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As businesses adapt to this 21st-century digital environment, they are recognizing the importance of data management and keeping a high standard when it comes to historical and real-time information.
Maintaining a standard in data quality involves helping companies tackle the timeliness and completeness of their projects, affording better business decisions, and limiting hurdles in the workflow. Here is more of what the quality of data is doing for business users and customer access.
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Primary Dimensions of Data
Data quality refers to when data fits the purpose that it was intended for. It’s considered high quality when it accurately represents real-world constructs. There are six primary dimensions of data quality, setting the standard for what companies can do to maintain data consistency in a paramount fashion. These standards vary from one project to another, but generally remain the same:
- Comprehensiveness
- Consistency
- Accuracy
- Format
- Timeframe
- Integrity
Comprehensiveness looks into the essential feeds that need to be filled for a dataset to be considered complete. This could be anything from customer data to information about a product or service. Consistency ensures that iterations of any piece of information are the same across various reports and spreadsheets.
Moreover, consistency is necessary to ensure a singular value across all channels. Accuracy deals with making sure those values are correct. The format makes for that a standard data format is encapsulated across all platforms.
Timeframe refers to the effectiveness of data to make sure it’s relevant to end-users, while integrity refers to the compliance standards that quality data is upheld to.
How to Improve Data Quality
For any organization, improving data quality is about the right mix of qualified people, intelligent processes, and accurate technologies. When working on improving the quality of data, the main goal is to enhance the range of those aforementioned data quality dimensions.
Data presentation runs into difficulty depending on the datasets. For example, when dealing with the uniqueness of customer data, large companies struggle to avoid deduplication techniques. In the case of product master data, the uniqueness dimension is not a large issue to contend with, putting more focus on the completeness of data input.
The primary reason why these datasets lack completeness is that different product categories have varying regulations and requirements. In many use cases, the conformity of product data bears direct relations to locations from where that data is being input.
Working on master data for locations comes with the issue of a lack of consistency in the entry template format. That’s why standardizing inputs is essential to maintaining proper data models. It’s important to have a clear picture, avoiding quality issues across data domains.
Benefits of Better Data
Plain and simple: high-quality data facilitates better decision-making. Access to better data promotes better analytics, which leads to better team collaborations across the span of an organization.
This promotes more effective communication, leading to better internal data systems and a greater customer relationship management effort. By understanding the customer better, companies garner a competitive edge that helps them stand out across different industries.
There is a significant cost of poor data quality, as it slows down production across broad categories and leads to inefficiencies brought on by poor data entry. When you have a specific goal in mind for your company, consistent data is the only way to make sure that an existing record supports your business decision.
It’s important for organizations to assess objective and subjective data regularly, analyzing the results to spot any discrepancies. This allows for companies to take data quality measures sooner to maintain standards, and afford greater reliability in their analytics.
Overall, high-quality data provides peace of mind to do better by the business.
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