• Bug List loading failing on certain characters
• Bug Rounding issue when applying Auto Approvals in Match Visualiser

When loading a list with certain Unicode characters the backend process was failing, unable to handle these characters. This has now been updated and the issue resolved.

In the Match Visualiser the scores displayed and the backend scores did not match due to a rounding issue in the frontend. This meant that entering some Auto Approval values resulted in unexpected behaviour. This has now been corrected.

Until the next time, happy matching.

IT security can be expensive. For years, large enterprises have used IT service providers for security and data storage as well as for technical support. Smaller businesses often ran their security in-house, wary of the high cost of outsourcing data operations and security to an outside IT management company.

The Rise of the Cloud

The advent of cloud computing for data quality and other data operations has allowed small and medium-sized companies —the majority of businesses worldwide— to take advantage of massive distributed processing power to achieve rapid results, greater scalability and impressive cost savings. The service standards once available only from expensive IT consultancy and management firms are now available on demand to smaller businesses around the world through the cloud.

Small businesses are not the only ones migrating to the cloud. IT management service providers have brought many large enterprise clients with them to the cloud as the standard IT service range expands to include cloud storage and software-as-a-service offerings. The convenience of storing, accessing and processing data on a service provider’s servers via an Internet connection is appealing to most businesses. However, many are concerned about issues of cloud security, and enterprise needs security to survive.

A Question of Security

With the rise of big data, increasing rates of cybercrime, and tightening data protection and privacy legislation, many businesses of all sizes face a dilemma: more data than ever is at greater risk than ever, yet it must be secured more thoroughly than ever.

Security was, until recently, a deal-breaker for some companies considering cloud services. Survey results from technology research group IDC show that in 2008, security was the main concern that prevented adoption of cloud services for business. In 2011, however, less than half the responding companies cited security worries as their leading concern, and the number of businesses already using cloud computing services is rising swiftly.

Safer in the Cloud

By outsourcing to the cloud not only data security and storage, but also the processing locations of the data, vital files may be doubly protected from hacking. Cloud computing security features may include firewalls; high strength data encryption; customisable user privileges; activity logs and auditing; anomalous activity notifications; and secured collaborative workspaces.

Even entry-level cloud computing services, and those not explicitly security-related, can improve a business’ overall security because cloud services are maintained and secured by the provider rather than relying on the user to keep antivirus programs up-to-date and apply recommended software security patches. Jim Reavis of Cloud Security Alliance has commented that for small and medium businesses, migrating to the cloud “ends up being almost in all cases a security upgrade.”

If you’re curious about cloud security and how Match2Lists helps to protect your data, feel free to get in touch or take a look at our security statement. You can also take a free trial of Match2Lists to see how it works for you.

Happy matching!

• New Global List Country
• New Scrutineer User account type ^{* Enterprise}
  
• New User permissions on Projects ^{* Enterprise}

When adding any list into Match2Lists it has always been the case that you had to specify the Country that best described the data being loaded so that we could best apply country specific algos and transformations as part of the matching process.

These transformations and country specific procedures are only necessary on regional and geographical data such as contact and address details as well as local Company names and identifications.

Now if your lists do not contain such data or a mixture of many countries simply select “Global” in the “List country” dropdown when uploading a list. Match2Lists will then process the list accordingly. One thing to note is, like country specific lists, you can only create projects using lists of the same “List country”.

Having been talking to some of our Enterprise customers we’ve added a new User Account Type – Scrutineer.

The Scruntineer User Account can access and only work on Projects that have been assigned to them. They do not see any Lists or other Projects you may have in your account.

With the creation of the Scruntineer it is now necessary to grant permission on Projects. A new section to the Create a Project screen has been added for this. All non-Scruntineer users within your organisation are automatically granted access to the Project which helps keep the process quick and easy.

The new User Access Permissions section for Projects.

You can change access permission to existing projects by making the necessary changes to the new “User Access” section of the Project Details screen.

Tip: To make a project private so no other user sees or has access to it – simple uncheck all users. You can change these settings at anytime be going back to the Project Details screen.

That about wraps it up for this update, some great changes and advancements continue to be made – we hope you think so too, please let us know what you think.

Until next time – happy matching.

Bad Data = Big Costs

A recent report from Artemis Ventures indicated that poor data quality costs the United States economy roughly $3.1 trillion per year. To provide some perspective on this unimaginably large figure, that’s twice the size of the US Federal deficit. An estimate from the US Insurance Data Management Association puts the cost of poor quality data at 15% to 20% of corporations’ operating revenue.

Meanwhile in Australia, David Howard-Jones of management consultancy Oliver Wyman confirms a similar trend. Speaking at an Institute of Actuaries of Australia conference, he said, “Overall estimates of the costs of poor data quality are 15 to 25 percent of operating profits for insurers and potentially even more for large banking groups.”

