Data transformation is the process of cleaning data in order to allow for more expansive analysis and to streamline decision making at the executive level. Initially, when the data is extracted from a source, it’s not necessarily in a format analysts can use. It might be hard to compare data. It might be hard to know what you’re looking at. A lot of the time, data transformation reformats data in a way that allows for analysis to be at least feasible. What does this look like in practice? Well, there are four types of data transformation. They are as follows.
Translation by way of mapping is the most mundane type of data transformation. Simply, it links the contents of one data set to the contents of another data set, allowing an end user to better understand an initial dataset. A good example of this is the automotive repair industry. Let’s say a technician has uncovered a problem with a car and is in need of a quick way to discern what’s happened to the car. With mapping, data can be distilled from other cars with similar problems. To understand a car’s problem via mapping is to understand that car’s problem much quicker because old data is being used to expedite the process of the analysis of extracted data. Combining data through mapping simplifies the process of drawing conclusions from data. A data analysis of vehicular error codes can result in faster identification of a problem, allowing for the pursuit of advanced solutions. In this way, one would be using past mistakes to one’s advantage instead of failing to learn from those mistakes completely. Certainly, a car technician might know what a single error code means, but an advanced analysis of data could allow for unique observations in the interest of distinct solutions. Mapping is perhaps most useful in situations where a business has to employ several customer-facing applications at the same time.
Summarization is the process of filtering out irrelevant data. When you need information for purposes of data analysis, you don’t necessarily need all the data to which your business has access. The bigger your business is and the more information it maintains, the less likely it is that all data is necessarily relevant. As most companies pull data upon data in real time from similar sources. This constant intake of data makes it very unlikely that even most data will be relevant to a certain data analysis proposition. To solve the issue of irrelevant data, you can use summarization to reduce the data with which to work by filtering fields, columns, or rows from a certain data query. To consolidate data in this way is to significantly simplify the process of data analysis. For example, while you might not care about the size of packages customers purchase, you might want to know how often customers order packages and how many those customers order at a time. Summarization would help resolve this type of concern.
Perhaps a major part of data analysis will be to preserve the anonymity of users. Essentially, relevant data must be separated from identifiable data. Without regard to specific legal issues, it’s probably not a good idea for a data team to have access to the identifying information of countless people across some medical or health-adjacent context. Sifting through identifying data is inherently risky and should be avoided at all costs. Any information that could lead to a breach in privacy or security ought to be scrubbed. In a lot of industries, the law dictates some form of encryption for identifying information, so scrubbing data is mandatory for most businesses, anyway. Generally, what’s not really possible is to guarantee the safety of identifying information while that information is on the internet in some capacity, but through anonymization, that identifying information is gone from the start, keeping you and your customers safe from identity theft.
Enrichment is the process of merging data from disparate sources in the interest of a more effective data analysis. Merging is a critical aspect of enrichment. If you want to keep track of how much money a customer spends at your business per month, then you do not actually need to keep track of every individual transaction. All you need is the sum. Adding these sums to a chart of customers’ information would allow for you to save storage space and lower business costs. Plus, you’d have a more concise way of predicting sales for the coming year. With regard to data substitution, there are situations where you might want to have certain data nodes adhere to certain standards. You might want to update “Bob” to “Robert” for all people named “Bob.” By doing this, you would guarantee consistency across all data.