What is data quality? Part 2

How do we ensure our data quality?

This is an ever-present point of discussion in many companies, regardless of department or duties. Data quality can be evaluated based on a number of parameters, which we started sharing in the last post:

• Accessible
• Relevant
• Comparable
• Valid
• Accuracy
• Completeness
• Reliablity
• Consistency
• Current

Based on these, we will recommend a selection of activities, both manual and system-driven, that can help you evaluate and improve your data quality.

Completeness

In the last blog post, we discussed what can be done to achieve data quality from the perspective of "correct data". That leads us into the next variable – how "complete" your data is. Here we mean whether you have access to your data and that you don't have holes in it. Is the data complete so that it supports the way you work? Maybe need the data is consolidated in and between systems to make it complete.Below we list tips on how you can achieve "complete" data.

Process control

1. Ensure that those working in the process understand the importance of accurate data and how inaccuracies affect operations further down the chain.
2. In the face of clear controllable routines in the process, perform random tests to ensure that it meets the data quality requested.
3. Set requirements for input from external parties, for example suppliers.

Required fields

Prevents data from being saved without mandatory information being included. Guides employees and contributes to process control. To make the data quantifiable, control can be strengthened by only allowing predefined values.

Master Data Check

Perform automatic checks of important data points, such as: 1) amount range 2) data format 3) list of allowed values, and more. Of course, checks can also be done manually, but are very time-consuming and introduce new sources of error and personal dependence. This takes up valuable time that could instead be spent on developing the business.

Consolidate and Standardize data

Important data is often obtained from several different sources. Compare and connect data from these to get the overall picture. By standardizing and washing data, you can get reports and dashboards with new information that is not available in the individual source systems.

Is your data complete? How can you improve the way you collect information?

Are you curious to learn how Business Cloud iPaaS can help your company with integrations and data quality? Contact me below or book a demo and I'll tell you more.

Staffan Hedbrandh, CEO