Enterprise Data Management: Benefits, Challenges, and Strategy
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Loading... - Veronique Ndzana
- 03 Oct 2022
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As information technology matures, it becomes increasingly clear that enterprise data needs to be managed as much as material flows, labor, and financial resources. The more significant the enterprise, the more distributed the enterprise infrastructure, and the more employees involved, the more acute the data management issue becomes.
You can increase the value of enterprise data with an optimized approach to its collection, naming, storage, and use. All of this together forms enterprise data management. This article explores how and why to implement enterprise data management.
Why Is Enterprise Data Management Critical?
If someone tells you that almost three-quarters of the data in the average company is not used, you might not believe them. But research shows this is true – up to 73% of companies waste most of their data because of poor or non-existent enterprise data management.
Data drives many company processes and decision-making throughout the product development lifecycle. That being said, you need to use quality data. Effective enterprise data management ensures clean and accurate data migration from a CDP platform to a product analytics service.
What is CDP?
A CDP (customer data platform) is a database that aggregates user information from different sources and can be integrated with other tools, such as a product analytics service.
As companies improve their enterprise data management practices, they are developing smarter workflows to transfer data to the product analytics tool and make it available to stakeholders.
These workflows include error correction, pre-planning data collection, and setting up approval processes to ensure that only correct data is imported into the analytics tool.
What Is Enterprise Data Management?
In the context of product analytics, enterprise data management includes the following elements:
Collection. Data will come from a variety of sources. Enterprise data management includes ensuring the purity and completeness of the data.
Correcting existing errors. When dealing with large amounts of data, errors are almost inevitable. Enterprise data management includes promptly correcting naming, organization, or collection errors.
Preventing potential errors. By analyzing existing data, you can identify recurring errors (such as unnecessary events and properties) and use this information to avoid them.
Taxonomy. Taxonomy is a guide with principles for naming events and properties in analytics. The product team should develop a taxonomy for data management and treat it as an evolving document worth revisiting and updating as enterprise data management needs and priorities change.
Storage. It’s important to store the collected data somewhere. Popular storage systems, such as data management platforms (DMPs), CDPs, data lakes, or data warehouses, allow data to be streamed to a product analytics tool for further analysis.
So, we have discussed the main functions of enterprise data management. Now let’s discuss its benefits. Read More...
