The State Of Data Quality In 2020 – Everything You Must Know

Melissa IN Team | Data Quality | , , , ,

Today, data is an organization’s most valuable asset. The way this data is managed and analyzed is changing the way business information is used. This has made people at all levels of enterprises alerted to the importance of data quality. Unfortunately, when a survey was conducted, very few organizations surveyed had dedicated data quality teams. The organizations were found to be dealing with multiple data quality issues and lacked the building blocks of data management and governance.… Read More

Melissa Data Publishes Insight on Top Global Data Quality Challenges

Blog Administrator | Address Validation, Address Verification, Analyzing Data, Analyzing Data Quality, Data Quality, Global Data Quality | , , ,

After
30 years in the industry, we’ve seen firsthand what bad data does to good
companies. You’ll be surprised to learn some of the issues companies have with
their data. We’ve collected the top 30 most pervasive data quality issues – and
what you need to know to solve them. Download our free Melissa Data Magazine to
learn more. Also featured: our 2015 Data Quality Catalog, packed with info on
our smart, sharp tools that include free trials, source codes, and unlimited
tech support.… Read More

Big Data Means Big Challenges

Melissa Team | Analyzing Data, Analyzing Data Quality, Big Data, Data Quality | , ,

By Elliot King

One thing that everybody agrees on is that big data is big. But big may be in the eyes of the beholder. Big does not just mean more information. It means more types of information–unstructured, semi-structured, structured, video, audio, graphical, you name it.

And it means more sources of data–sensors, social media, the Web and so on. And it means more potential for data to have a big impact on an organization economically or otherwise.… Read More

Get Used to It: Inconsistent Data is the New Normal

Melissa Team | Address Quality, Analyzing Data, Analyzing Data Quality, Data Cleansing, Data Management, Data Quality | , , , , ,

By Elliot King

Nobody is perfect and neither is corporate data. Indeed, data errors are intrinsic to IT’s DNA. Data inevitably decays. Errors can be caused when data from outside sources are merged into a system. And then, of course, the humans that interact with the system are, well, human.

Unfortunately, despite the best efforts of data quality professionals, the three major IT trends–analytics, big data, and unstructured data–while promising great payoffs generally, promise to exacerbate data quality issues.… Read More

Classifying Data Quality Problems

Blog Administrator | Address Quality, Analyzing Data, Analyzing Data Quality, Data Quality | , , , ,

By Elliot King

Data quality is generally most fruitfully defined in the context of its use. Is the data good enough to allow the process with which it is associated to run efficiently and effectively? For example, is the mailing list you are using for a direct solicitation accurate enough that you can achieve your goals and not generate any unwanted and unanticipated negative consequences?
… Read More