Best Data Quality Software for Medium-Sized Businesses

What is Data Quality Software?

Why Use Data Quality Software?

Who Uses Data Quality Software?

Kinds of Data Quality Software

Data quality solutions are usually designed with one or a few specific data sources in mind. They typically focus on improving the quality of data in one or more of the following categories.

CRM — Many data quality solutions are used in the context of CRM because of how important customer and prospect data is for generating and retaining sales. CRM-focused data quality software is aimed at keeping all prospect and client data up to snuff.

Database — Other data quality software is built specifically for creating and retaining quality within databases. These data quality solutions are built to handle a high volume of data entries. The range of data that these solutions can process is wide, since so many different types of data—from personal to transactional and anything in between—can be stored in a database.

ERP — Some solutions are designed for ERP systems to ensure end-to-end financial, business, and supply data are accurate. Though taking a similar span of data types as database-focused data quality solutions, those for ERPs deal with that data through the actual ERP platform directly.

File type specific — The previously noted data sources aren’t the only places where quality matters. Many of us rely on files like spreadsheets or XMLs every day to complete our tasks. Data quality solutions can be built for those specific file types to help businesses organize data that doesn’t get stored in larger systems like those listed above.

Data Quality Software Features

Data identification — Data quality solutions must be able to identify “good" and “bad" data within your data source(s). Data identified as bad is then passed through a cleansing process.

Data cleansing — Cleansing—correcting or removing—bad data can take several forms, be it deletion, modification, merging, or other methods. Deduplication (the act of removing identical entries) and modification (e.g., capitalizing all first letters in names) are some of the most common cleansing methods.

Normalization — For many kinds of data to be useful, the data has to be uniform for processing through other software. Normalization is the data quality process of standardizing all data of a certain type to the same format.

Automation and scheduling — Manually running data identification and cleansing tasks can be a pain. Many data quality solutions allow you to automate these tasks, both routinely and with new incoming data, and they can offer scheduling capabilities for, say, weekly quality checks.

Reporting — Many data quality solutions will include reporting features to give your business an idea of how much data was assessed, what changed, where any issues occurred, and other overhead information you may need.

Additional Data Quality Features

Data appending — While some data may need to be deleted, adjusted, or normalized, other data might have components missing that are critical to the data being affected. Data appending—the addition of component pieces to existing data—can be a valuable asset for making whole, useful data from an incomplete source.

Data enrichment — Businesses sometimes use disparate data sources to build a stronger overall pool of data from which to work. Data enrichment features allow businesses to bring in data from multiple sources, normalize and bolster that data, and use it as though it came from a single source.

Regulatory compliance — For industries where regulations are top priorities (e.g., health care and finance), a data quality solution would need to be able to preserve quality in the data source(s) without breaking regulations. Some data quality solutions take regulatory compliance into consideration for cleaning, which helps businesses stay compliant at the data source.

Potential Issues with Data Quality Software

Normalization inaccuracy — Normalizing your data against an improper format can be a project-halting mistake. Be sure, when setting up any normalization parameters for your data, that the parameters are absolutely correct the first time. Otherwise, you may have to spend extra time renormalizing your data to fix a mistake from the normalization template.

Over- or underbuying — Prior to purchasing, be sure you understand the full extent of capabilities you need (or might need) your data quality solution to have. Buying for only, say, a CRM focus might not be the best idea when your company also stores a lot of business data in databases. Or purchasing a file-type-oriented solution might be limiting if your business plans to expand into a CRM in the near future. But also, be sure to be realistic about the functions you’ll use, else your business might overspend for a product whose functions aren’t all useful to you.

Standards adherence — If your business deals with data that needs to apply to certain regulations (e.g., medical data), be sure that the data quality solution you select can handle the demands that come with regulatory adherence. Not complying with data regulations is an expensive problem that businesses should avoid as much as possible.

Software and Services Related to Data Quality Software

Address verification — Address verification solutions focus exclusively on preserving the validity of any addresses a company might use on a daily basis. Unlike data quality solutions, address verification tools rely on their integration with authoritative databases (usually government address databases) to guarantee addresses are both accurate and properly formatted.

Email verification — Email verification software focuses explicitly on email data validity. Similarly to address verification solutions, email verification tools must connect to external sources to verify email addresses in email lists are valid, in both the local part and domain.