- Data Quality
Contact data quality is all we do – and we do it better than anyone in the industry. Since 1985, our foremost goal has been helping customers achieve the highest quality data at the most affordable price.
We understand that without clean, correct, and complete contact data, it’s a challenge to target and communicate with the right audience. We offer global address, phone, email, and name, identify verification solutions, and data enrichments to maximize the effectiveness of business intelligence, big data analytics, and omnichannel marketing initiatives.
More than 10,000 companies worldwide rely on Melissa Data to gain and maintain a single, accurate, and trusted view of critical information assets.
Full Spectrum of Data Quality
Our full spectrum solutions work across the entire data quality lifecycle – at the point-of-entry to prevent bad data from entering your systems in the first place, to continuously monitoring and updating your data periodically to combat stale data. With our range of flexible and affordable solutions, you can be confident your data is reliable, accurate, and complete.
Hover over a circle below to learn more about each crucial step in the data quality circle:
Profile & Measure
Gathering Data about your Data (MetaData)
Profiling is the first step in the Gartner defined steps in data quality. It lets you gain a better understanding of your data, what the problems are, and gather important statistical information (such as number of blanks, distinct values and the max length) for generating reports and analytics used for business intelligence.
Melissa Data’s Profiler discovers existing weaknesses in your database.
Profiling Over Time
As we continue to get new data, new data quality problems arise. Data changes all the time, and so do the rules that govern them. This is why the maintenance and governance of data is essential to the success of data quality implementations. In order to have the best and most accurate information in our data warehouse, continuous data quality is a must.
Data Quality is not just a one-time execution. It is a continuous process, to make sure that your data is clean and stays clean.
See how Profiler continuously analyzes warehoused data to enforce data quality.
Parse & Standardize
Organizing and Normalizing your Data
Bringing the concept of data scrubbing to the next level involves the processes of parsing data into more proper and manageable fields, as well as implementing normalization rules to standardize casing and formatting throughout the entire database. In this case, we can see how the address and name are divided into its individual components, while being formatted and cased nicely.
Learn about Melissa Data’s unique solution to identify, extract and organize improperly fielded and/or free form data.
Validating and Correcting your Data
Implementing validation routines and cleansing the data is the next step in the data quality process. Whether it’s the removal of special characters, correcting bad addresses and other contact information, or implementing specific business rules for validation, the cleansing step makes sure that your data are both scrubbed and validated in accordance to your specified standards.
Search a collection of data cleaning solutions to clean contact data, verify identity and improve mail & shipping.
Appending Additional and Useful Information
Enrichment is the process by which additional data can be appended to the data we already have. Having good clean data is one thing, having complete, comprehensive, and enriched data brings it to yet another level in the data quality process. Here are a few of the enrichment information you can get:
• Address Latitude, Longitude and Census Information
• Address Plus4 and Delivery Point
• Missing Name, Phone Number and Email Address
Discover Melissa Data’s solutions to enrich your data to drive greater value.
Match & Merge
De-Duplication and Survivorship
A very common, yet extremely problematic dilemma in data are duplicate records. Associating matching records (record linkage) and getting rid of duplicates play a huge role in the entire data quality process. This can be achieved through the association of similar records, which the Melissa Components handle nicely through fuzzy matching. Once duplicates are found, they should be consolidated into a single Golden Record, selecting the best piece of information for each column – which can also be achieved through the Melissa Components.
Learn how Melissa Data’s MatchUp’s deduplication solution can improve database integrity by identifying duplicate records and helping create a single customer view.
Melissa Data Products
Delivered both directly and through ISVs and systems integrators, our data quality products are designed to ensure the accuracy and reliability of your contact data.
Melissa Data Professional Services
Melissa Data backs our industry-leading solutions with a comprehensive and customizable set of professional services, including product training, proof of concept development, integration consulting, and custom development services to help our customers improve the value of Melissa Data products and maximize ROI.
Solving Real-World Data Quality Problems
Find out how we solve real-world problems for customers every day in the following industries: e-commerce, nonprofits, healthcare, banking, hospitality & travel, insurance, CRM & call centers, telecommunications.