Preparation is the most time-consuming and least exciting part of analyzing data. But cutting corners or making a mistake will have significant consequences on any analysis your team performs after.
The right analytics platform, however, can turn the data prep process into a well-oiled machine: streamlined, automated, and simple. You can even make multiple steps repeatable, minimizing the risk of error and increasing the accuracy of your downstream analysis. This can give your team hours back in their day so they can work with a wider range of analytic techniques and focus on advanced analysis.
Here’s how.
Access Data from Anywhere
Moving large amounts of data or pulling from multiple systems is a time-consuming and constant struggle for many teams. If your analytics tools don’t easily connect to all your data sources, it will take more time and energy to gather everything you need, and might even require more expertise than your team has.
Choosing an open analytics platform ensures that no matter where your data lives (either now or in the future), you’ll be able to access it. This includes a variety of standard or non-standard databases, data lakes, apps, or simply a desktop folder. The more easily you can connect to any source that houses your data, the more efficient your process and the more time saved in the end.
What’s more, open platforms support a wide array of different data types, such as images, sounds, or spatial data, as well as domain-specific file formats like networks and molecules. This enables teams to work with more than strings and integers.
Reuse Data Prep Steps
Whether you're grouping data or removing outliers and missing values, the action you build out is not easily reusable with scripting language alone. You have to recode and manually re-perform the same task because there’s no way of capturing what you’ve already done.
Low-code, no-code analytics platforms are built to self-document (at any level of complexity), so you can save segments or full workflows. They also let you build a library of saved workflows that you or anyone on your team can easily access and reapply. This cuts down on time even further by removing manual and repetitive work. Plus, no one is stuck reinventing the wheel.
Low-code/no-code doesn’t just mean an easier way for data teams to build a workflow; it also means you can save that workflow, reuse it over and over again, and share with colleagues. Self-documenting workflows can help teams set standards for how data is prepped, enabling easy knowledge sharing.
Save Even More Time with Automation
Embracing automation improves accuracy and productivity, and accelerates time to insight. That’s why it’s important to identify which manual and repetitive data prep tasks can be replaced with a more streamlined process.
As a spreadsheet alternative, the right analytics platform can allow you to execute repeated steps automatically on any file or data source. For instance, you can schedule a workflow to read multiple lists from different locations, as well as modify, remove, or combine the data in those lists.
If this is a task you need completed every month, day, or even hour, or it’s a data cleaning best practice you complete prior to every project, it can be scheduled whenever you want it to run (as many times as you need), and then you don’t have to worry about it again.
When you can standardize and expedite a process like data preparation, you can then spend more time on deeper analysis, use your expertise to provide more insights to end users, build data apps so departments can visualize and experiment with results, and so much more.
Download this eGuide to learn more about this and other must-have capabilities when choosing a data analytics platform, and how you can get end-to-end support on any data project.