The Future of Product Analytics is No-Code/Low-Code

When it comes to analyzing data, many of us struggle. Working with data is messy in more ways than one can count. And one of the reasons that it’s considered messy is that traditionally most data analytics tools require coding skills. But what if you could get the same insightful data without the need to know how to code?

The next generation of the workforce is more technically literate than ever before. Born in an era of smartphones and tablets, they can understand how apps, networks, and web technologies work. They are? more commonly referred to as “Citizen Developers''. Citizen Developers with little or no coding training can create new software capabilities which can help speed-up the overall digital transformation initiatives.?

The No-code/low-code way of Analytics

Organizations have defined frameworks and processes for their data analytics strategy. It usually boils down to the following four phases:

• Discovery - The discovery phase is all about defining problem statements, developing hypotheses, collecting and exploring data to derive insights. While defining problem statements and forming hypotheses have their own complexities, the data collection and exploration are the most challenging in the discovery phase. For Product Managers, instrumenting events when they build new features to measure adoption and engagement is a tedious task. They need to rely on engineers to instrument these events for them and it needs to go through testing, verification and release cycle. This is a very time-consuming process. What if there is a way by which Product Managers? themselves can capture events, analyze the data and take actions on their own? The No-code/Low-code tools would play a key role in this area.?
• Insights - The Insight phase is all about analyzing data, validating hypotheses and making decisions based on data. The most important aspect of any analytics tool is ease of use. If everyone in your organization relies on one or two people in your data team to help them make sense of data, it would be challenging to become data driven. Product Managers are the ones spending the most amount of time working with data. In a typical scenario, PMs would spend a lot of time working with data engineers to build Insights and Dashboards. Leadership, Sales, Marketing and Customer facing teams rely on PMs to provide them with the data points that they need for various purposes. This is where the No-code/Low-code analytics tools help in automating some of these tasks, making various stakeholders self-sufficient and lowering the burden from the PMs and Data Engineers for trivial tasks.?
• Actions - The action phase is all about applying learnings gathered through Insights phase. The action phase requires linking Insights to actionable recommendation and coming up with an executable plan. The actions could be for different functions in the organization. For Product, it could be changing the user experience for certain features. For marketing, it could be changing the value proposition, target audience, etc. When it comes to Product, PMs typically take these learnings as feature enhancements and new feature requests. Sometimes even though a feature enhancement is as simple as adding a smart tip that could help users navigate faster or showing a pop-up with more information, it takes an entire release cycle. What if the tool can provide you a way to make such minor enhancements so that you can rapidly build, test and validate? The modern no-code/low-code analytics platform is an integrated platform that? allows you to capture, analyze, and build.???
• Outcomes - Once you take actions, it's again time to analyze outcomes. Whether the outcomes align with overall organization goals or not. It's a continuous cycle where you'll define problem statements and hypotheses, build insights, take actions and determine if your outcomes meet? expectations.???

Why will no-code/low-code tools be valuable in Analytics?

?? If you have studied the usage of analytics tools in your organization you will notice that, most users are operational users. Operational users usually focus on a single workflow and analyze it end-to-end to be able to gather insights. No-code/low-code tools can enable them to be self-sufficient.

?? With a No-code approach,? building analytics is as simple as putting together lego bricks. They lower creative barriers for Citizen Developers. thus, enabling more?people to work with data. However, data knowledge must be distributed among users. You cannot hope to become data-driven by having an analytics tool that relies on just one or two people.

?? No-code helps in saving time on your internal resources. Data and tech people are scarce and costly. Is making them work on fixing data issues, building queries and dashboards that require repetitive work worth their time? No. No-Code lets engineers save so much time and focus on tasks that matter.

No-Code is a great way to distribute your data culture to actually more people than just the data engineers.

What do you think about No-Code analytics? Do share your thoughts in the comments.

#analytics #pmlearning #productmanagement #nocode #lowcode