Low Quality = Low Efficiency

Even more jawdropping than the cost of poor data quality is the typical lack of action to improve it. The flawed data may be easily corrected with a cloud data quality tool like Match2Lists, but flawed data quality systems are often allowed to continue producing data that could have been better. And that’s when the wider effects of low data quality come into play: where there are data quality issues, the data is less trusted. Decisions become overly conservative and opportunities are missed due to lack of timely, reliable, usable data.

Failure to attend to the sources of data quality underperformance leads to unmanaged workflow friction that can slow a business’ operations and reduce their effectiveness. In turn, revenue and growth will be limited, and the economy as a whole will be carrying one more data dead-weight.

Hollis Tibbetts, managing director and principal analyst at Artemis Ventures, commented, “About half of IT executives consistently agree that data quality and data consistency is one of the biggest roadblocks to them getting full value from their data, yet consistently organizations fail to address this issue.”

So, what now?

1. Get Clean

Use Match2Lists to compare and match data, remove duplicates from your data, and merge lists of data without introducing new duplicates. It’s swift, smart and simple.

2. Stay Clean

Keep working. Data quality isn’t a one-off task or even a static goal to work towards; it’s a continuously evolving set of strategies that require ongoing contemplation and implementation. Evaluate your data and processes regularly, and fix any quality issues that you find.

3. Fix The Plumbing

Look upstream. Look downstream. Where is your data coming from, and how does it reach its users? What happens to it along the way? Now look again. Did you answer those questions by observing, or did you make assumptions? What really happens to your company’s data may not be what you intended or what you anticipated. Identify the problem points and involve the relevant teams in developing solutions.

Don’t leave poor data quality unmanaged. Take a free Match2Lists trial to check your data for duplicates and inconsistencies now.

Happy matching!

A lot of new features have been added to the site in this update all with the intention of making the site and the tool even easier to use.

• New Account Homepage
• New Project Selection page
• New Filters added to Lists and Projects pages
• Change Updated Advanced DeDupe output customisation
• Change “Tel/Fax” datatype has been separated to just “Telephone”…
• New …a new datatype of “Fax” has been created

One of the first things you will notice when logging into your account is the new homepage. Here you will find out latest feeds from the Blog as well as our latest Tweets from Twitter so you can keep up to date with the latest goings on at Match2Lists. Please follow us and join in the conversations, we love hearing from our users.

Also on the homepage is where we will announce any large updates or important new features coming soon to the tool – so check it regularly as we’ve got some great things lined up…!

The new Project Selection page was added to aid in selecting the correct project type for your data matching requirements. If you were unsure as to what or why you would want to Merge 2 Lists or what actually happens in a DeDupe project, this page explains these things to you so you know exactly what project type you choose to get the results you need.

One of the features our Enterprise customers have asked for an easier way to manage the large number of lists and projects they have. So we added filters to the Lists and Project pages so its quicker and easier to find the list or project required. We released this to our Enterprise customers and they loved it so much we thought we would add it to everyone’s account. We think its a great new feature so why not tell us what you think, we would really like to get your feedback.

As always your feedback helps make changes. Any suggestions or improvements to the features we’ve added or what 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.

There are some common data quality misconceptions that, if they become entrenched in an organisation, can lead to a costly data downfall. If data quality is misunderstood, then quality suffers and the efficiency of every department from IT to PR takes a hit. That means less return on the investment you’ve made in obtaining and maintaining all that data!

Take a look at this quick list and see if your business has fallen for an expensive data fairytale:

1. Data quality is all about bad data that “got into” your systems

If you do have inaccurate or untidy data, we can help you get clean and stay clean. Sign up for a free trial of Match2Lists’ cloud data quality tools to match, deduplicate and merge your datasets.

Incorrect data certainly do pose a data quality problem, but data quality is much, much broader than that. What about data that can’t be accessed and analysed with ease, or data that is only consistent within its own little (or large) silo? What about data that is highy accurate and easily available on the whole, but not reported in a format that includes all the relevant data points each user needs?

2. You can solve all your data quality issues by fixing the bad data

Data quality is an ongoing process, not a target. Identifying and improving problem points throughout the process will yield far greater rewards than merely identifying and improving inaccuracies in the data itself. Without an agreed organisation-wide data quality policy and strategy, bad data will be the least of your concerns.

3. Data quality is too complicated for normal people

In fact, the essential principles of data quality are relatively simple. Everything is focused on trying to make your data as accurate, relevant, consistent, comprehensive and timely as possible. With cloud data quality tools like Match2Lists, anyone can swiftly match, merge and dedupe datasets on demand. You don’t need to know how it works, unless you want to.

Some of us might like to debate the finer points of fuzzy matching algorithms, master data management, and business rules. But Match2Lists is designed to make improving the quality of your data swift, smart and simple.

