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?
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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.
The benefits of Enterprise Data Management
Improperly managed data is useless, no matter how much or where it’s collected. Let’s explain how effective enterprise data management changes that and makes helpful data.
It may happen that several teams in a company need access to the same metrics. For example, customer service departments and product developers may need user journey data to determine where they spend the most time or encounter the most difficulty. Effective enterprise data management will ensure a single version of this data for everyone.
Standardized data helps avoid the common problem in the marketplace of teams collecting vast amounts of data that does not drive business growth.
More than half of companies say poor data quality is a severe issue affecting the entire enterprise. For example, poorly collected or maintained user behavioral data makes it difficult to develop a retention strategy.
Lack of behavioral data results in product teams not knowing exactly where users are encountering problems and not being able to improve their experience. Poor quality data can also exacerbate the situation, leading to incorrect conclusions and decisions, and negatively impacting the product and its metrics.
One of the most significant risks of implementing a data-driven culture is the creation of siloed data silos.
Data silos occur when vital information is known or available only to a small number of people in the company and not to all employees who may need it. Data governance avoids silos by giving teams access to the full range of data they need to do their jobs. In other words, it leads to the democratization of data.
Better Understanding of Users
The final benefit of enterprise data management is that it helps product teams better understand how users interact with the product.
Users generate data every time they use the product. A wide variety of people in the company (from product managers and marketers to designers and programmers) will need access to this data to propose hypotheses to improve the product and measure its effect.
Enterprise Data Management Best Practices
The best enterprise data management practice is about making the right data available to the right people in the right place. Let’s take a closer look at what that means.
Having the right data means creating a convenient, accurate, comprehensive data library. Data is handy if existing employees can use it to answer questions, and new team members can quickly figure it out and start using analytics. Data is accurate if it adequately reflects accounting systems.
Providing data to the right people is critical to getting the most out of analytics. The enterprise must find a middle ground between democratization and data security. Although data security is as important as network security, a balance with democratization must be found. Start by building a data management team responsible for data usability, accessibility, and integrity. A robust data taxonomy is vital to ensuring the availability and use of information in the database as it expands, so team members should have a consistent naming convention in analytics.
Finally, putting data in the right places requires easy synchronization between systems for data analysis and data recording.
Then it’s worth investing in tools that will communicate the data to the rest of the enterprise and teach employees how to use them. Tracking plans are great for this. A tracking plan is a blueprint agreed upon by all product development stakeholders, showing which data to track to most effectively improve processes at work. Subsequently, those involved in the process store the insights from the analytics in a centralized document. The tracking plan prevents the generation of siloed data and is convenient for communicating insights from well-managed data.
How to Strengthen the Existing EDM Strategy?
Here are five ways that will help you strengthen your EDM strategy:
- You should identify business objectives. If business objectives don’t match, data can’t serve its purpose. Accordingly, as an initial stage in creating an enterprise data management strategy, you should analyze business requirements and challenges.
- You should understand the potential of existing data. It’s critical to explore the current data within the enterprise and recognize chances to up-sell or cross-sell products and enhance general efficiency.
- You should create robust data processes. Your enterprise data management must collect, store, and distribute the data.
- You should adopt a data governance policy. It assists in avoiding mistakes, blocks probable misuse of susceptible data, and aligns the enterprise with data-related legislation.
- You should manage metadata. Enterprises repeatedly miss out on checking fundamental facts like who possesses specific data, how crucial that data is, and how suitable it is to the enterprise. Metadata helps to maintain the above data.
Following these 5 points, you will strengthen your enterprise data management strategy.
Conclusion: Success in Numbers
By 2025, the global data sphere will be five times larger than it was in 2018. In modern enterprises, many information systems are designed for different purposes and solve problems using computers, networks, databases, etc. But one of the most important undoubtedly is enterprise data management system.
As the market for digital products expands, customers are demanding a more pronounced return on investment in the products they invest in. To achieve this, you need to harness the full potential of data while maintaining high-speed performance. This can only be done with a sustainable, reliably designed approach to enterprise data management. Early investing in an enterprise data management architecture will help enterprises take full advantage in the coming years.