5 Most Common Data Quality Issues In Data Warehouse

When it comes to data quality issues in data warehouse solutions, people often talk about – garbage in, garbage out. Most decision makers already know that reliable results can only be achieved with good quality data. This is not only true for classic business intelligence data warehouses. Real-time data warehousing and artificial intelligence only work well when they are based on a reliable set of data, whether it’s predicting sales in retail, optimizing production processes in industry or identifying trends in law enforcement

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How can we get good quality data? Here are 5 most common data quality issues you can take into consideration to improve the reliability of your data warehouse. When implementing these actions, it is important that it is not a “one-of”’ project. Instead, data quality should be seen as an ongoing process. The term “closed cycle” or “data quality cycle” is often used in this context. Therefore, quality must be defined and regularly monitored to achieve sustainable results.

This includes, among others, the following activities:

1. Define Data Quality

What is data quality? As is often the case with data quotes: ask five experts and you will get five different answers. This makes it even more important for business professionals to develop a common understanding of the term before addressing data quality. One definition, admittedly rather general, in this context might be the following:

Data quality is the suitability of the data for a specific purpose.

Data quality can be assessed according to the following criteria:

Consistency – data should not be inconsistent or overlapping.

Completeness – data must be accurate.

Validity – data must come from reliable sources

Accuracy – data must be in the correct format and have the required number of decimal places.

Timeliness – data should be provided on time and as expected.

It is reasonable to prioritize the different criteria. Depending on the sector and the business process, individual data can be analyzed in more detail. For example, the accuracy of data is particularly important in the case of the profit and loss account during the audit. On the other hand, consistency or uniformity of data is important in the assertion based approach. However, these priorities may change over time. You should also regularly monitor comprehension and data quality requirements. This will allow you to assess whether your understanding of data quality is appropriate to current needs.

2. Continuous Measurement With Proven Software

Continuous and automatic measurement of data quality using appropriate software. Inconsistencies, redundancies and missing data are identified by the appropriate tools and indicated by automatic alarms when necessary. All monitoring results are presented in a transparent and clear manner. The evaluation and quantification of these results should be guided by business-oriented data quality principles and appropriate targets to be defined in advance.

However, audits are also useful because they establish the status quo on data quality by first starting from zero. An automated process can then be established.

3. Involve Consultants And Stakeholders

As in the previous point, do not rely solely on the “green light” of the data quality assessment software. Seek feedback from business users who work with the data on an ongoing basis. Reputed data warehouse consultants can also help with best practices and expertise. In addition, regularly invite different stakeholders to meetings. This intensive exchange of information prevents misunderstandings and makes changes in business processes transparent. Seamless interaction between business and technical contacts is therefore an important step towards improving data quality.

4. Use A Time First Right First Approach

Gaps in manual data entry, for example, in call centers, can also be a source of inaccurate data. It is therefore advisable to start taking appropriate action at the data entry stage, not just in the system. This includes validation and format checking, which can be done with an intelligence input mask. For example, the date of birth should not be a free text field. Similarly, address data should not be sent to the system without validation, but should be verified by post. Other measures include comparing the input data with references or searching for copies. In other orders, careful monitoring of data quality issues in the data warehouse at the time of data entry can save a lot of hassle during subsequent sorting.

5. Avoid Isolated Data

Another source of inconsistent and inaccurate results is historical data from different departments. These need to be analyzed and integrated into a single data warehousing platform. These will create a single version of the truth that provides a unified view of all the data in the company, and thus a single point of contact for all users.

In short, high-quality data is always the result of the interaction of technology, knowledge and personal interaction. Every company needs to find the right combination for its needs. It is also important to consider the cost-effectiveness of each measure. If you take these aspects into account, you are on the right track to build a reliable and high quality data warehouse.

Want to learn more about how to build a quality database for digital transformation? Then check out our page on Data Science and Big Data Implementation Services or visit our page on Data Warehouse Solutions.

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