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September 23, 2010

Data Integration to Achieve Data Quality

With an ever-increasing amount of data coming from various sources, you are sure to face data quality issues. Should you maintain a huge database of contacts and send notifications, sales offers and other documents to all of them? Isn’t it way too time-consuming and cost-ineffective? Wouldn’t it be smarter to check, which contacts do have a potential of becoming your customers and whether they really exist at all?

One of the ways to tackle the issue is to implement demographics-focused solutions in data integration of your CRM system, Web site membership database, Excel documents, and other data sources. For example, data integration with CDYNE Demographics Web service will allow you to receive relevant information about contacts from any U.S. postal address before you launch an advertising/marketing campaign. With a help of the appropriate ETL software, you can integrate this Web service into your customer database to determine contacts’ age, nationality, income or other characteristics, such as type of residence, average income, average house value, average number of vehicles for residents in their neighborhood, etc.

You can also integrate your CRM or any other contacts database with StrikeIron Email Verification service. This data integration solution allows instant determining the validity of an email address or domain. You can check all of your contacts and send emails to those that actually have them.

Data integration with demographics-focused solutions and address verification software ensures enhanced data quality, which results in better customer service, effective marketing and advertising, and, eventually, increases your revenue.

March 17, 2009

Keeping Your CRM Data Quality at Its Best

Filed under: Data Cleansing, Data Quality — Tags: , , , — Olga Belokurskaya @ 4:59 am

What is one of any organizations most valuable assets? CRM data is probably one of them. Companies are likely to protect and secure their CRM data, but what about its quality? Data quality management is very often one of the most neglected areas of CRM management and one of the major pain areas for administrators and managers.

So trying to learn more about the problem, I came across some practices that could help maintain and enhance the value of the data.

  • Do not ignore bad data until it starts affecting your work. Keep an eye on your data and monitor any changes in its quality.
  • It’s a good practice to manage, normalize, format, qualify and filter out your leads outside your CRM and then have it uploaded so that what is not valuable or quality data does not get added.
  • Periodical data append is important although it involves a lot of manual effort and may seem time consuming.
  • Duplication is possibly one of the most common problems and creates redundancy as well as inaccurate reports. So it has to be kept in check
  • The same thing may be said about expired data that simply junks your CRM. The more regularly you check for expired data, the healthier your CRM is.
  • And, finally, if data cleansing is what helps you maintain your database quality then data enrichment is what will help you enhance your data quality and make it more valuable to the end users.

And here we go with the conclusion: good data management practices, constant cleansing and enrichment process – that’s when your CRM data really becomes an asset.

May 12, 2008

Enriching Customer Information

Filed under: Data Integration, Data Quality — Tags: , — Alena Semeshko @ 9:15 pm

In one of my previous posts I briefly mentioned the possibility of integrating the data received from CDYNE Demographics web service with your customer database and thus extending your customer information. Well, this is now officially possible with CDYNE Demographics connector for the Apatar Open Source Data Integration toolset. The new connector delivers statistical data about customers and allows organizations to identify the ethnic and socio-economic makeup of their current customer base or purchased marketing lists. Aside from that, the connector can be used with any contacts database to determine the age, race, income, as well as type of residence, median income, median house value, or median number of vehicles, all without coding.

Ideal for data modeling and marketing
Whether you need to build customized marketing campaigns and determine ethnic or socio-economic information, this new data quality service from Apatar and CDYNE can be used to tweak your product offerings or advertising messages to reach your desired target market. Non-profit organizations or companies relying on donor support can use this data to match other groups or geographic areas to these traits in order to expand membership base and increase donations and support.

The CDYNE Demographics Web service can help companies better select target groups and learn more about their customers. With Apatar’s visual drag-and-drop interface, this source of useful socio-economic information can be integrated with your database or CRM system in minutes.

You can learn more over here.

April 17, 2008

Apatar CDYNE Phone Verification Connector Released

Filed under: Data Quality — Tags: — Alena Semeshko @ 4:34 am

We all know that the combination of contact data from many sources introduces myriad opportunities for error. There’s this bulk of databases with data entered by different people (and humans are prone to error, right?) at different time… and you have to trust all of it is correct and still up to date? Auch. Checking the validity by hit-and-miss method? Auch. Tired of dialing phone numbers from your CRM and hearing that you’ve got wrong number?

Well, you don’t really have to anymore. New CDYNE Phone Verification connector for Apatar data integration toolset can automatically verify and filter customer phone numbers before they enter CRM applications for you. And it doesn’t matter where your data came from, whether it’s databases (such as MySQL, Microsoft SQL, Oracle), files (Microsoft Excel spreadsheets, CSV/TXT files), applications (Salesforce.com, SugarCRM), or the top Web 2.0 destinations (Flickr, Amazon S3, RSS feeds).

