Any organization that wants to make the most of its data needs to understand its data landscape clearly. Data mapping is essential to achieving this understanding, but it can be a complex task. There are several pitfalls that organizations need to avoid. Here are the six most common mistakes to avoid when data mapping your enterprise.
1. Defining Data Governance Too Narrowly
The ability to rapidly draw information from ever-evolving data sources is vital for an enterprise that wants to stay competitive. However, organizations can struggle to harness new data sources if data governance is defined too narrowly. To ensure the best results, it’s crucial to create a data governance framework that is flexible enough to accommodate changes in the data landscape. Pairing tools like Microsoft Purview with the Profisee MDM is a great way to keep up with the ever-changing landscape.
The businesses that thrive are those that look beyond traditional checks and balances to implement a framework that is agile and responsive to the changing environment. By doing so, they are better equipped to exploit emerging technologies, establish data governance best practices and create an enterprise-wide culture of data trust.
2. Failing To Prioritize Data Quality
No matter how detailed your data mapping strategy is, it won’t be of much use if the quality of the data is poor. It’s important to prioritize data quality and cleanliness throughout the data mapping process. High-quality information is free of duplication and errors and is consistent across systems. It is also obtained from reliable sources.
Using a data profiling tool can help identify issues with the quality of your data so that you can address them early on in the process. It’s also important to implement procedures for cleaning and validating that data regularly to ensure that it remains accurate over time. Other steps that provide data quality include data profiling and validation, which can help you secure the accuracy and completeness of your data.
3. Not Involving Stakeholders
Data mapping involves multiple stakeholders, including IT, business analysts, executives, and other decision-makers with an interest in the data landscape. Failing to involve key stakeholders in the process can lead to poorly defined goals and a lack of understanding of the objectives.
It’s essential to ensure that stakeholders have the opportunity to provide input and feedback throughout the process. This will help ensure that all parties are aligned on the mapping requirements and provide valuable insights into potential challenges that need to be addressed.
4. Overlooking Data Privacy
Data privacy is crucial when mapping your enterprise data landscape. Any organization that collects, stores, and processes personal information must comply with applicable privacy laws. When mapping the data landscape, it’s essential to consider how these laws apply.
Organizations should also ensure that they have appropriate measures to protect their customers’ data from unauthorized access or misuse. Data encryption, secure data storage, and access control are just some actions organizations should take to ensure their customers’ data is safe.
5. Not Taking Advantage Of Automation
Data mapping can be an intensive process that requires many steps. To save time and resources, taking advantage of automation wherever possible is essential. Automation can streamline the process and reduce the risk of errors.
Tools like Microsoft Purview can help automate data mapping and profiling, eliminating the need for manual data entry. Automation can also track changes in the data landscape over time, ensuring that organizations have up-to-date information consistently.
6. Ignoring Data Security
In addition to protecting personal data, enterprises must ensure that their corporate data is safe and secure. Data security should be a priority in the data mapping process, as it can help protect sensitive information from unauthorized access or misuse.
Organizations should use appropriate measures to protect their corporate data, such as encryption, authentication, and access control. It’s also important to monitor data usage and ensure that only authorized personnel can access it. By taking these steps, organizations can help ensure their corporate data is secure and protected.
Data mapping is critical for enterprises that want to make the most of their data. By following best practices and avoiding common mistakes, organizations can ensure that their data mapping process is efficient and effective. An effective data mapping strategy will help organizations gain valuable insights into their enterprise data landscape and unlock new opportunities.