Big data has become huge over the last decade and businesses around the globe are striving to find new ways to gain insights from this data. The sheer volume of data that businesses manage is increasing every year and can be somewhat of a struggle to properly organize. In an effort to make data more accessible across the board and increase the reliability of data, businesses are turning to data-driven solutions, primarily data transformation. Data transformation is an essential part of any company’s data management plan, and creates data visibility, deeper insight into customer behavior, and empowers business leaders. This isn’t an easy task, so we’ve highlighted six types of data transformation everyone should know below.
What is Data Transformation?
Data transformation is the process of converting data from one format to another in order to make it more usable. This complex process is a vital part of data management and data integration for businesses. Data transformation allows businesses to integrate, cleanse, store, analyze, and mine data for actionable insights, better-informed decisions, and an increase in the reliability of data. There’s a wide array of techniques, tools, and resources used to properly execute data transformation. The process can be done manually, automated, or using a combination of the two methods. Some benefits of data transformation include increased accessibility, faster data processing, enhanced data quality, and greater application compatibility.
Data Normalization
Data normalization is the process of converting source data into another format for effective processing, improved algorithm efficiency, and optimal data extraction performance. The main objective of data normalization is to minimize and/or exclude duplicated data, and scale data to a much smaller range. The main three methods companies use to achieve this include decimal scaling, min-max normalization, and Z-score normalization.
Data Generalization
Data generalization is the process of taking low level data attributes and transforming them into high level attributes by using the concept of hierarchy. This is also done by generating successive layers of summary data in a database, which creates a comprehensive view of a problem. Teams usually apply this to categorical data set sets that have a finite, but large number of distinct values. Data generalization helps with Online Analytical Processing (OLAP), which generates quick responses to a wide array of analytical queries. Companies also use this process when it comes to the implementation of Online Transaction Processing (OLTP), a class system that both facilitates and manages transaction-oriented applications.
Data Aggregation
Data aggregation is the process of gathering raw data from multiple sources and presenting the data in a summary form, which is used for statistical analysis. The two main types of data aggregation are time aggregation and spatial aggregation. Businesses usually aggregate their data in a specific time period, which provides statistics such as sum, average, minimum and maximum. This data is written as a report that can be used to gain valuable insights into certain resources within a business and increase the reliability of data.
Data Manipulation
Data manipulation is the process of changing or altering raw data sets to make it more readable and organized. This helps businesses identify patterns in data and transform it so business leaders can gather insights into customer behavior and different sorts of data. Data manipulation converts both structured and unstructured data, so it can be utilized by machine learning algorithms.
Data Smoothing
Data smoothing is a technique that removes noisy, meaningless, or distorted data from a data set. Getting rid of this noisy data allows businesses to pinpoint trends and better insights pertaining to stocks, the economy, and customer sentiment. There are three primary types of algorithms that help accomplish this task, including clustering, binning, and regression. Clustering is the process of grouping similar values together, which form clusters and anything else is considered an outlier. Binning helps you smooth data values in each bin and split the data into bins. Regression algorithms help you predict an attribute based on the value of another and identify relationships between two dependent attributes.
Data Discretization
Data discretization is the process of transforming continuous data attribute values into a set of data intervals. There’s a variety of discretization methods including an equal-frequency, equal width, and Minimum Description Length (MDLP). Another method utilizes decision tree algorithms to transform data sets into categorical data. This type of data transformation improves the efficiency of applied algorithms, makes the data easier to analyze, and increases the reliability of data.
Conclusion Data transformation seems like a relatively simple process, especially since it’s converting data from one format to another. Still, this is a somewhat sophisticated process that requires algorithms, data engineers, and specific tools to accomplish this task. Businesses can use a variety of data transformation techniques including data normalization, data generalization, and data aggregation. Data manipulation, data smoothing, and data discretization are three other popular techniques. All of these methods improves the reliability of data, increase accessibility, and sets businesses up for success.
