One important rule in using enterprise data for business purposes is straightforward, as the quality of business decision-making depends on the quality of the data used for it.
Knowing this in-depth is extremely helpful while you plan for your enterprise database administration practices.
To get tangible results from data, you should always measure its quality and act on its various measures to improve it.
This article will shed light on the quality issues related to a complicated enterprise data management scenario and provide some tips to excel in resolving these issues.
Data quality, attributes, and metrics
Data quality is a data state connected directly with its capability or incapability to solve essential business tasks.
Notably, data quality can either be good or bad based on what to do with it and to what extent your enterprise data corresponds to its essential attributes like consistency, accuracy, completeness, audibility, orderliness, timeliness, and uniqueness.
To understand what is there behind each of these attributes, we will explore these in detail.
- Consistency – No matter what you are looking for in the enterprise database, you will not find any data contradictions if it complies with consistency needs. For example, a payment system that consists of various entries related to the same information must have consistent data.
- Accuracy – This means information related to your data corresponds to reality. For example, a customer name in the database should be the same as how it is reflected in the CRM.
- Audibility – This means the data in the database is always accessible, and it is also possible to trace any changes made.
- Orderliness – This pertains to data as it should always be in the desired format and structure. This is usually applicable to date and time format etc.
- Uniqueness – This means a data record that contains specific details must appear only one time in the database. The number of duplicates is the measure of uniqueness.
- Timeliness – This means the data represents the reality within a specific time or based on the set industry standards.
Providers can offer you unwavering support in terms of data quality management as a part of the enterprise database management services.
Implications of low data quality
When it comes to enterprise database administration, the problems related to impaired data quality are grave.
We will give you some real-life examples of how low-quality data can have adverse impacts on business processes.
Reliability of information
Manufacturers transporting goods think they know the truck’s exact location transporting the goods from the production unit to the distribution sample. With this data, they can optimize the route and estimate the delivery time.
But, if the location set based on the data is wrong, it will end up in trouble, like the shipment arrives late and disrupt the entire distribution unit’s entire workflow.
You may take an example as you are trying to optimize a supply chain management process to assess the supplies and understand which of them are working in a trustworthy and disciplined manner and are not needed to track the delivery time.
However, unlike the delivery time schedules, the actual delivery time field is not mandatory in your database system. Therefore, the warehouse employees may not enter it. Further, on missing this info, you will fail to understand the performance of the suppliers.
At first glance, duplication is not very challenging; however, this can become a serious concern in many cases. Say, for example, if your customer appears multiple times in the CRM, it will take up additional storage space and lead to wrong assumptions.
Furthermore, along with duplicates existing, any analysis done on your data may disintegrate the purchasing history or customer care needs, etc.
Suppose a customer completes a given questionnaire and is given specific, timely information (like marital status or so). As time passes by, the validity of this information may change.
Without getting this updated, you may end up with poor customer segmentation, which may lose many opportunities for you.
Late entries and late updates
The late entry of data and late updates may also negatively affect the reporting and analysis of data, which may tamper with the entire business process.
For example, invoices sent to the wrong addresses may be a classic error related to this.
Data quality best practices
When thinking of data quality, at the first point, you need to ensure that every user of the database understands the problems related to data quality. Incorporating comprehensive data quality management into your business process may require multiple steps as:
- Designing a solid enterprise-wide strategy for data management.
- Creation of clear user roles and ensuring accountability.
- Setting up a comprehensive data quality management process.
- Maintaining an easy dashboard to monitor the status of data.
Automating data entry can also help to avoid any issues related to manual data entries. However, companies need to think of data entry automation in a foolproof way to reduce human errors.
When the system can do something automatically, it is always worth relying on it. Creating your master data is very important, but you should also not forget about the metadata.
Data quality management is a comprehensive process that consistently achieves and maintains high data quality data. The major stages involved in defining data quality thresholds and rules comprehensive data quality assessment are quality issue resolution, data monitoring, and the control needed.
To make it more comprehensive, you need to go beyond the theory and take control of each of these stages practically based on your specific use case to maintain better data quality.
Consequently, comprehensive data quality management is a significant consideration for enterprise database owners to make the best selection. As data quality management is an expert affair, it is also advisable that you also take the assistance of external consultants, who can assess your data quality needs and administer them well.