A Closer Look at Data Preparation
Data Preparation, Data Prep or Data Wrangling, refers to the process of collecting, combining, structuring, and organizing data for it to be used in business analytics, Business Intelligence (BI), and other data visualization applications. The process includes putting together data from external and internal sources and pre-processing it before final profiling and cleansing, validation, and transformation for use. It is a lengthy undertaking and an important step to put data in context and get result-oriented insights.
The purpose of Data Preparation is to curate and make ready, raw, and unprocessed data to make it consistent with the objectives of the business. This is to ensure that all results of data, analytics, and allied applications are valid for the defined applications.
Why the need for data Preparation at all?
Data sourced from a plethora of platforms comes with errors, inaccuracies, and missing values. This often appears as independent information without a set format or consistency. Here begins the process of Data Preparation, calling for corrections in data errors, and verifying it qualitatively followed by mapping it on the analytics canvas for concrete action.
Finding relevant data that can be used for analytics and applications, to deliver the exact information that the business is looking for is critical to Data Prep. Creating new data fields from internal and external sources, attending to imbalanced data, and eliminating outlier values can all help make data more informative and useful. Data Wrangling helps avoid data duplication and identify and fix issues with data that usually escape detection.
Its impact on businesses
Well-managed Data Prep helps businesses and organizations use data effectively for analytics to initiate positive qualitative and quantitative effects on revenues. The prep process facilitates more informed business decisions with more relevant data presentation that can further augment ROI.
Data preparation is the first step to addressing a business problem through data analytics. The formatted data can subsequently be used to develop, measure, and train a Machine Learning (ML) model or enable AI initiatives. After all, only high-quality data and its ensuing BI solutions, along with other analytics can ensure better operational efficiency!
Without Data Preparation, even the best data emerging from your in-house systems could remain underutilized or short of value. Most enterprise data need re-formatting, transforming and blending for actionable analytics, modeling, and reporting. Lack of proper data preparation could result in analytics solutions using junk data leading to calibration issues or discrepancies among datasets. Erroneous data use will invariably result in incorrect reporting and analysis with catastrophic business consequences!
Working through a complex process!
Data Preparation Steps
Needless to say, the process is not just complicated, but also a recurring one that is time-consuming. Given the highly competitive market, businesses can barely afford to lose either time or money and they definitely can’t wait to realize the true value of data.
That’s where IBM Watson Studio is playing a significant role as a data refining tool to clean and transform business data, readying it for tangible data analytics.
IBM Watson Studio in Data Preparation
IBM Watson is much more than just another tool. Its embedded cognitive technology processes information like a thinking human rather than a mere machine. This powerful solution is capable of unlocking the gigantic world of unstructured data surrounding your business and fully distinguishing between the different kinds of data. As it culls relevant data from distinct sources it continues to create hypotheses and test them to be able to narrow in on the most consistent outcomes. The platform’s cognitive-assisted machine learning uses natural language processing (NLP) to sort information, and make context-specific decisions, just as a human would do!
In fact, it’s much more than that! The studio was created with analytics reports in mind. Data scientists and analysts can interact with the reports by
- Building models
- Uncovering insights from unstructured data
- Visualize all your data in exportable charts
The desktop version of the IBM Watson Studio lets you use the drag-and-drop tools to affect all of its operations.
You need to organize all your data resources for analysis in a project with its own directory.
- Data assets to link to data files on your computer, or create a connection to access data from a remote data source;
- Data Refinery flows to refine your data by cleansing, reshaping, and enriching it;
- SPSS Modeler flows lets you build, train, and test models;
- Notebooks to understand your data and form business insights to share with collaborators;
- AutoAI Experiments help you choose model types, apply algorithms, and optimize models;
- Deployment spaces let you configure and deploy your models;
IBM Watson Studio is a potent technology for data preparation and is revolutionizing industries from e-commerce to retail, finance to real estate, education to health, Watson is democratizing data with its combined dynamic learning capabilities. The platform enables the self-service of converting raw and disparate data into a clean and consistent form, ready to be used for AI and Data Science-enabled business solutions.
Summing it up
Data preparation is the first in the complex stages of big data organization for effective business results. It begins with putting together data from myriad sources, analyzing and sorting it so that it becomes useful for business analytics. The complex and lengthy task of data preparation is important to weed out irrelevant and erroneous data that can hinder potential and future business insights. The “prepared” data patterns can be used for business analytics or business intelligence and Artificial Intelligence models to facilitate operational efficacy and increase ROI.
Connect with us today to see how IBM Watson Studio is playing a disruptive role in analyzing data using its cognitive Artificial Intelligence!