the sheer power of an organisation-wide enthusiasm for optimising quality in all undertakings.
So what does data quality mean to you and your team? Is it an obsession, a labour or a seemingly unreachable mirage? Whatever your data quality situation, Match2Lists can help – try our lightning fast data matching, merging and deduping tools free today!
Get more return on your investment with Match2Lists When you choose a data quality tool, it's important to know what return you expect on your investment. How do you work out your ROI?
Read More Post a comment (0)A couple of updates to inform you all about today.
- Design Slight changes to the look and feel of the site
- Change DeDupe Project processing
Recently we took a look again at our processing of DeDupe projects. Although they were working fine they were taking longer to process than we would have liked, and some of our customers had also expressed similar thoughts.
So we set about improving the whole process. It took a few days testing, tweaking, trying to get the best performance possible – but we finally did it.
DeDupe Processing is now alot quicker.
Those of you who have run DeDupe projects in the past go check it out and lets us know what you think. Those that have yet to run DeDupe projects now’s a great time to do so and enjoy the speed, simplicity and accuracy that Match2Lists has to offer.
As always your feedback helps make changes. Any suggestions or improvements you think we can make to Match2Lists please let us know, we discuss and read every one. Many thanks.
Until the next time, happy matching.
Big data is the latest buzzword across the business world. How are you handling your company’s data volumes? What are the implications of big data for the way you work?
Big Data: The Facts
‘Big data’ is a term applied to datasets too large for a typical database application to handle. That’s a fairly flexible definition, so try this: if you’re wondering whether or not you have big data, then you probably haven’t. But many companies are managing a petabyte* or more of stored data already; even if yours isn’t among them, your future success may depend on your understanding of big data issues.
Big data is changing the way we do business. Cloud computing and other technological developments make it possible to store, compare, combine, deduplicate and analyse enormous volumes of data. Big data analytics supports and even automates fine-tuned decision making processes, adding value while conserving resources.
Managing Big Data
Compare the 40% projected growth in global data generated per year to the 5% growth in global IT spending*, and you can see that many companies will fail to keep up. Those who make the best use of this data torrent will increase their market share at the expense of their less forward-thinking peers.
Large companies and governments are therefore seeking out innovative ways to maximise the return on their big data. The more prudent smaller companies already have strategies in place to deal with increased data volumes, and to apply big data thinking in their business.
The Way Forward
Legacy systems and compatibility issues often stand in the way of seamless integration and sophisticated deep analysis of big data. There is now an exponentially increasing need for new technologies, methods and business models that make big data quality swift, smart and simple.
One very effective way to match, merge and deduplicate big datasets is with a Match2Lists Enterprise Account. Processing billions of potential matches in the cloud takes only seconds, while collaborating with colleagues or third parties speeds validation and ensures accurate results. No matter how big your data or where you keep it, if you can upload it to Match2Lists in text file format then your data matching, merging and deduping projects will be a breeze.
Add Value to Your Data
Whether you’re working with big data or one small list, Match2Lists has an account to suit your needs. If you’re not already using it, take a free trial now to see how it works for you!
*Source: McKinsey Global Institute
I once worked at a small local branch of a very large yet antiquated organisation. Budgets were tight and the team’s IT knowledge was slim, yet we had a high volume of customer data to process on a daily basis. Our survival depended on timely, accurate sales and marketing intelligence; we didn’t have it. It was the Dark Age of Data Matching, and it was not so long ago.
Data Quality doesn’t have to be a chore
That single issue became the hot potato of our working lives. Our manager bought some matching software, but nobody knew how to use it and there was no budget left for extra training. Nobody wanted to sit in the corner all day, manually matching, deduping and merging our records. But somebody had to. If only Match2Lists had been available back then…
Make Data Matching simple
Match2Lists offers enough detail and customisation to please the most expert user, but with an easy intuitive visual interface that even a novice will understand in minutes. It’s as simple as uploading your lists, letting Match2Lists do most of the hard work, then approving and downloading the results. My inexperienced team really could’ve used that kind of help to lead us out of the darkness.
Get better results faster
By blazing through billions of potential matches in seconds instead of spending hours manually cross-checking match results from Excel and Access, we could have achieved much more with our data. With Match2Lists’ lightning fast cloud based tools, we’d have had more time to spend on other vital tasks. Match2Lists users always comment on the impressive accuracy and number of matches found with Match2Lists, too.
Not everyone knows this…
Today, I’m going to call up my old manager and explain how Match2Lists will lighten her data quality load.
Think about your colleagues and contacts; is there someone who could benefit from using Match2Lists? Maybe they haven’t heard how much faster and easier we’ve made data matching with our cloud-based tools. You could be their hero – just point them to our free trial at www.Match2Lists.com!
To make sure anyone who isn’t already using Match2Lists gets a chance to explore this awesome cloud based data matching tool, we’re proud to announce you can now take a free trial.
What’s Included?
You get to use all the features of a Pay-As-You-Go or Monthly Subscription account, with 1000 free match download credits to get you started. Once your free credits are all gone, it’s up to you: choose an automatic upgrade to Pay-As-You-Go, or get even lower costs per match download with a Monthly Subscription or an Enterprise Account.
So go ahead and check it out – instant access to lightning fast data matching, merging lists and removing duplicates with Match2Lists, no credit card required. With all this power at your fingertips, we think you’ll definitely like it!
How Was It For You?
After you’ve taken Match2Lists for a test drive, we’d love to hear your opinions: What did you like the most? How do we compare to your previous solution for data matching, merging and deduplication? Is there anything we could do to make Match2Lists even better?
Just leave us a quick comment here on this blog post, or contact us to share your thoughts.
Happy Matching!
P.S. Don’t keep the joy of Match2Lists a secret – if you know someone who works with data, let them know they can try it out at www.match2lists.com. You’ll be their hero once they see how fast and easy their task becomes. And we’ll be their new favourite tool. Thanks!
As of version 3.2.0
Some big news regarding the latest update to Match2Lists.
- New Syncing of output columns in DeDupe and Merge projects
- Bug Fixed an issue with lists containing NULL in first column
“Syncing of output columns” – now when you output your DeDuplication and Merge 2 Lists projects you can specify which columns from either your Source or Match lists should be outputed based upon a shared logic.
For example, say you were de-duplicating a list of contact details and found some duplicates for a Mr Joe Bloggs. Most of the duplicates found where due to some misspellings and initials used rather than his name but you were able to match them on other contact and address fields.
When using the Match Visualiser you had seen that the record with the fullest name “Mr Joe Bloggs” also had the most complete Job Title, Telephone, Fax, Email and Web address details and wanted to have those fields used in the output result.
Now instead of setting up the logic for all those individual fields you can simple set the logic to “Sync with FullName” for these fields and the output fill contain the Job Title, Telephone, Fax, Email and Web address for the record “Mr Joe Bloggs” ignoring these fields for the other deduped or merged records for that match.
Its a very powerful and useful addition to the tool and we will not doubt be creating a more in-depth look at this particular feature.
Until the next time, happy matching.
