Guide to Data Quality Management #3 – The main features of DQM tools

Data Quality refers to an organization’s ability to maintain the quality of its data in time. If we were to take some data professionals at their word, improving Data Quality is the panacea to all our business woes and should therefore be the top priority. 

At Zeenea, we believe this should be nuanced: Data Quality is a means amongst others to limit the uncertainties of meeting corporate objectives. 

In this series of articles, we will go over everything data professionals need to know about Data Quality Management (DQM):

1. The nine dimensions of Data Quality
2. The challenges and risks associated with Data Quality
3. The main features of Data Quality Management tools
4. The Data Catalog contribution to DQM

One way to better understand the challenges of Data Quality is to look at the existing Data Quality solutions on the market.

From an operational point of view, how do we identify and correct Data Quality issues? What features do Data Quality Management tools offer to improve Data Quality?

Without going into too much detail, let’s illustrate the pros of a Data Quality Management tool through the main evaluation criteria of Gartner’s Magic Quadrant for Data Quality Solutions.

Connectivity

A Data Quality Management tool has to be able to gather and apply quality rules on all enterprise data (internal, external, on-prem, cloud, relational, non-relational, etc.). The tool must be able to plug into all relevant data in order to apply quality rules.

Data profiling, data measuring, and data visualization

You cannot correct Data Quality issues if you cannot detect them first. Data profiling enables IT and business users to assess the quality of the data in order to identify and understand the Data Quality issues. 

The tool must be able to carry out what is outlined in The nine dimensions of data quality to identify quality issues throughout the key dimensions for the organization.

Monitoring

The tool must be able to monitor the evolution of the quality of the data and warn management at a certain point.

Data standardization and data cleaning

Then comes the data cleaning phase. The aim here is to provide data cleaning functionalities in order to enact norms or business rules to alter the data (format, values, page layout).

Data matching and merging

The aim is to identify and delete duplicates that can be present within or between datasets.

Address validation

The aim is to standardize addresses that could be incomplete or incorrect. 

Data curation and enrichment

The capabilities of a Data Quality Management tool are what enable the integration of data from external sources and improve completeness, thereby adding value to the data.

The development and putting in place of business rules

The capabilities of a Data Quality Management tool are what enable the creation, deployment, and management of business rules, which can then be used to validate the data.

Problem resolution

The quality management tool helps both IT and business users to assign, escalate, solve, and monitor Data Quality problems.

Metadata management

The tool should also be capable of capturing and reconciling all the metadata related to the Data Quality process.

User-friendliness

Lastly, a solution should be able to adapt to the different roles within the company, and specifically to non-technical business users.