Getting to know DATA PREPERATION TOOLS

Have you ever been presented with data and wondered “How do I deal with this massive amount of information? What is the best way to sort it so that it makes sense?” It would take several days or months to manually comb through all of it. After all, digitization with its extensive list of perks also brings along the creation of a considerable amount of data. With actions of users and detailed tasks in business operations being recorded continuously, the data collected becomes unmanageable.

However, the point of collecting all this data is for organizations to have the information they need to perfect business practices and strategies. To be able to turn all this data into manageable lots, it has to be organized, reconciled and structured.

Data Preparation Tools are the solution to all data processing challenges. Initially presented as a solution for data analytics automation, data preparation tools or data wrangling tools now have data integration capabilities that allow its use to expand to data handling and data management in production as well as business awareness.

“Data Preparation Tools are software applications that enable improving, correcting, structuring, transforming, and blending of data to prepare it for analysis. They carry out complex classification and compartmentalization processes to form logically and physically sound data models.”

Data Preparation tools incorporate artificial intelligence, machine learning and mathematical algorithms to manipulate, visualize and model data. The intention of using digital tools to prepare or pre-process data is to ensure that the conclusions drawn from processing and analyses are not compromised by the poor quality of the foundation – the raw data. Consequently, the primary goal of data preparation is to improve and enrich the raw data that will further be analyzed. The elimination of blanks, errors, contradictions and incongruencies from data enables data blending and highlights valuable information.

It is important to note that humans are also capable of performing these tasks. In fact, data preparation tools are designed to mimic human actions. The processes that are executed by the tool are like what a human employee would do. However, data preparation tools can perform these much faster and more efficiently.

Collection

Like any other technology, for the data preparation tool to run, the data that needs to be processed must be collected. Once the sources from which data can be collected are provided, the software gathers all the data into a single space. This is done from internal and external sources – business operations data, data fetched by specific departments, interdepartmental communications, shared resources as well as third party information gathered through APIs.

Exploration and Cleaning

In this step, the data is broken down and probed for inaccuracies, inconsistencies, errors, and patterns. Once these are found, the missing data is located and filled in. Inconsistencies are corrected and the data is organized categorically to fit the needs of BI, analytics, and data science tools.

Metadata management

The basic cleaning operation allows the tool to easily access the metadata and source information. Metadata often supplies the context that is crucial to understanding the analytical results. Referencing metadata can provide pivotal points for data management, auditing, BI and operations management.

Transformation

Once the data is collected, it needs to be formatted. Since data is collected from countless sources, disparities in format are unavoidable. Transforming all the data to a unified format makes it easier for other data management and analytics tools to draw inferences that turn into insight.

Verification

Even automated, highly accurate tools are bound to make errors every once in a while. Therefore, running through all the changes made to the data set and verifying if the result is correct is important.

Data preparation or pre-processing is a step that is often skipped, but it is extremely crucial to generating meaningful results. Low-quality input can only lead to mediocre results. Therefore, optimizing data even before it is processed can ensure that its analysis will produce substantial, enriched, actionable results. The emergence of data management tools has made the troubles of data scientists, analysts and even non-technical users vanish. With the surge in data generation, the need for scalable solutions has grown rapidly. Data preparation tools have data integration and data storage capabilities that make them easily expandable. They also have graphical user interfaces that makes them much easier to operate for users who are not analysts or data scientists. Smaller organizations that do not have data science departments can also use these tools to catalog, unify and transform their data and make informed business decisions.