4. Truth is Data; Data, Truth

That’s beautiful, but it isn’t true. Truth, though a metaphysical concept, has to be true to real life when we're talking about data quality. But the real world is out there, and the data is inside your organisation. You can get awfully close to some pieces of truth with very good data and even better analysis and interpretation. However, the data in your hand are never quite the same as the facts in the field.

5. It’s all someone else’s fault

Please, don’t blame IT. Don’t blame the data operations team. Don’t blame anybody, it’s a waste of your time. Start here instead.

Data quality is a company-wide endeavour. There’s a growing trend for business data end users to process and analyse their own data, rather than receiving ready-made reports from a data analyst. Therefore everyone who creates, handles, oversees or uses the data needs to know their data quality aims, and the business processes governing and applying the data must be designed to maintain data quality in all its aspects.

For a swift, smart simple solution to some of your data quality issues, see what Match2Lists can do. Would you like to compare and match datasets, merge your data or remove duplicates from data? We can do that for you.

Happy matching!

We love the smell of feedback in the morning! Every time a Match2Lists user lets us know what they think of our cloud-based data quality tools, every single person on the Match2Lists team wants to hear all about it. So we were very excited to see some of our favourite data superstars testing out Match2Lists and sharing their opinions.

Match2Lists Users Say…

Data Quality and Master Data Management expert Henrik Liliendahl Sørensen kindly tweeted that he was “impressed by the features and user interface on the SaaS Data Matching tool from @Match2Lists.” We’re honoured to receive such approval from one of the leading figures of the international Data Quality & MDM community.

Information Management consultant Rohin Bhargava also devoted a blog post to reviewing Match2Lists, giving us some excellent constructive criticism and making us feel like proud parents! He writes, “All in all I was very impressed with the tool… you really have a winner.” It’s great to hear that users agree on one thing: Match2Lists works.

It’s awesome to hear the opinions of Match2Lists users, from data management experts to home business owners. Thanks for checking us out, and thanks for your comments! Every bit of feedback we receive is genuinely appreciated.

What we’d really like to know now is… what do you think of Match2Lists?

Take a free trial now — it only takes a minute to sign up, and you don’t need a credit card.

Then let us know your opinions and ideas: What did you like? Was there anything that you disliked? How can we make Match2Lists serve your needs even better?

Where to Talk To Us

You can contact us online any time, leave us a comment right here on the blog, or follow @Match2Lists on Twitter.

We’re looking forward to hearing from you – happy matching!

We read the latest Gartner Magic Quadrant for Data Quality Tools with great interest as always, seeing which data quality tool providers have been identified as leaders, challengers, niche players or visionaries. Gartner’s Magic Quadrants provide an excellent overview of the field for decision makers; however, as a recent post by Liliendahl on Data Quality pointed out, the majority of the world’s businesses do not use a Magic Quadrant solution for data quality.

Why no Gartner Magic?

Why? Simply put, because Gartner only include in the Magic Quadrant solutions that match their criteria. Match2Lists, for example, performs 3 essential tasks: data matching, merging lists and resolving duplicates in data. This makes us too specialised a tool for inclusion in the Magic Quadrant for Data Quality Tools, but an excellent option for anyone with data matching and data quality needs.

Let’s take a look at the key points made by the authors of the 2011 Gartner Magic Quadrant for Data Quality Tools. Here’s what you need to know:

High Demand for Data Quality

Business intelligence (BI) and master data management (MDM) require a constant high level of data quality activity, creating ever-increasing demand for tools to ease this workload and reduce the lead time on data quality projects. The sharp rise in the number of information governance initiatives implemented in the last year has driven further demand for data quality tools.

Uniting Data Quality, Integration and Management

Data quality tools now incorporate ever more data integration and master data management features, as businesses seek to streamline their data quality processes. Tools that can merge data intelligently and remove duplicates from data without sacrificing any useful information will therefore be increasingly popular in all industries.

Data Quality Tools for Data End Users

Gartner cautions organisations evaluating data quality solutions to “consider not only the functional capabilities… but also the degree to which this functionality can be readily understood, managed and leveraged by business resources rather than IT.” Intuitive interfaces and data visualisations allow the data end user to achieve success without the resource drain of regular in-house IT support.

New Approaches to Pricing and Delivery

Both interest in, and actual deployment of, offsite models grew rapidly in the last year. More organisations now seek hosted, SaaS and cloud-based data quality tools that provide the specific desired data quality capabilities on demand. Over 20% of the organisations surveyed used some sort of “as a service” solution alongside (or instead of) traditional software with onsite installation. This proportion is expected to grow as cloud computing solutions apply pay-per-use and pay-on-results models to their pricing.

Here at Match2Lists we’re very pleased to see that other data quality providers are beginning to offer cloud based solutions. And we designed our tools from the very beginning to appeal to both the data end user and the data specialist, so it’s great to hear that more data quality software developers are following suit. Here’s to another great year in data quality!

Source: Gartner (July 2011)