Data preparation is a crucial step in data science and data analytics.
Data preparation is the process of cleaning and transforming raw data before analyzing and processing it further. It involves multiple steps such as gathering, assessing, cleaning, transforming, reformatting, correcting, filtering and combining data. Improving the data quality will usually lead to improved quality of results (“garbage-in, garbage-out”).
Companies and organizations often generate huge chunks of data daily and would want to be fact-driven when making business decisions, hence the need to analyze data to revamp their day-to-day operations, determine clients’ demands, increase performance, as well as proffer solutions to existing problems.
Data is gathered for different logical reasons; this can be for mining or for artificial intelligence as there is typically a requirement to determine and pull out relevant data for a given analytic purpose. Every task or learning curve has a set demand concerning how a dataset must exist for evaluation and for this reason information should be transformed to accomplish those needs.
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Information preparation entails gathering data from various sources; external and internal sources, cleansing, transforming raw data before processing as well as evaluating. It is an action that usually involves reformatting data, making corrections to data, and combining data sets to enhance data.
Data preparation is a key part of any data science project and is frequently one of the lengthiest parts of the job hence the need to eliminate errors before drawing insights from the data.
What Are The Processes of Data Preparation?
Your data is just as good as its accuracy. Investing 75% of the assigned time on prep work may feel overwhelming. But in fact, most industries report that data preparation steps for data analysis or machine learning consume 70% to 80% of the time spent by data researchers as well as experts.
Data preparation is the procedure of transforming raw data into a tidy collection. This can be done by accumulating, cleaning, and consolidating data which is typically unstructured and unpleasant into a helpful type mostly for use in the analysis.
The figure below shows the steps in a typical data preparation process.
Understanding the problems that need analysis is the very first step before data is gathered. Data collection is the process of gathering various datasets from repositories like SQL, Azure, CSV, Excel, folders, PDF, web, and other resources of data, which are sent to a central location for transformation.
The table above shows the various ways to get data from different sources. This phase is a good time for data experts and Business intelligence employees to take a first look at the data and to determine whether it is a great fit for the application that it is designed for.
Discover and Assess Data
It is pertinent to uncover each dataset. Discovering is all about getting to know the information and recognizing what needs to be done and what data will be useful in a particular context.
The accuracy of the analysis is highly dependent on the quality of data. An incorrect or obsolete data will result in a wrong result. Dataset frequently contains some dirt and needs to be cleansed.
Data cleaning is defined as the process of spotting and correcting (or removing) corrupt or unreliable documents from a table, dataset, or data source. Errors and disparities identified in the initial stage are sorted out in this stage. Power Query is a tool that is often used to carry out this process and this involves recognizing abnormalities that lower data quality, such as duplicates, redundant columns and rows, errors, and null values.
Cleansing also includes data profiling of the dataset to check the quality of the data making use of validation rules that are pre-specified, and after that producing a report/distribution of the quality of the data and errors.
As shown above, in Power Query, for example, profiling helps to identify the column statistics and value distribution of the dataset at a glance before commencing transformation.
Transform and Enrich Data
Data transformation increases the efficiency of analysis. it is the process of upgrading the style or value entries to reach a well-defined result or to make the dataset easily accepted by a bigger target market.
This step basically focuses on formatting, shaping, and structuring the dataset into the desired form.
Transforming can be:
- Date transformation,
- Number transformation,
- Text transformation,
- Conditional formatting,
- Merging tables and appending tables
- Mapping, index creation, and translating into a different format.
- Consolidating dataset by, for example merging columns as shown in figure below or applying a filter to take out unnecessary fields, rows, and columns.
Enhancing data means connecting the data to several other pieces of information that are related for a much deeper insight.
When the data is prepared, it is saved or directed into a third-party application such as a business intelligence tool, database, or machine learning environment for analysis.
Why Data Preparation is Important in Data Science
By preparing data, users can produce far better and more effective models of any type.
There are several advantages of data preparation as follows:
Provides Quality Data
A top benefit of preparing data for analysis is that it provides users with enhanced quality data which in turn results in high-quality results. Data use for business intelligence, predictive analysis, machine learning, and various other sorts of applications require to be in its finest quality to create results that are reliable as well as workable.
The flexibility of data usage
When the data passes through the preparation stage it can be used for several applications.
Preparing data before usage allows for easy identification and correcting data challenges.
You can offer more individuals access to much better top-quality information, which generally makes way for insightful company decisions.
Data Cost Savings
It saves cost and provides a highly efficient way of processing and preparing the dataset for evaluation.
Make better service decisions
Information that is devoid of mistakes and has undertaken cleansing procedures can be assessed more quickly as well as effectively causing much more prompt, reliable, and top-notch service decisions.