Data life cycle for building product analytics

Many organizations collect data, but not all effectively manage it throughout its lifecycle. According to McKinsey report, organizations are still managing data “with unplanned initiatives and one-off actions, rather than through long-term strategic adjustments that are required for sustainable success in an evolving business environment.” And unplanned data lifecycle management can cause problems for your product analytics.

Well-organized data is vital for locating actionable insights from product analytics. With data lifecycle management, you can ensure that your data remains clean and usable. Data-informed teams improve your bottom line now and well into the long run .

What Is Data Lifecycle Management and Why Does It Matter?

Data lifecycle management (DLM) is that the method of effectively monitoring and organizing information. It involves a comprehensive approach to managing an organization’s data, recognizing what’s relevant, and proactively updating your processes.

Effective data lifecycle management has benefits that reach all areas of the business:

Efficient access to data: an excessive amount of knowledge may end in an excessive amount of noise and not enough signal. With effective DLM, teams create a thought that data they have to trace and intake.

Cost savings: Effective data lifecycle management allows organizations to reduce the expenses associated with fixing mistakes in data storage and collection.

5 Data Lifecycle Management Steps in Product Analytics

The process of data lifecycle management are often categorized into five overall steps, which, when done well, provide clean data everyone can use to surface valuable insights.

1. Plan and Instrument Your Data

The first and most significant step of product analytics DLM is selecting what data to send to your product analytics platform. Being clear and consistent about which events and properties are important and what those events and properties are called will set the stage for a healthy data lifecycle.

Choosing the right data to trace involves creating a tracking plan and a knowledge taxonomy. Your tracking plan is documentation about which events and properties you plan to trace, the business objectives behind those events and properties, and where all of this data will end up. Your data taxonomy is your methodology behind how you name the events and properties you track.

Together, you’re tracking plan and data taxonomy ensure most are aligned around what data is being collected, why, where to hunt out it, and therefore the thanks to ask it. One important note at this stage is that you simply must resist the urge to trace everything immediately. Start your tracking plan and taxonomy with what you recognize is significant, and move from there.

Defining what data to trace are often tons to believe, and you likely won’t figure it all out directly. Throughout this initiative, it’s important to remain in mind the four truths of data management for product analytics:

Once your plan is in place, you’ll start instrumenting your data lifecycle. Instrumentation is that the initial process of fixing your product analytics platform, including setting your event and user properties and connecting it to where you store your data.

2. Store the data

With your plan for collecting data in place, you’d wish to send and store all that data during a secure place. You’ll store data during a kind of tools—databases, data warehouses, data lakes, data management platforms (DMPs), customer data platforms (CDPs)—each with its own specialty.

For the requirements of getting your data lifecycle off rock bottom, all you’d like could also be a DMP or CDP. Within the past, there was a transparent distinction between these two tools, with DMP generally handling second- and third-party data and CDPs generally handling first-party data. Nowadays, the two tools have (for the foremost part) merged into one common purpose: to collect, store, and organize a selection of customer data.

3. Send the data to Your Product Analytics Platform

Now your data is ready for your product analytics platform, which makes all this data you’ve been collecting and organizing usable for people across your organization.

In Amplitude, when you’re ready to start analyzing your data, you’d wish to make a project, which connects to your data source, be it Segment, Particle or another source via API or SDK. We highly recommend creating a test project and a production project. Here could also be a screenshot of those to projects side by side:

Test project and production project

Your test project is where you test your instrumentation and confirm the info you’re using is accurate, presented how you’d love it to be, and usable. Your production project is where you’ll actually do the analysis. Keeping the testing process outside of the assembly project allows you to edit data and inspect things out without messing up the clean production environment that good analysis requires.

4. Act on Data Insights

Next, it’s time to put your data to use. This stage is that the particular act of study, where you create graphs and analyses of the data, draw insights, then act on those insights.

The more widespread the facility to act on insights is, the more democratized your data are getting to be. All the work you’ve wrapped to this point within the DLM process will ensure your data is clean, relevant, and usable for everyone that needs access there to.

With access to this data, product managers can create a data-informed product strategy, marketing can tailor their campaign targeting supported feature usage, and much more. None of this is often often possible without the good, clean data lifecycle you’ve established with the three previous steps.

5. Practice Consistent Maintenance

There is no final step to the merchandise analytics data management lifecycle. It’ll always be an ongoing endeavor, requiring consistent, intentional maintenance.

As data becomes outdated, you’d wish to update the events and properties you track, confirm you’re storing and organizing it efficiently, and confirm the excellence between your testing and production environments is maintained.

Instead of deleting outdated data, we recommend that you simply simply stop tracking the event and properties that have run their course. This might stop the flow of outdated data at its source instead of having to trace it through the lifecycle.

Overall, though, consistent maintenance of your product analytics DLM could also be a a neighborhood of a healthy data governance framework, which includes three pillars: education, instrumentation, and maintenance.

Good, Clean Data is significant to Actionable Insights

The purpose of DLM in product analytics is to provide the only, cleanest data possible. With this good, clean data, product analytics insights will come easily to anyone using your product analytics platform. The data are getting to be easy to understand, communicate with, and use.

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