Businesses today are oversaturated with different types of data from a number of different sources. Without understanding the different types of data transformation, companies will be left with a wealth of useless information. Transforming data into something your organization can easily interact with may not only streamline efficiency, but may also transform how you do business!
The Deal With Data
When data comes into your business from many different sources, there’s a strong likelihood that it will be formatted uniquely. Even the smallest difference may be enough to throw a wrench into some search queries. Transforming data simply means organizing it in a way that will allow a particular organization to use it in the most efficient ways.
There are a number of different tools and techniques used to manipulate information, each depending on how the data is organized, structured, and formatted. Using the proper tools, developers can translate between several formats including XML, non-XML, and Java, allowing for rapid integration with a number of different software systems.
What Are The Types Of Data Transformation?
Data can be manipulated in a number of different ways. Generally, they fall under four main categories. Aesthetic transformations are stylistic changes including standardizing address information. Removing, adding, and manipulating columns is a structural transformation. Constructive transformation rebuilds data, adding and copying information until it is standardized. Destructive transformation actually eliminates some data to make it more useful to the end user.
When data isn’t transformed, there are always risks that arrows can creep in. Taking steps to organize information is verified, reducing the risk of errors cropping up in future work. There are eight ways that businesses can transform their data. These methods help companies find the value in all of their data, either by manipulating its structure or reorganizing the construction of the data set itself.
1. Attribute Construction
Using software to look for patterns in data, called data mining, is an essential task that lets companies turn their raw data into usable information. Attribute construction adds to or changes specific characteristics of data to make it more dynamic. For example, a set with “length” and “width” attributes becomes more functional by adding an “area” attribute as well.
Aggregating data collects it in its raw form, expressing a summary of that information for statistical analysis. Gathering information over a specific period of time, for example, allows statisticians to find patterns as they appear over time, and can check that aggregate in several other ways as well. Performing time or spatial aggregation can help businesses notice changes both chronologically and spatially.
Companies with a lot of data may need to condense it down to a manageable size. These types of data transformation do so, but without sacrificing the integrity of the information. Sets can be analyzed without needing to worry about data loss.
Also known as rolling-up data, data generlization allows for a broader look at the information. By removing specific attributes from view by layering data in a specialized database, businesses can get a zoomed-out view of trends and patterns, seeing the big picture without the distraction of inconsequential or private details.
Data pre-processing often involves collecting information from a number of different sources. Data integration allows businesses to correlate and combine those figures into a single cohesive database. Merging data is accomplished with either a tight-coupling or loose-coupling methodology, based on the business’s data access needs.
Also called “curve fitting” or “low pass filtering,” these types of data transformation is meant to search for trends in a set of noisy data. Information databases hardly ever provide analysts with a tidy bell curve. Smoothing makes the data set look much cleaner, making it easier to see what the information is trying to say.
Computers read structured data in very specific ways. Using data manipulation, this information can be organized in a manner that is much easier for the end user to deal with. Interaction with data in this way requires using specific programming languages including DML and SQL to ensure data isn’t compromised while it is being sorted.
The main purpose of data normalization is to convert source data into one specific form. Information from different software sources may need to all work within a sing database, and using these types of data transformation will prevent as well as eliminate duplicate data that can cause errors in analysis.
Getting Traction On Transformation
When you are trying to look at your data as a whole, it can be difficult to know how to transform it into information your business can actually use. Most sets of data need some form of manipulation before they can provide reliable analysis, usually requiring several different types of transformation in the process. Preserving the integrity of data is key to ensuring it retains its functionality.