Recently in Data Steward Category

Get in the Contact Zone

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If you're looking for that perfect all-in-one solution that combines the power of easy integration and Melissa's full spectrum of data quality solutions, then you need to get in the Zone - The Contact Zone®.

Contact Zone employs all the customer data management tools you'll need to help provide consistent, trusted, accurate data across the enterprise - all in one single platform for effortless integration. 

Plus, it's powered by Pentaho® Data Integration (PDI), which gives Contact Zone a simple, graphical user interface, dynamic templates, administrative features, and so much more. Collect data from any source, cleanse and transform it, and gain immediate insight for meaningful use.

Record Matching Made Easy with MatchUp Web Service

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MatchUp®, Melissa's solution to identify and eliminate duplicate records, is now available as a web service for batch processes, fulfilling one of most frequent requests from our customers - accurate database matching without maintaining and linking to libraries, or shelling out to the necessary locally-hosted data files.

Now you can integrate MatchUp into any aspect of your network that can communicate with our secure servers using common protocols like XML, JSON, REST or SOAP.


Select a predefined matching strategy, map the table input columns necessary to identify matches to the respective request elements, and submit the records for processing. Duplicate rows can be identified by a combination of NAME, ADDRESS, COMPANY, PHONE and/or EMAIL.


Our select list of matching strategies removes the complexity of configuring rules, while still applying our fast and versatile fuzzy matching algorithms and extensive datatype-specific knowledge base, ensuring the tough-to-identify duplicates will be flagged by MatchUp. 

The output response returned by the service can be used to update a database or create a unique marketing list by evaluating each record's result codes, group identifier and group count, and using the record's unique identifier to link back the original database record.


Since Melissa's servers do the processing, there are no key files - the temporary sorting files - to manage, freeing up valuable hardware resources on your local server.


Customers can access the MatchUp Web Service license by obtaining a valid license from our sales team and selecting the endpoint compatible to your development platform and necessary request structures here.

Data Quality 101: The Ultimate Guide for Data Stewards

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"Ninety percent of everything is crud." This popular phrase is known as Sturgeon's Law (with tongue firmly in cheek). Could the same be true about the quality of your corporate data assets?

It's no surprise that organizations still struggle with some level of bad data. Many haven't yet initiated a data quality or data stewardship plan. Some may not even know where to start; who's responsible for it; or how to sustain a data quality program over the long haul. Some may not even realize they have burning issues with their data - until it's almost too late to fix the problem.

The Data Steward Companion, written by leading industry analyst Elliot King, answers these questions and offers insight into the complex role of a data steward - how to create a data maturity model to effectively define, manage and optimize their data management and data quality activities; who should be in charge of setting data quality goals; how to develop key performance indicators to measure data quality; why justifying a data quality plan is important; and much more.

The Ultimate Guide for Data Stewards eBook Download

Low Cost Ways to Improve Data Quality

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By Elliot King

Elliot King

In many organizations, when one side of the house starts talking about improving data quality, the other side of the house starts hearing one thing and one thing only--costs. They assume that initiating a data quality program is going to be a heavy lift financially, requiring consultants, investments in technology, training and more. And even if those
responsible for containing costs agree that improving data quality is important and can have a real impact on the bottom line, they often wonder if the impact will be big enough to justify the investment. Investing in data quality is seen as a choice and there may be more effective ways to invest limited resources.

But data quality improvement does not have to be high cost. Data quality rests on people and processes as well as technology and by focusing attention on the first two, companies can make significant progress in improving data quality inexpensively.

Perhaps the easiest first step is to make somebody responsible for data quality--appoint a data steward charged with monitoring data quality or at least trying to determine how data quality could be monitored. Depending on the size of the organization or the department within an organization, this does not have to be a fulltime job; nor does the person initially have to be an expert. In the beginning, data stewards can educate themselves about the quality issues.

The next easiest step is to have the data steward poll employees responsible for entering data about where mistakes happen. Front line personnel represent a deep repository of knowledge about which they are seldom asked. If a process or data screen is broken leading to data entry errors, they will know.

Then, companies should focus their efforts on safeguarding the quality of their most important data. Organizations do not have to do everything at once. Just knowing what is most important is a critical step forward.

These ideas are not completely cost-free but they certainly are not expensive. The key is starting somewhere, even if the first couple of steps are very small.

It Takes a Team

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By Elliot King

Elliot King
As the cliché has it, data is an organization's most valuable asset. But the question is--who guards those corporate jewels? Is it the IT staff that is charged with making sure the information infrastructure supports the business correctly? Is it the database developers and administrators who are the front-line data professionals? Is it the business
users who need accurate data to make sure tasks are executed as anticipated? Or is it the executive staff, which is in the best position to have a birds-eye view of the entire operation?

In practice, safeguarding data quality requires an interdisciplinary team approach, with different players coming from different parts of the organization. As with most teams, you need a team leader or program manager. This person is charged with supervising the entire data quality improvement program, recommending what resources are needed and where those resources should be invested.

In addition to the program manager, most data quality initiatives require a project leader, a person responsible for addressing specific data quality issues at hand. Each project team has at least three specific roles that need to be filled with representatives from the IT and business staffs.

The IT professionals must have the technical ability to fix what might be broken and the business personnel must serve as the subject matter experts, understanding the characteristics the data must have to get the job done. Finally, there should be a data steward to set policies, procedures and standards to improve standards.

Finally, one last critical role must be filled--executive sponsorship. Those of you who are sports fans may have noticed that some teams are good year after year while others aren't. The difference is in the ownership (think the Los Angeles Dodgers for a case study in good and bad ownership.) A data quality improvement team cannot succeed without a strong commitment from the top.

What is a Data Steward and Do You Need One?

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By Elliot King

Elliot King
The metaphor of "ownership" has become popular in organizations and their IT shops. Companies have "application owners" and projects that are "owned" by this or that group. So that raises the question, who "owns" your data?

The right answer is that nobody "owns" the data. Data is a resource that must be shared across an organization. Data flows from the point of creation--perhaps capturing contact information on a website or importing a third-party mailing list--through staging, consumption, storage and archiving. At each step of the way, a different functional group within an organization has to be able to use the data in different ways.

To insure that data meets the standards needed by each stakeholder in the data lifecycle, companies have to implement enterprise-wide data management policies and procedures. A typical policy might say that all contact information must conform to a specific format. Don't assume that to be the case in your organization. Unmonitored, your sales department, service organization and billing department could easily capture names differently. Indeed, in larger corporations, different sales organizations might have different formats for names and addresses.

Data stewards both develop those policies and create mechanisms to insure that the policies are enforced. On the flip side, the data steward should be accountable for enterprise data quality and the advocate for data quality initiatives.

Data stewardship is neither an easy job nor an easy job to fill. The foundational technical skill is a deep understanding of specific business functions, the data associated with those functions and the processes that rely on the data.

Those technical skills have to be coupled with a strong set of interpersonal skills as, by definition, data stewardship requires interacting with a wide range of stakeholders (often including other data stewards). Finally, regardless of the formal position they hold, data stewards need to be able to establish their authority as the role sometimes calls for stepping on other people's toes.

Stewardship is quite different than ownership. But if your organization has data, it probably needs a data steward.