Selecting the Right Data Quality Solution for your Organization

The massive growth and reliance on data from a range of internal and external sources now being used to underpin business critical decision making has led to an increasing need for data quality management tools and services to engender certainty that the data is fit for purpose at all times. Used effectively, these tools remove the errors and inconsistencies from legacy data sets that can result in poor decision making that can not only prove costly in financial terms but can also be highly damaging to customer and partner relationships.

A new generation of DQM tools are now available

DQM tools have been around for many years and used to handle basic data management tasks such as data cleansing and data integration but often with the need for a high degree of manual intervention. However, over time a new generation of more sophisticated and automated tools has emerged that are much easier to use and with added functionality such as address and contact validation, data mapping and transformation as well as test data generation and augmented analytics.

With a range of solutions to choose from, including free open source software, the challenge for data managers is to identify which DQM solution is right for their organisation, which means having a good understanding how data flows across their network and taking into consideration the practicalities of where data is stored, how it is used and whether theirs is a complex scenario that calls for a hybrid tool set, before making the decision.

How To Choose the Right DQM Solution?

Choosing the right data quality management solution is essential for the future success of any organization and it is important to start by conducting an analysis of existing data sources as well as the current tools in use, and the typical problems that occur. This approach helps to identify where any gaps are and the possible fixes that need to be addressed.

However, not all DQM tools offer the same level of functionality and all have different strengths and weaknesses: some are designed to enhance specific applications such as SAP while others focus on key elements of the DQM process such as contact and address validation or data integration, which means that you need to decide what features are most important to your particular organization. Additional important factors to consider include the level of automation as well as data controls/security and licensing costs the solution has to offer.

A report published by Gartner in October 2021 provides some additional useful insight and recommendations to help with the selection process;

“Explore how metadata, AI and machine learning impacts augmented data quality capabilities of these solutions. Solutions that provide augmented capabilities will be better placed to address needs for self-service, automation, reducing costs linked to data quality programs, and scale and distribution. Focus on solutions built not just for identification but also for easier analysis and remediation of data quality issues. This will allow you to unify your business users, augment your data engineers, improve data literacy, reduce operational challenges and reduce your IT specialist costs... Business usage of data quality solutions determines the success of data quality programs in the long term.”

The IDS DQM Solution

At IDS our iData toolkit has been developed using these principles to offer businesses a modular DQM solution that can flex and work together to meet the full range of business needs based on four levels of data management, increasing in complexity in line with the requirements of the organization:

#1 Data Quality - including data cleansing and deduplication, data matching and merging data classification, data curation and enrichment, data standardization and data transformation.

#2 Data Insight - including data profiling, data quality monitoring, data validation, business rules and entity resolution.

#3 Data Innovation & Application - including establishing lineage, metadata and visualization of metadata, outlier and anomaly detection (potentially through the use of AI), pattern and trend discovery and data quality prediction.

#4 Data Quality Assurance - including test data management, data obfuscation, data synthesis, non-production environment support, AI automated testing; load and performance and test delivery management.

Delivered through our unique Kovenant™ methodology that uses automated techniques to ingest, cleanse and then transform every cell of your data, before migrating it and delivering application QA to 100% of the data migrated.

The combination of our iData toolset and Kovenant™ methodology enables the delivery any data in digital transformation project with unequivocal certainty, offering massive efficiencies of 50% or more in cost and time.

Contact us to learn more about how IDS can help you with all your data quality management requirements.