Written by GoodData Author |
Let’s say you’ve been tasked with deploying analytics, and everything is finally up and running. Everything appears to be going smoothly, but before long a trouble ticket comes in. A customer is having an issue where a certain bar chart isn’t showing up, and all he or she can see is a gray box in the corner. You want to get that customer up and running as quickly as possible so they can get back to work, but who do you call to fix the problem? The visualization provider? Whoever did the embedding? The ETL provider? The consultant? In this moment, you’ve realized the value of going with a full-stack solution instead of a piecemeal approach.
Going with a full-stack solution has a host of benefits, but two stand out most clearly to me. First, a full-stack solution brings all of these disparate parts—visualization, data management, ETL—together under one umbrella. Second, with one company handling the entire solution, you know that if anything goes wrong, you have a single point of contact.
Full-stack solutions package everything together
When it comes to analytics, most tools on the market excel at one thing in particular. That means that a company wanting to introduce analytics would need to find a visualization tool, a data management tool, a modeling tool, and others to build out their analytics offering. But when you’re embedding analytics, especially at scale, do you really want to spend your time finding the best individual tool for each task? The best tool for data transformation, then the best tool to separate out the data, then the best tool to visualize that data, then a tool to manage all those customer instances?
When I was a product owner, I certainly didn’t. I didn’t feel comfortable going out and finding the perfect data warehouse, the right modeling layer, the exact ETL that fit our needs, and then cobbling all of that together on my own.
A full-stack solution removes much of the uncertainty associated with analytics, because each of the components has been designed to work together. There’s no risk of choosing a data management tool that doesn’t play nice with the other tools you’ve selected. With a full-stack solution, everything from data ingestion to data preparation to modeling, analytic visualization, and distribution is handled for you.
With a full-stack solution, you get one point of contact
A piecemeal approach leaves plenty of room for things to fall apart or slip through the cracks. When they do, it’s, unfortunately, your neck that’s on the line as you figure out who to call to fix a given issue—not a great position to be in.
In my previous life as a product owner, one of the biggest selling points of a full-stack solution was that it meant I had a single point of contact for any issue with the analytics. In the example earlier, I wouldn’t have to figure out who to contact and then potentially help two disparate tool providers work together to solve a customer issue. Instead, I could call up my solution provider, tell them about the problem, and know that it would be fixed quickly and by the right person. This reduces the headaches associated with managing separate vendors, consultants, and project teams throughout deployment and on through the life of your analytics.
A full-stack solution like GoodData’s is a one-stop shop for everything you need for analytics. With mechanisms for extracting your data, modeling it, and protecting users from data chaos behind the scenes, these solutions are able to be deployed to tens of thousands of users across multiple companies easily and with minimal effort on your part. If you’d like more information on the benefits of full-stack solutions or if you’re looking for more in-depth technical content, check out our platform whitepaper. It’s designed to give you an overview of exactly how GoodData is able to deliver powerful analytics to massive audiences—while still being the most cost-effective platform on the market.
Written by GoodData Author |
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