How A Data Strategy Framework Simplifies Data Collection

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More than 50% of the data collected by companies goes unused. That means companies are letting a lot of valuable data go to waste. If you can reduce your company’s wasted data by even a few percentage points, you can unlock a huge amount of value and have a measurable impact on your bottom line.

Reducing wasted data can be difficult though, because most companies aren’t doing this deliberately. Companies are collecting so much data that some of it is falling through the cracks. That’s where a data management strategy fits in.

Your data strategy fills in the cracks by keeping track of all the data you’re collecting. It also explains why you’re collecting data, what you’re doing with it, and who is in charge of it. Those four pieces will help you get the most out of your data.

The problem is, data strategies are complex. There are a lot of moving parts to them, so creating one can be difficult. That’s why we’ve put together a framework to help you identify exactly what needs to go into your data strategy.

5 components of a successful data strategy framework

Our framework has five components. If you carefully think through and organize each component, you’ll be well on your way to creating a data strategy that organizes your data and helps you get the most out of it.

Component 1: Own

Before you do any work on your data strategy, you need to answer one question: who owns your data strategy?

The owner of your data strategy is your data chief. In large companies, this could be a chief data officer or a chief information officer, whose sole function is to oversee the company’s data.

But for some companies, the data chief can perform other functions too. Many smaller companies appoint a data chief on a part-time basis, usually in addition to the person’s full-time job. It can be someone on the product team, data analytics team, or marketing team, or just someone who deals with data sets often.

The data chief’s main responsibilities are implementing a data strategy framework, keeping track of your company’s data goals, helping the company leverage data to innovate, setting KPIs, and ensuring that the company is compliant with data laws. Once you’ve determined who your data chief is, you need to appoint someone to own each of the tools you’re using in your data stack.

The person you appoint to each tool should be an expert on the tool or should at least be willing to become an expert. That way, if someone in your company has a question about a specific tool, they know exactly who to turn to.

Take Google Analytics as an example. The owner of this tool should be someone who works with the tool daily. Chances are, that will be someone on your marketing or analytics team.

Component 2: Explain

Now you need to explain to various stakeholders the use cases and value of data you're collecting. This component is designed to help you define your data goals, align different teams on your data strategy, and show how data analysis augments your business strategy and decision-making. Think of this in two ways:

Why are you collecting data as an organization? Why are you using each data collection tool?

When you set out to answer those two questions, position them in a way that ties into your overall business needs and goals. That will create alignment with your team because everyone will understand the data’s purpose. It will also help your company’s upper management understand the importance of data in improving business processes.

Say, for example, one of your company’s main goals is to increase annual recurring revenue (ARR) by 15%. You should explain how your data strategy will help with that.

For your overall data goal, you might say, “We will use our data assets to improve the customer experience, which will increase ARR because users will get more value out of our products.”

For a specific tool, like an A/B testing tool, you might say, “VWO will help us optimize our onboarding experience, which will help users understand the value of our products more quickly.”

Notice how those two statements tie together? Each one talks about value, which can help reduce churn and increase ARR.

Component 3: Plan

The data you collect must be standardized across your products to ensure data quality and consistency. This helps when you need to consolidate and use your data.

The best way to standardize your data is with a tracking plan, which is a document that serves as a roadmap, a project management tool, and a reference document. Your tracking plan will have three parts:

  • The events you plan to track

  • Where in your codebase they’ll be tracked

  • The business justification for tracking them




The three parts of the tracking plan will create standardized naming conventions. That prevents you from having two data collection events that track different things but have the same name. This is why proactively monitoring data integrity is critical; you might accidentally end up using the wrong data when it comes time to analyze it and then make the wrong decision.

Component 4: Categorize

This part of your data governance strategy shows how you acquire, consolidate, and store your data across different data sources. To do this, you’ll make a list of all the tools you use.

First up, the tools your company uses to acquire data. This list will be bigger than you think. Chances are, the majority of the data tools your company uses will fall into this category. Make sure you’re working with all departments in your company that collect data to compile this list. That could include tools the finance, operations, and product teams use.

Second, identify the tools that your company uses to consolidate data. These are the tools that help break down silos in your data architecture and make your data useful. Your company will use only one or two of these tools.

The third category is data storage. You should have only one tool that falls into this category — your data warehouse.

Putting all of that into practice, let’s say your company’s data stack is Google AnalyticsRedshiftIntercomMixpanel, and Segment. If you were to categorize all of those tools, it would look like this:

  • Google Analytics: Acquire

  • Intercom: Acquire

  • Mixpanel: Acquire

  • Segment: Consolidate

  • Redshift: Store

Remember to update this list consistently. Your company is going to add and remove tools from your stack, so it’s important to keep it up to date.

Component 5: Analyze

At this point, you’re almost ready to analyze your data. You’ve done all the legwork to acquire, organize, and store it. All you have to do now is define the tools you’ll use to analyze your data and choose your metrics. You’ll implement business-intelligence tools to handle this part of your data strategy.

It’s important to choose these tools now so that you have time to familiarize yourself with them. That way, when it comes time to solve problems with your data, you’ll know how to do it. Understanding how your business intelligence tools work will also help you figure out what questions you can answer in the first place.

Sometimes the problem with solving problems and answering questions with data is that you don’t even know what’s possible. Taking the time to figure out what’s possible before you need to will help you get more out of your data.

It can also help you prioritize what to track, rather than tracking everything.

Putting together an actionable data strategy framework

Putting all of this into practice, you can build the bulk of your data strategy in a spreadsheet. We recommend organizing your strategy by different tool categories and then listing the relevant components with each tool.

This is what it might look like:




Notice that we’ve put the goals from Component 2 at the top and then within the “why” column for each tool. That helps everyone stay aligned on the overall goal and the reason for using each tool.

The data chief is listed below the overall data goal. That lets people know who owns this and who will answer broad questions about the strategy. The owner of each tool is listed at the far right. That way, if someone has a question about a specific tool, they’ll know exactly who to go to.

The only thing missing from this example is a data-tracking plan. We recommend keeping that in a separate tab on your spreadsheet. You can also keep it in a completely separate document. If you try to fit it onto the spreadsheet with the rest of your data strategy, it will become too cluttered and confusing. The key to your data strategy is to keep it organized and simple so that people use it.

Organization is the key

When you’re putting together your data strategy, you need to make sure you’re keeping it organized. That’s why we recommend following our five-step framework. Once you’ve gone through this whole process, it will be simple to collect and use your data because you’ll be prepared to handle it.

There’s nothing worse than collecting data without a strategy that explains what you’re going to do with it.



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