Introduction

When creating a new application, one crucial aspect that often goes overlooked is how to store data effectively. While defining fields and data structures is a common practice, the physical information capture process is often left unconsidered. This oversight can lead to significant challenges when attempting to retrieve and utilize the captured data.

In this article, we will explore the importance of designing for data integrity and highlight why data quality matters.

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The Challenge of Fast Data Capture

Data capture occurs at a remarkable pace, with servers quickly filling up, being modified, or copied without proper consideration for future usability. Additionally, the lack of consistent data entry practices and inconsistent naming conventions further exacerbate the problem, resulting in data that is nearly unusable.

The Complexities of Consolidating Data

As businesses strive to consolidate information from various systems, each with its own quirks and inconsistencies, the data management problem grows exponentially.

Users often face the daunting task of sorting through vast amounts of data to create accurate master records for client or product information, generate reports for spend analysis, or ensure regulatory compliance.

However, when confronted with the data, confusion sets in as nothing seems to make sense. Consequently, data management is often regarded as a technical challenge due to the intricate process required to resolve the mess created over years of neglect.

Establishing Proper Data Capture Standards

To overcome these data management challenges, it is essential to prioritize proper data capture standards from the outset.

As part of your data governance function, it is crucial to identify critical data elements that support key business processes. This identification process should ensure that data is captured with appropriate levels of quality. Investing in real-time data cleansing and matching capabilities can significantly contribute to maintaining data integrity.

Data Standards: A Necessity from the Start

While data standards may not be at the forefront of your mind during system design, neglecting them can lead to future regret. Establishing data standards from the beginning is a proactive approach that saves time, effort, and frustration in the long run. By setting up data standards early on, you create a solid foundation for effective data management and ensure that your data remains accurate, consistent, and reliable.

Privacy Concerns

Since this post was written back in 2012, regulations such as GDPR and PoPIA have placed increasing scrutiny on unnecessary access, particularly to sensitive, personal data. Design considerations for data privacy, including privacy-by-design and least-privilege access should also, by definition, be considered during the design phase of any new system.

Conclusion

Data management should not be an afterthought but rather a fundamental consideration from the inception of any application or system.

By emphasizing data quality and data privacy and implementing robust data capture standards, businesses can unlock the true value of their enterprise information asset.

Don’t wait until the data becomes an insurmountable challenge; think about data management from the beginning and set yourself up for success.

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