How to onboard customers with maximum speed, minimum effort
Companies who manage and analyze data on behalf of their clients often have one common problem – taking data from many sources, in many formats, and getting it in the right format to where it needs to go.
Whether the challenge comes from having to process large volumes of data, dealing with complex data formats, or having thousands of data feeds you need to consolidate, data ingest can often be a bottleneck to onboarding customers or providing timely insights.
Dealing with the data ingest task by manually manipulating data or maintaining a complex network of scripts can lead to:
- Lack of collaboration – individuals building a data solution in silo can make sharing work difficult and problem solving challenging.
- Lack of productivity – developers spending time on data processes means they’re not spending their time on more valuable work.
- Lack of scalability – there’s only so far you can grow by throwing more people at the problem. Relying on manual effort will always be a bottleneck, and an ever-growing landscape of scripts quickly gets impossible to maintain efficiently.
Data ingest done right
Let's look at Gain Theory, one of our customers. They provide a unique marketing analytics platform, and to get data into the platform they need to ingest thousands of client data feeds at speed.
But those data feeds are hardly ever in a standard format, and so each one needs customized processing rules.
At times, people in the business had built their own processes to standardize the data, with different teams using different tools.
Transformation Practice Lead at Gain Theory, Brian Suh, describes the issue: “It didn’t translate that well into team efficiency. It was working, but I knew there was a more elegant way to organize the data to create faster, more efficient results for our clients. We saw an opportunity to do better”
Gain Theory talk about streamlining data ingest processesAs Suh explains “You can have thousands of lines of Python code, and if you didn’t write the Python, and the person who wrote it didn’t comment it very well, it’s impossible to read. And it’s impossible to fix when it breaks.”
But with CloverDX’s visual, repeatable approach, data workflows became more readable and more transparent, as well as all being managed from one central place. The Gain Theory team can work and collaborate more easily now they don’t have to rely on complex code, and the workflows are also easier to maintain – creating a robust, reliable and scalable platform.
Data Ingest with CloverDXHow CloverDX helps streamline data ingest
Improve collaboration and productivity
A shared platform not only brings more efficiency as people can work together, but can also shorten time to delivery as components can be shared and reused, rather than having to create them from scratch or recreate something in Python that was written in SQL.
Scale easily
It’s simple to add new data feeds into the centralized, automated process. And dealing with increasing volumes of data is also no problem, so you can onboard any number of customers as you need.
Troubleshoot more effectively
Unlike undocumented scripts, with CloverDX you get a visual representation of your data workflows, making troubleshooting faster. And the centralized platform gives you better visibility into everything that happens (and anything that’s gone wrong).
Solve 100% of your problem
If 90% of your process is easy, then CloverDX is built to help you solve the other 10% too. With CloverDX, everything sits in a single platform, you don’t have to go to external scripts to get things done. The combination of the readability of a visual interface and the ability to code whenever you need means you get both efficiency and the power to customize what you need.
Share
