Pretty much every company with digital products has a trove of data that can be leveraged to make their products better. The Amazon’s and Google’s of the world have teams of data scientists and algorithms to make sense of any product’s data analytics. For example, they know how to manipulate on-screen product placements relative to your eyeballs and button sizes in relation to their context, but you’re just looking to do right by your team, your manager, or most importantly of all, your users.

Maybe you don’t have a million-dollar budget to flood your team with analysts, but you understand the value of the data you’re swimming in, and you want to learn how to tread that water and use your data to do something awesome. The first step to finding actionable insights for your product is to make sense of your data analytics.

Here are five tips and lessons learned from our own experience to help you get set up and succeed in data analytics.

Decide on Tech That Provides Clean Data

When you’re working with digital product, it’s easy to end up with silos of user data. Over time or even by product launch you might have Google Analytics, Firebase, Adobe, Shopify, Apple, and maybe even a payment processor constantly sending various streams of data your way, each with a unique perspective and containing information on only a fragment of your users’ experience. This makes for a frustrating environment in which to tell a story about your users.

In the digital age, it’s easy to be persuaded to buy new, expensive software that promises end-to-end analytics but inevitably falls short. Or you might slowly add software components or data tracking systems to answer shorter term questions, leading to a mass of increasingly convoluted data systems down the road.

There is no one solution here. Every project and product is different. However, it’s helpful to have a serious discussion with internal stakeholders about the metrics that matter most and which will matter most for the foreseeable future and build out data capturing infrastructure around those KPIs.

You Can’t Track It All. You Just Can’t.

Well, you could, but you couldn’t process all those data points anyway. To start, the best approach for data analytics is simpler, cleaner data. You’ll need to prioritize your key performance indicators and quantitative goals. I mean really prioritize them. Try this activity: List out every KPI that matters to your team. Put each one on a sticky note. Get your stakeholders together and put those sticky notes on a wall in a list, placing the most important KPI at the top and the least important at the bottom. You may not like it, but some of those on the bottom may need to be forgotten about, and their placement down there might indicate that you’re likely to forget about them over time anyway.

Any data service you use can generate reports all day long, but that doesn’t mean you have the ability to react to or even process endless reports. Get serious about what matters to you, your company, your team, and your users, and focus on a few things to track. Imagine the five data points you might feel it’s important to see on a dashboard displayed in your office every single day.

Generate Data Analytics Reports You’ll Use

The trouble with data systems is that they tempt you with endless possibilities when you start following breadcrumbs of data. Much like Wikipedia rabbit holes, a quick glance in Google Analytics to pull out some key metric might lead you down a dozen other paths as you hunt down interesting but nevertheless insignificant insights.

Time to return to your prioritized list of KPIs. Generate reports around those only, and chase supporting data points only when they’re relevant to those specific KPIs. Better yet, create a dashboard and display it in your office so that everyone can see various KPIs in real time, which can prompt much more productive and organic real-time discussions, questions, or concerns around how your data is working.

Use Data Visualizations

All this won’t help you achieve your goals if you can’t tell a story with your data. Storytelling with data is the act of creating presentations or reports that effectively establish an “aha” moment and use data points to communicate actionable insights or lessons learned. One of the best ways of conveying themes or takeaways around data is through data visualizations using a tool like Tableau or even well-designed charts built around .csv data with a cost-effective tool like Piktochart.

Humans are visual creatures, and most are not inclined to respond emotionally to numbers represented in a spreadsheet. Instead, a well-designed presentation that personalizes the story your data is telling and offers compelling, highly visual charts and graphs with key slides that emphasis specific quantitative values is much more likely to help you sway your audience. Whether you’re convincing a manager that you need more budget for a project or communicating a much-needed pivot to a team, data visualizations can help use objective data to make your point resonate.

Internal Teams Don’t Always See the Whole Picture

Anyone working within and dedicated to a company or product eventually inherits a wide set of biases when looking at their own data analytics. This is pretty much an unavoidable aspect of human psychology, and it’s why companies with big data sets derive so much value from seeking the input of outside partners. Whether working with an agency like Punchkick or even giving IBM Watson access to your data sets, a fresh perspective can allow you to zoom out, helping you see the forest for the trees and guiding you in the right direction.

It can be a challenge to use various specific sets of data and create a holistic 360-degree vantage point of your product or service, but several agencies including ours specialize in just that.

Don’t forget, though, that you can never focus solely on numbers—qualitative aspects like user experience and sentiment will also always play a critical role.

Learn more about Punchkick’s data analytics and other business intelligence offerings.