Explain Data Preparation In Data Science

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Cleaning and converting raw data before processing and analysing is known as data preparation. Before processing, it is a crucial phase that entails data reformatting, data corrections, and the mixing of data sources to enrich data. For data experts or business users, data preparation is a time-consuming task. But it is necessary to place data in context to generate insights and remove bias brought on by irrelevant data. Data preprocessing, cleansing, validation, profiling, transformation, storage are all parts of data preparation. It involves combining data from various internal systems and outside sources.

Effective data preparation facilitates data analysis and reduces errors and inaccuracies that may arise during processing. It increases user accessibility for all processed data. New tools allowing each user to independently cleanse and certify data have made it simpler.

Data Preparation In Data Science

The goal of applying data preparation to raw data is to guarantee the accuracy of the data. Quality output is produced by processing data with business intelligence and other analytical applications. Raw data is full of inaccuracies, errors, and missing numbers. Different formats can duplicate or omit values when several data sets are involved. As a result, the initial phase in data processing entails fixing all of these problems, confirming their accuracy, and unifying data sets.

The Reasons For Preparing Data

One of the main goals of data preparation is ensuring that raw data is correct and consistent before processing and analysis so that the outcomes of BI and analytics applications are valid. When data is created, it includes missing numbers, inaccuracies, or other problems. Moreover, when disparate data sets are merged, they have different formats that need to be reconciled. Large portions of data preparation tasks involve correcting data problems, confirming data quality, and consolidating data sets.

Also Read: 20 Best Data Visualization Tools for 2023

Below are reasons for data preparation:

•    You prepare the miner by prepping the data. Data preparation enables the miner to build better models more quickly when using the pre-processed data.

•    For the creation of any form of an effective model, good data is necessary.

•    Data must be formatted by the necessary software application.

•    The data must be made suitable for the chosen procedure.

•    In the actual world, data is full of unnecessary details.

Benefits of Preparing Data:

•    Errors can be easily fixed by data preparation. These inaccuracies are harder to identify and fix once data is collected from its source.

•    Produce high-quality data by cleaning and reformatting datasets. With data preparation, it is possible to guarantee the high quality of all the data used in analysis.

•    Offers timely, effective, and high-quality business decisions. The data of higher quality can be processed and analysed quickly and effectively.

•    Generate a higher ROI from BI and analytics activities

•    Lower the costs associated with data management and analytics

•    Prevent duplication of work in data preparation for use in different applications.

Data Preparation Steps

Collecting the appropriate data is the first stage in a series of activities that also includes cleaning, labelling, validation, and visualisation.

1. Data Collection

The process of gathering all the data you need for your work is known as collecting data. Data can be found in a variety of places, including on devices, data warehouses, computers, the cloud, and software. It makes data collection time-consuming. It can be difficult to figure out how to connect to multiple data sources. There is a lot of material to search through because data quantities are likewise growing dramatically. Additionally, depending on the source, data comes in a wide range of formats and types. 

2. Data Cleansing

To make sure the data set being used produces reliable results when it is evaluated, it needs to be cleaned. Small data sets can be manually evaluated. But for bigger ones, automation using easily accessible software tools is necessary. 

Manual data preparation is time-consuming, expensive, and prone to error. Analytics are used to make business decisions. However, if the data is unreliable or lacking, your analytics will help organisations make bad judgments. Poor analytics lead to bad business choices.

3. Enrichment And Transformation Of Data

Data transformation involves changing the format or value entries to achieve a specific result or to make the data more reachable to a larger audience. Adding to and connecting data with additional relevant information to deliver deeper insights is referred to as enriching data.

4. Validation And Publication Of Data

Automated procedures are applied to the data to verify its accuracy, consistency, and completeness. Then, the data is used directly by the person who generated it. Also, it can be made accessible to other users after being stored in a data warehouse, a data lake, or a similar repository.

5. Data Storage

When the data is ready, it can be stored or fed into a third-party program. For instance, a business intelligence tool frees it up for processing and analysis.

Problems With Data Preparation

Data preparation is a difficult procedure. Numerous challenges with quality, accuracy, and consistency will arise when data sets are compiled from various source systems. To make the data more user-friendly, all irrelevant information must be removed. It can take a while to complete this. Here are some common challenges faced while preparing data:

•    Data profiling may be insufficient or ineffective. When data is not properly profiled, it can result in many mistakes, oddities, and problems.

•    An organisation may have data that is incomplete or missing if data profiling is not done properly. 

•    Data enrichment is necessary, but choosing what to add to it can be challenging. It requires sound knowledge and business analytics expertise.

•    To standardise the procedure and ensure that it can be used repeatedly, data prep processes must be set up, maintained, and improved.

•    Standardising names and addresses is crucial when merging data sets. These details are saved in numerous systems in various formats. They may influence how people perceive the information if they are not fixed.

•    Invalid values might also be present in data sets. This can happen as a result of misspellings, typos, or incorrectly entered numbers. To maintain analytical accuracy, these incorrect entries need to be identified early and corrected.

Conclusion

Organisations may be confident that their data will be helpful for any process or generating insight with the right data preparation. Any organisation can benefit greatly from the confidence it instils in the procedure, the system, and the result. Above are some data preparation techniques to filter data for your purpose. To learn more, you can join Data Science through the courses offered by The IoT Academy. 

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