Your business almost certainly has access to a lot of data. This information can tell you how well your company performed over a given period, as well as where it went wrong, how it’ll perform in the future and what it needs to grow and improve. But unless you’re a data scientist (and even occasionally if you are), it’s not always easy to draw these answers from it.
The benefit of data wrangling
Also known as ‘data munging’, data wrangling refers to the process of cleaning up, editing, structuring, enhancing and validating a raw dataset and finally presenting it in an intelligible form. A jumble of numbers or words becomes a clear set of results or a plan of action for businesses or individuals.
Many businesses still use legacy systems which either struggle to collect and process large amounts of data, or store it in messy, inefficient or inconsistent ways. That means that data scientists often spend a large chunk of their time on the cleaning and organising part of data wrangling.
This is all time before the data is actually being analysed and starting to tell a business something useful.
Does your business need to automate its data wrangling?
Collecting, cleaning and enhancing data isn’t always time wasted. It’s an important job and can help data scientists better understand a dataset, develop their skills and do a bespoke analysis.
But most businesses could greatly benefit from automating the majority of their data wrangling. This is for three main reasons: it takes less time, costs less money and leads to fewer mistakes.
Using machine learning and artificial intelligence, a new crop of companies provide automated data wrangling systems which also present data within easy-to-use dashboard systems and provide regular notifications and data-based recommendations. As a result of these industry innovations, 'business decisions will now be based on valid data, which massively increases the chances of good results,’ according to the experts at Avora.
What kinds of businesses could benefit from automated data wrangling?
The best way to know whether automated data wrangling is for you is to seek out a free consultation and weigh up the benefits.
A few types of business that would typically see a big transformation include:
- E-commerce firms that need to learn about and quickly respond to consumer behaviour
- Marketing firms seeking a richer insight into campaign analytics
- Manufacturing and logistics firms aiming to increase efficiency across their operations and supply chains
- Energy firms that want to understand consumption patterns and improve network performance
- Consultancy firms looking to provide more data-based insights to clients
How does automated data wrangling work?
The issue with automating data wrangling was previously that it doesn’t simply require the automation of repetitive processes, which are easy to get a computer to do. Data wrangling requires intelligence to find good data, root out bad data, convert it into the required format – the list goes on.
That’s where advances in artificial intelligence and machine learning come in, with the two technologies now able to work in partnership to build, test, deploy and review algorithms within a live environment in a way that would previously have to be done by a team of data scientists or engineers.
It’s a complicated science, but the main thing to know is that it’s these two tools, sometimes collectively known as AutoML or ‘automated machine learning’, that have transformed our ability to read raw datasets quickly and to bring this capability to non-experts in the field.