There’s nothing more frustrating than laboring away at the early stages of your data science project, only to discover that the success of your model is undermined by mistakes or oversights made from the outset. Data preparation takes time and care to get right — and if you do, you’ll be off to an excellent start.
But that doesn’t mean you have to undertake all those tedious cleaning and prepping tasks manually. The key is to invest in data preparation tools. No, scrap that: the key is to invest in the right data preparation tools and/or data science platform. You need to know exactly what you’re looking for, to ensure the platform or tools you choose are up to the task.
For your machine learning project to run smoothly and productively, you need to have the right data pipelines and infrastructure in place. For starters, you’ll need a way to move away from siloed data and towards a platform approach. That’s because silos make it very difficult to get visibility over your data, to spot errors and inconsistencies, or to manage and integrate this data effectively.
Instead, you need a way to pull together all your data into one place, so that you can treat it as a holistic, harmonized resource. You’ll then be able to keep track of what you have, clean it properly, combine it in useful ways, and collaborate across departments.
What’s more, your data preparation tools need to work hand-in-hand with the data infrastructure and storage systems you have set up. If you have a robust platform that automates connections to different types of datasets and takes care of cleaning and harmonization tasks, you can cover all these bases at once. The platform will play a key role in establishing the framework you need by bypassing silos and bringing all your data into a single source of truth. Even better, it will combine this with other key stages in the data preparation process by allowing you to perform all your tasks at the same time. Plus, it does all this in a way that allows you to trace back through to the source of the data quickly and easily, allowing you to document the lineage and ensure good data governance throughout.
Long lead times are the enemy of any machine learning project. Oftentimes, you can’t know for certain exactly how valuable a particular model or line of inquiry will be to your business until you’ve tried out your model with real data. That means you need ways to adapt and experiment quickly and efficiently, without wasting time on the early stages of a model or product that won’t deliver results.
All of which makes automation in your data preparation tools absolutely crucial. The system you use must allow you to build automation systems (or implement existing ones) easily and at scale, getting your models to production faster. If your data has not been prepped — i.e. cleaned, organized, and enriched — in advance, your platform needs to take care of the heavy lifting for you.
It’s all very well having awesome automation built into your tools or platform, but can these cope with the realities of what you need to do? Can you build automated processes based on your full data, even if you’re using unstructured and complex data sets? Or do these automation processes only work when applied to small samples of data and a handful of data types?
For example, can the tool you plan to use handle unsupervised machine learning tasks like clustering? This is a highly valuable part of data preparation, especially when you need to organize large, unstructured datasets into something easier to navigate. It helps you figure out how to divide the data into subsets, identify categories and classes, cut down the time it takes to annotate and classify data, identify anomalies, and generally start to answer crucial questions about your data.
Having a tool or platform in place that can automate this part of your data preparation, no matter how large, complicated, or unstructured the dataset is, will save you time. And will open up new possibilities and lines of investigation for your data science project, right from the outset.
Usability is crucial. No matter how powerful your data preparation tool is, if you can’t access it and get to grips with it quickly, it won’t be much good to you.
It’s important to choose a tool that’s easy to navigate and can connect seamlessly to all the internal and external datasets and databases you want to use, including programming languages like Python. You also need to take into account your team’s skillsets, technical capabilities, and bandwidth. Will this tool make their lives easier or harder? Will they have to invest time and hassle into learning to get value from it? Could it end up hampering their work? Will they even use it?
The important thing to remember in all of this is that you are looking for ways to streamline your data preparation process. When you compare tools and platforms, keep asking yourself: will this remove headaches, or will it add to my workload? Don’t be tempted by flashy technologies that actually overcomplicate matters. Focus on ways to consolidate tasks, make data preparation tasks swifter and easier, and give you a single point of focus to work on your data, while improving the quality of your work for successful projects later on.