Why Data Standardisation Is So Important: Tips For Success

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.

Key Takeaways

• Data standardisation is the process of transforming data into a consistent and uniform format that can be easily accessed and used by different systems and users.
• Data standardisation is essential for financial automation, as it enables faster and more accurate data processing, analysis, and reporting. It also reduces errors, risks, and costs associated with manual data handling and manipulation.
• Data standardisation can be achieved by using a no-code automation platform like SolveXia, which allows users to easily connect to various data sources, apply data transformation rules, and validate data quality.
• Data standardisation can deliver significant benefits to finance teams, such as improved data integrity, enhanced data insights, increased operational efficiency, and better decision making.

Coming Up

1. What is Data Standardisation?

2. Why is Standardised Data Important?

3. Data Standardisation vs Data Normalisation

4. Data Standardisation Use Cases and Examples

5. How to Standardise Your Data?

6. Final Thoughts

What is Data Standardisation?

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:

• Simple mapping- external sources: Pulling data from external systems and mapping the records to an output schema

• ‍Simple mapping- internal sources: Pulling data from internal systems and creating a single, unified, and trustworthy dataset for the organization

Why is Standardised Data Important?

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:

• Application inefficiency or failure
• Duplicate records
• Poor marketing attribution
• Need for more manual labor (which can cause more human errors at the expense of time and money)
• Poor lead scoring and inaccurate market segmentation

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:

• Create a seamless flow of usable data
• Achieve accurate market segmentation and lead scoring
• Benefit from improved analytics
• Gain personalisation and the ability to tailor your messages to your audience
• A way to share data between business intelligence and artificial intelligence systems

Data Standardisation vs Data Normalisation

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.

Data Standardisation Use Cases and Examples

Let’s see how data standardisation is useful in practice by considering the following examples and use cases:

Use Cases

• Online travel agencies pull data from various airlines, hotels, and car company rentals to assess inventory. They must standardise data in order to have an accurate review of availability to offer to their customers.

• Holding companies use data standardisation to pull financial information from each subsidiary. This is necessary to ensure that financial documents are correct. Data automation tools can help collect, standardise, and store data in a centralised system for easy access and review.

Examples

• First and last names may be structured differently in different systems. For example, one source of data may collect the full name in one column, whereas the next will separate first and last name. To aggregate the data, you’ll need the same structure (rearranging).
  

• If there are extra white spaces or punctuation marks in data records, you may need to remove them.
  

• Domain value redundancy refers to the fact that different units of measurements may be used. For example, airlines may store data in aeronautical miles or ground kilometers. To compare data, measurements must match.
  

How to Standardise Your Data?

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:

1. Know your needs:

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.

2. Assess data entry points:

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.

3. Define data standards:

Create a standard template for how you want to collect and store data for standardisation. Some considerations worth outlining at this step include:

• Company names - capitalized, fully spelled out, or abbreviated (i.e. Johnson & Johnson or Johnson and Johnson)
• Phone numbers - use of area codes in parenthesis or hyphenated (i.e. +1 [888]-412-3104 or  667-8976 or +1888-412-3104)
• State names - fully spelled out or abbreviated into two letters (i.e. California or CA)

4. Clean Your Data:

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.

5. Use Existing Measures and Questions:

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.

6. Normalise Your Data with a Data Automation Platform:

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:

• Common formats: Record data in the same format every time when you collect it. For example, if you’re storing data for expense reports, use decimal places every time for monetary records (i.e. $100.00 and $56.67).
  

• Pre-set standards: Utilise any predetermined standards for certain types of data points.
  

• Z-scores:  Instead of using data’s own scale for reference, convert data into z-scores. This provides you with an understanding of how far (standard deviation) that the record is from the mean (average). The formula to obtain a z score is: Z = value-mean / standard deviation.

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.

Final Thoughts

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.