This service identifies the phone numbers in your list that have new area codes following a NANPA split and replaces incorrect area codes. If the area code is incorrect or missing, Phone Verification can be used to identify the error or return the corrected one to update your data.

William Chenoweth, VP Director of Marketing CDYNE Corporation says:

“This new Apatar Connector provides customers the ability to automate their every day data management duties with scheduling features and visual drag-and-drop interface. The more automated the data cleansing process, the less expensive and more consistent the end result will be for your company.”

There’s more over here.

March 26, 2008

Data Quality Ups and Downs

Filed under: Data Cleansing, Data Quality — Tags: — Alena Semeshko @ 3:53 am

Everyone seems to be discussing a recent QAS data quality survey entitled ‘Contact Data: Neglected asset seeks responsible owner’ that questioned over 2,000 organizations worldwive and revealed an increasing number of businesses taking data quality isses seriously and bringing it up to the boardroom level.

“Within the past three years, the number of businesses where the responsibility of data integrity has risen to boardroom level has soared by 16 per cent, showing how important an issue accurate data has now become.”

The survey also stated that:

* the number of employees directly involved in the data quality management has increased by 5% only in the last year
* 23% of the businesses that participated in the survey claimed to use strategica data planning applications on daily basis
* 46% have their own documented data quality strategy

These increasing numbers sure are encouraging and if the growth persists, or even speeds up a bit, we might see a conceptually new, better, cleaner data emerge as an accepted standard of data quality. Now that would be nice, wouldn’t it?

However, with the survey showing 34% of respondents not validating any of their customer and prospect data, there’s still a long way to go to reach the “standard” I’m talking about.

QAS group operating officer Jonathan Hulford-Funnell says: “I find it incredible that organisations are not paying more attention to data quality. It shouldn’t be seen as a burden for middle management, it should be something that every employee in the business takes responsibility for.”

March 21, 2008

Stop Accusing IT for Dirty Data

Filed under: Data Cleansing, Data Quality — Tags: , — Alena Semeshko @ 4:19 am

IT is the easiest to blame for drawbacks and holes in your data, that’s no news. Whenever you don’t get the results and the information you need (provided your business processes are set to present you with quality data), you naturally start looking for someone or something to blame. And IT seems to be the perfect scapegoat. Little do we realize that the problems lie in the business, not in IT.

The thing is, we associate data with IT, consider it a part of IT and don’t realize the two are totally different. Gartner research VP Ted Friedman suggest the solution that should keep the blame off of IT and cause less data quality problems:

“Business needs to be in the driver’s seat,” Friedman said. “At the moment we feel that the focus on the topic is way way too much in the IT camp.”

To advance data quality, Friedman suggests the use of a data steward, who is responsible for benchmarking current levels of data quality and measuring the impact on the business of bad data. The data steward looks at the data transfer processes, making sure, for instance, that the data passes through as few people as possible.

Data stewards will come from a business background, but have good relations to IT, Friedman said. They will only be effective if they are held accountable for their progress, and receive bonuses for meeting quality targets.

March 7, 2008

Data cleansing…cleans data

Filed under: Data Cleansing, Data Quality — Tags: , — Alena Semeshko @ 5:42 am

As I mentioned in the previous post, data cleansing deserves a post of its own. Even more than just one post actually.

Well, it’s obvious data is the key player in business decision-making. Good clean data provides the platform for wise decisions that put the company’s profits onto an upward curve.

Acquiring the right data, however, is not always as simple as it seems. The techniques are many, but the effect from them doesn’t always meet the expectations. That’s where data cleaning technologies come in place. Data cleaning software cleanses the initial data, making it more precise, useable and up-to-date. Techniques used in data cleaning, among others, include:
• Data merge from data sources
• Record matching and synchronization
• Data type and format conversion
• Data segmentation

In this post I want to focus more on record matching and data synchronization.

An example that is often used in this regard is name and address data. Name, address and phone information is the quickest to get outdated and easiest to get wrong. Of course, there are directories and yellow pages that you can always check…but if you do it by hand each time you encounter a mistake, that’s an impermissible luxury in that it takes way (I mean waaaaaaay) too much time.

That’s pretty much the reason and the root of data synchronization technologies. They process the data, compare it to the standard and return a valid quality dataset with all possible mistakes (misspellings, wrong street type extensions, city and state names) eliminated. Apatar’s StrikeIron US Verification data quality service, for instance is one of such tools.

Employing sophisticated matching and data synchronization technology, it first closely inspects each address to ensure its validity and then updates incorrect addresses according to postal standards and cleans customer data before it gets into CSM/ERP systems, databases, flat files, and RSS feeds. It also adds ZIP+4 data, specifying congressional districts, carrier routes, etc. Data cleansing tools of this sort are indispensible in business today. They allow companies to increase productivity, improve sales strategies, and deliver a better and more accurate customer service.