Why You Need a Full-Stack Analytics Solution
Why You Need a Full-Stack Analytics Solution
Conventional data analytics solutions are great, but when you start asking more complex questions, you're going to need something more advanced.
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Conventional out-of-the-box analytics solutions — like Google Analytics — are mainstays for the early stages of many companies. They're a cheap, quick way to get some analytics capability into your company. And when you're only just starting to figure out what your product even is, they seem like a reasonable way to peek under the data hood without stretching your resources.
But then you start asking more complex questions, like:
- How long does it take for a new user to post a piece of content?
- How likely are canceled customers to come back when they get a reactivation email from us?
- What actions do users take in their first few weeks that best predict retention?
It's at this point that you start to need more than what your "easy" out-of-box solution is giving you if you want to move forward with building your product and your company.
High-Growth Companies Need Better Analytics
Building your own analytics hits directly at the big problem with these analytics solutions: they aren't built to give you complex, real-time information about your users' (or any other type of actor's) behavior.
They can't be because conventional out-of-the-box analytics solutions only address parts of the analytics pipeline. In order to achieve maximum interoperability and market size, they sell you just the dashboard or just the ability to A/B test.
Trying to build the right mix of data collection and accessible analysis can lead to supplementing out-of-the-box tools with add-ons and apps that work in tandem with one another. But with a patchwork of off-the-shelf solutions, you end up with:
- A rigid pipeline you can't change quickly
- A lack of transparency and trust (How do I even know this data is right?)
- Complexity that inhibits you from being able to have consistent, easily accessible data
...which makes building your own analytics seem like a good option-one that's flexible, with reliable data, and completely customizable solutions for your business.
You're Not Gonna Need It
Unless you're Facebook or Google, you'll quickly realize it's pretty much insane to build your own analytics. Without Google-esque resources, you simply can't sustain costs or maintenance.
To pile on, it's also difficult to get the backend system in place while also making something that's easy for people to use. So even if you're collecting and storing your data, you have to spend even more time and energy building a visual interface that lets everyone harness that data.
As tempting as the promise of building in-house analytics seems, it's not feasible. And it probably wasn't what you should have been considering, anyway — especially not when there's a better, easier option out there.
How the Full-Stack Analytics Solution Works
In general, the idea of something being full-stack is roughly the same as the idea of something being vertically integrated: You create and control all parts of your process from beginning to end. In the tech space, Apple is often cited as an early powerhouse of the "full-stack" method, creating both hardware and software for their product.
For analytics, a full-stack system has to include its own user interface, query engine, persistence, and data ingest framework. That is, it has to include functionality for every service on the backend but also have the frontend user interface that makes it usable.
The key here is that good full-stack analytics solutions should be flexible enough to allow you to really get into answering questions with your data. You should be able to do more than just set a few parameters on a funnel, like some out-of-the-box solutions. You should be able to create the metrics you need easily within your analytics interface.
Full-stack analytics also need to be able to scale to your needs with ease. Any good full-stack solution should be able to handle however much data your product and users can throw at it. Having the capacity to scale is crucial for any full-stack solution.
Now let's take a look at how a full-stack solution helps you harness your data.
Why You Need One
What full-stack analytics solutions really enable you to do is to open up data to your team. This means that sales and marketing can answer questions about users, PMs can corroborate their hunches with product analytics, and engineers can check in on their feature releases.
When data is accessible to everyone, it is a much more valuable asset to your company. Full-stack solutions enable that particularly by:
- Having an interactive UI. A friendly, interactive UI means that anyone can feel comfortable poking around and exploring behavioral questions. When you remove the need for knowledge of code or queries to access data, you also free up overburdened data science teams. With a little time, anyone should be able to manipulate the interface to answer their own questions.
- Speeding up the whole process. With a full-stack solution, there's no waiting for engineers to retrieve data, no waiting for data to be cleaned. Raw data should be ready to be manipulated whenever you want. This means that you can reduce the amount of time that any given person needs to go from questions to potential answers, graphics, and reports.
Full-stack solutions facilitate this ease of access because there's no intermediary and no parts to interlink. The data goes from being ingested to being manipulated in one well-oiled system. So when you have questions about user engagement and retention and product, you can get right to finding answers.
Full-Stack and Full Speed Ahead
The full-stack analytics solution is the best option for most companies looking to put in place the best analytics possible for the least amount of time and effort. Bypassing the total mess of creating your own analytics pipeline, but maintaining the flexibility to answer your questions, full-stack analytics platforms are a great fit for most companies.
Any good full-stack solution will not only help you speed the process of putting analytics in place, it will also help you get data into the hands of everyone who wants to use it — with no waiting. This opens up your company to the wealth of information that data can offer to teams across your company.
Published at DZone with permission of Aditya Vempathy , DZone MVB. See the original article here.
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