To change the world with your product offering. But what does that mean?
An incredible product is a product that your customers derive value from. It's a product they derive more value from than from the products your competitors are releasing out into the market. Ideally, it's also a product they enjoy using.
To get there, product teams need to:
In order to understand, evaluate, and respond to all of these things, product teams undoubtedly need access to data.
Think about it. You can’t differentiate a product without competitive intel. You can’t resolve bugs you don’t know were reported. You most certainly can’t measure your product against your customers’ satisfaction without some idea about whether customers are happy, if they're buying your product, and if once they're purchasers, that it satisfied their needs.
In the case of product teams, the data that can clarify questions like these usually lives in a lot of different places:
Especially critical for product teams are the product analytics and usage metrics which can detail which users interact most with your product, how they are using your product and how often. These data, especially when paired with the other data points mentioned above can provide meaningful clues and insights into what would make your product better, or in other words what would help entice more users to use your product more often.
The challenge then becomes harnessing these data that live in so many places.
I’ve chosen the word harnessing really carefully here—because the challenge is no longer in data analysis.
Most organizations have invested in self-service data analytics and BI tools that empower semi- and non-technical users within cross-functional teams to derive insights from data the way only statisticians previously could. In other words, the data analytics problem has been “de-skilled”, resulting in up-skilled product teams.
There’s one glitch here: your team can't use a self-service analytics tool to analyze data without having usable data in there first, because as we all know the golden rule to analytics is: garbage in, garbage out.
So how do most cross-functional teams (like product teams) approach data integration?
Typically, they don't.
They rely on central ingestion teams. The reason is simple: legacy data integration tools are hard to use. They require technical specialists or data engineers to operate them. And that would be fine, except you, like most modern enterprises, are building cross-functional product teams. Teams that are meant to lead data-driven projects and initiatives end-to-end, increasing the speed at which these projects get completed and, subsequently, the number of projects run.
It's clear where this is going: your team is not carrying projects end-to-end if data integration is handled by another team. And your team can’t be expected to handle data integration using the same heavily technical tools that the central ingestion team is using.
Why not do the same thing you’ve done with analytics, this time with data integration? De-skill the problem, up-skill the team?
You haven’t because a tool that enables this hasn’t existed. There are self-service data integration tools out there (and there have been for years) but they have failed to dominate the market for two simple reasons:
The data governance point is especially crucial. No IT department will approve data integration software that does not have the necessary governance features built to keep your data safe and secure, nor should they.
You probably have an idea of what's coming:
That's right, finally someone out there has developed exactly this hypothetical tool I've been describing.
Seriously though, a self-service data integration tool that doesn’t compromise on governance is exactly what we’ve built. And we built it with your cross-functional team in mind.