Numbers should be delivered with objectivity. However, when data comes from various sources and has been collected according to varying practices, it may not be easily comparable. That’s why the process of data standardisation should never be ignored. With data standardisation, data can be processed, analysed, and compared more efficiently and accurately.
Here, we will define what data standardisation means, the importance of this practice, and how you can standardise your own data.
Data standardisation is a process that converts data into a common form so that research, comparison, and collaboration can take place. Before data is loaded into a centralised system, data standardisation calls for data transformation and reformatting.
The outcome of data standardisation is such that the user can analyse data consistently. Different systems store data with different formats. So, when an automation tool (or human) pulls together data from various sources, they may not match in their structure. However, in order to analyse data effectively, data should be standardised.
Data standardisation usually takes place in one of two ways:
Imagine a library where every shelf is organized according to a different schema - one shelf is categorised by genre, the other is organised by book color, and the next is alphabetised by author’s last name. It’d feel impossible to locate the book you’re looking to read.
In the same way, data without standardisation can create chaos and just become a way to store information without providing the value to be able to use it.
Data without standardisation can wreak havoc within your business by causing:
At the end of the day, the aforementioned inefficiencies can and will result in lost revenue and opportunity cost. To avoid these downfalls, you can leverage data standardisation to:
When using machine learning, data normalisation (also known as scaling or min-max scaling) is used to standardise the range of features of data. Data values can be infinite, but through normalisation, each feature falls between a range of 0 to 1. The outcome is that data can be visualised and described using a normal distribution (a bell curve), where roughly the same number of observations fall above and below the mean.
Standardisation (or z-score normalisation) transforms data such that the distribution has a mean of 0 and a standard deviation of 1. Neural networks, logistics regression and SVM use z-score normalisation.
Let’s see how data standardisation is useful in practice by considering the following examples and use cases:
There are a variety of ways your business can standardise its data. The way that you collect, store, and share your data will depend on why you need the data in the first place.
Before choosing the method, it’s useful to answer the following questions:
Understand why you are collecting data in the first place. This will include questions like: “Will this data help us to make better decisions?” “Who uses this data and why?” and “Is this data field useful or redundant?”
When you have the answers to these questions, you have a better idea of the type of data you’re collecting. With this knowledge, you can begin to see how you can group data and normalise large data sets with consistency.
With today’s technology, data exists virtually everywhere and anywhere. From finding out information about a potential customer through their browsing activity to directly asking them for data via a survey or email newsletter, data entry points are ubiquitous.
So, it’s useful to define and answer questions like: Where are you pulling data from? How often do you receive new data? Platforms and business intelligence tools will store data in their own way, so you could end up with 5 different ways to name the same company. You’ll want to know the structure so you can avoid data redundancies.
Create a standard template for how you want to collect and store data for standardisation. Some considerations worth outlining at this step include:
At this point, you know the data you need, where you get it, and how you’re going to standardise it. But, before you get to work on organising data, you need to be sure that it’s “clean.” Clean data refers to the fact that the information is complete, correct, and properly formatted.
Before you begin working on the data or allowing an automation software solution to utilise the data, it’s paramount that it’s accurate. Otherwise, you run the risk of deducing false information and analytics, and in turn, making ill-suited business decisions.
No matter how much you know or don’t know about data standardisation, there are existing measures that you can rely on to help standardise your data. Automation solutions make this intuitive because you can choose from existing options for data standardisation methods.
For example, you can use batches and list imports based on templates. To illustrate, batch normalisation is helpful when you want to standardise records like state names, fix title case and capitalisation of company names, or to normalise phone numbers so auto-dialers can function.
Rather than having to manually edit each record one-by-one, data automation platforms can normalise large datasets in seconds, saving you time, money, and mistakes. By way of automation software, data gets automatically segmented and stored.
Furthermore, the system can remove duplicate entries and help you to create personalised and targeted content to better serve your customer base. Additionally and importantly, you’ll be assured of accurate analytics because you can trust that the data exists as it should for your business processes and purposes.
Some methods for standardising data may be:
You can leverage automation tools to complete most of the heavy lifting for you. Automation tools are equipped with the power to pull data from multiple sources and aggregate records without the need for manual work. The process of data cleaning can help to remove redundancies, standardise data in the same format, and ensure that it is complete (no missing information) so that data analysis can be performed with integrity.
Businesses need data to perform at their optimal levels. Given the mass amount of sources from which to collect data, there’s no doubt that data comes in different shapes and sizes.
However, through data standardisation, it’s possible to organise data so that it can be usable. This helps to ensure that data is accurately reflected so that business leaders and various organisational systems can analyse collected records accordingly. With a data automation tool, this can be achieved easily and securely.
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