If someone told you that up to three-quarters of your company’s data was going unused, you might not believe them. But, as the data shows, it’s true. A considerable number of companies let the majority of their data points go to waste through poor or nonexistent data management.
Data is the driver of your company’s operations and decision-making and is essential throughout the product development lifecycle. Data management—the capable collection, storage, and organization of data—is fundamental for getting the maximum amount of value possible out of your customer data. Effective data management is what ensures that when data is ported from a customer data platform (CDP) into a product analytics tool, that data is clean and accurate. Data management also includes fixing mistakes in the data and designing a taxonomy to keep naming conventions consistent.
As companies advance in their data management practices, they develop more mature workflows to get data into a product analytics tool and make it accessible for multiple stakeholders. These workflows include fixing mistakes, planning data collection in advance, and setting up approval processes to make sure only the correct data is imported into an analytics tool.
What is Data Management?
Data management involves the collection, storage and organization of data. Effective data management makes sure that a wide variety of stakeholders at your company—not just the team writing SQL queries—can access, understand, and analyze data. In the context of product analytics, data management includes:
- Collection: Data will be coming in from various data sources. Data management involves making sure the data is clean, complete, and not subject to any compromise.
- Fixing Existing Mistakes: When dealing with large volumes of data, mistakes are nearly inevitable. Data management involves retroactively fixing mistakes in how data was named, organized, or collected.
- Proactively Prevent Future Problems: By analyzing the mistakes you’re seeing in your existing data, you can identify mistakes that are recurring (e.g. unneeded events and properties) and use that new information to refine your structure, reducing the likelihood of future problems in your data.
- Taxonomy: A taxonomy is a guide for consistent naming conventions across events and properties in your data. Your taxonomy is there to answer questions that improve your product’s functionality and profitability. Your team should develop a taxonomy for data management and see this as a living, breathing document—something your team should return to and update as data management needs and priorities change. Some teams use spreadsheets to keep track of their taxonomy, but there are also data governance systems that help streamline this work.
- Storage: Once you’ve collected your data, you must store it. Popular storage systems— likea data management platform (DMP), CDP, data lake, or data warehouse—allow you to stream in your data to a product analytics tool for further analysis.
Why is Data Management Important?
Improperly managed data is useless data, however much of it you have, and wherever it’s collected from. Effective data management can release the value of data for businesses in multiple ways.
Data Management Helps You Standardize Your Data
It may be the case that multiple teams in your company need access to the same metrics. For example, your customer success and product development teams may both require user journey data to determine where users are spending the most time or encountering the most friction. Data managed effectively will ensure that there is a single version of that data for everyone—a single source of truth.
Having standardized data helps avoid a common problem we see in the market, where teams collect huge amounts of data which doesn’t then lead to better business outcomes. By standardizing your data, you can still take in large amounts, while taming its complexity.
Data Management Helps to Avoid the Collection and Use of Erroneous Data
Over half of companies say insufficient data quality presents critical complications for them, and it can have impacts throughout your organization. Poorly collected or maintained customer data will make your retention strategies more difficult. Insufficient usage data will leave your product development team clueless about where your users are getting stuck in the product journey and unable to improve customer experience.
Data Management Helps to Create Data Democracy
One of the biggest risks of running a data-driven culture is the development of data silos. Data silos occur when important information is only known or accessible to a handful of people in your company, instead of to everyone in your company who might need it. A good data management solution allows you to avoid siloing by giving teams access to the full variety of data they need to do their job—in other words, it helps you create data democracy. In a data democracy, all teams across the company are empowered to explore the data that’s pertinent to them.
Data Management Helps Bridge the Gap Between You and Your Customers
The final benefit of data management is that it helps close the gap between your customers’ behavior and your team. Your customers will be generating data every moment they spend using your product. Various people within your organization (from product and marketing to design and engineering) will need access to that data in order to determine improvements to your product, and to measure how those improvements perform.
What Are Data Management Best Practices?
Amplitude’s philosophy of data management involves making sure the right data is available in the right places to the right people. Let’s break down the best practices of data management along those lines.
Having the right data is about building a data library that’s usable, accurate and comprehensive. Data is usable if existing team members are able to answer questions with it, and new team members are able to get to grips with it quickly. Data is accurate when it adequately reflects your systems of record. Finally, data is comprehensive when your customers have all the data they require to answer your questions.
Having your data in the right places requires easy sync-up between the systems you use for data insight and the systems you use for data record.
Finally, having your data made available to the right people is fundamental for getting the most out of your data. As we noted above, your company should find the sweet spot between having data democracy, while ensuring that your data remains secure. Begin by building a data governance team, which will be responsible for your data’s usability, accessibility, and integrity. Having a strong data taxonomy is vital for keeping the information in your database reachable and usable as that database expands, so align your team members on a clear data dictionary.
Then, invest in tools that will carry your data to the rest of your company, and teach them how to use it. Tracking plans are great for this. A tracking plan is a scheme agreed upon by all your product development stakeholders about what data they need to track to make the most effective process improvements. Your stakeholder will then store the insights they gain from your data platforms in a centralized document. A tracking plan comes in handy for communicating the insights surfaced through well-managed data, preventing the formation of data silos.
Data Management: Success by Numbers
As the market for digital products expands, customers are demanding a more pronounced ROI from the products they invest in—more success, and fast. To do this, you need to harness the full potential of your data while working fast. It can be done only with a robust, reliably strategized approach to data management.
And the benefits of good data don’t stop there. By 2025, the global data sphere will be five times bigger than it was in 2018. Investing early in your data management architecture will help your company take full advantage in the years to come.