Data can be an important analytical tool for your organization regardless of industry or niche. However, sometimes data has to be shifted, altered, or transformed in order to be leveraged properly or understood for maximum effect. After all, the way data is presented can impact how people read it, the conclusions it fosters, and more.
Today, let’s take a look at the different types of data transformation practiced by organizations like Mozart Data.
The first type of data transformation is translation and/or mapping (depending on how you define it). Put simply, data translation involves linking or connecting data points to other elements, including other data sets or points.
Say that you have a business with many different error codes for important machinery. By translating or mapping those error codes (as distinct data points) to specific conditions or failures, that data becomes much easier to read without having to call a data technician on-site.
The benefit of data translation and mapping is that people can more easily use the data and see how it affects real-world metrics or things. Think of data translation and mapping as making data more easily parsed and usable by the average person. It’s crucial if technical data needs to be utilized by everyone throughout your organization, not just data scientists.
Data summarization is also very important. As the name of this transformation suggests, data summarization involves rounding up and summarizing data sets to provide their most important or salient points to readers/executives.
Imagine that your data scientists have collected a massive wealth of data on your target audience and potential customers. Rather than going through the data point by point, your data scientists then analyze and summarize it to provide company executives with a few important bullet points or conclusions.
In doing this, executives can make wise decisions without having to go through all the data by hand or having to spend lots of time doing the same exercises the data scientists did previously.
Data filtering is related to data summarization, though it’s not exactly the same thing.
Data filtering involves looking at big data sets and filtering out unnecessary or unusable information that otherwise clogs up the entire analytical process. For instance, if you run a survey and have a data set with consumer responses, and a certain number of those responses are joke answers or don’t have any important information, you can filter those data points out to make a more understandable, usable data set.
Data filtering necessarily requires eliminating certain data points, however, so it should only be practiced by trained data scientists or those who know how to accurately identify points that can be filtered out without contaminating or ruining the rest of the data.
In the digital age, data anonymization is highly important. It involves masking or encrypting data, such as crucial company data or the private data of your customers/site visitors. With data anonymization, you make sure that data can’t easily be stolen or read by a cybercriminal, which helps to minimize the possibility of identity theft (and which protects your organization from fees due to legislation like the GDPR).
Data anonymization and/or encryption is arguably the most important type of data transformation, especially for modern companies that want to keep their corporate information secure at all costs.
Data enrichment involves merging data from different sources to induce more complex and more useful conclusions from it.
Imagine that you have a business with customers who purchase products from your retail or in-person stores. You also have customers who purchase products online or through other channels.
To understand how much every customer spends on your business each month, you should not view each transaction individually. You’ll benefit much more easily by combining the transactions into a single figure you can then analyze with your other major business metrics.
Data substitution is related to data enrichment. When you perform data substitution, you alter or otherwise substitute values in your data to standardize data points across a larger set. Alternatively, you may use data substitution to scramble data points or values to assist with encryption or protecting the identities/personal information of your customers.
The benefits of data substitution include:
- Assisting with data security or encryption
- Helping large data sets feel more “unified” for easier analysis
- Faster and easier time analyzing big data sets through standardization
- Easier organization of data throughout your organization
All in all, each of these data transformation methods could be useful for your organization. It’s a good idea to ensure that someone on your team knows how to practice each type of data transformation so you can twist and evolve data sets when needed – and so you always know what your data tells you rather than coming to incorrect conclusions.