Companies operate their businesses based on the data that is available to decision makers. If the data quality is low, or simply inconsistent, the result is poor decisions that don’t correspond to the actual business environment, and ultimately, a loss of profit impacting the bottom line.
Data quality is especially important in our world of application development. You need to know who is using your applications, how, and why.
If the data has many mistakes in key records, you won’t have an accurate picture of your users and your development could easily go off track. While data that is almost completely free of mistakes is hard to verify and very expensive to maintain, you should set benchmarks within your organization, and make it a priority to ensure that you only use data of sufficient quality for any development work.
So, how does one navigate this process of setting benchmarks, and maintaining them?
The first step is to establish a data profile specifying the data quality that you need. You have to decide what data is required, what is accessible, which data sources you will use and what level of accuracy you need.
Once you have established your data profile, you have to decide how to make sure you have the data validity and consistency required by the work. After completing this process, you can proceed with software and product development.
Data Availability and Accuracy
The accuracy of available data is a key characteristic of data quality. While exact data, such as location, may be available for some users, for others it may be less accurate, or missing completely. Your data profile has to specify whether to include the less accurate data as your standard or to discard users for which the data with a higher accuracy is not available. For a location-based application, for example, your decision would depend on whether exact user location information is critical for your work.
Once you have decided on the accuracy required by your data profile, you have to examine data validity. When the data in a particular field is critical, you can structure the database to help ensure that the data format is valid. For example, the database can check that a date is valid or even present lists of possible answers for some types of data. By identifying critical fields and structuring your database accordingly, you can help obtain the needed data validity.
The consistency of your data impacts data quality, and has two critical components. Data stored in several locations must be identical, and data of a particular type must be consistent across all records. For example, if you have customer addresses stored in two databases, the address of a customer must be the same in both locations. In addition, all addresses must consistently apply formats such as abbreviations. It may seem obvious, but inconsistencies can easily lead to false assumptions.
You can help ensure data consistency by preparing data entry rules and by storing data in only one database. If the data is needed elsewhere, it can be linked via a unique field such as a customer ID number or email addresses.
Working With Quality Data
When you work with custom data acquisition and reporting platform, quality data means that the platform delivers the information you require to do your work effectively. Your data profile details the requirements for accuracy, validity and consistency needed to make the correct decisions regarding your application. You need a platform that offers the features and tools to let you amass that data, and analyze it effevtively.
With a properly implemented data profile, and the right reporting tools, you can concentrate on development, and more confidently make resource, and other critical decisions that impact your bottom line.