Data Preparation can save millions by cleaning enterprise-level data sets.

What is Data Preparation?

Similar to any other kind of preparation, data preparation is the essential activity of cleaning raw data. This is a value-adding step before any kind of data processing and data analysis. Data preparation enriches the data but it is no doubt a lengthy and demanding task. It demands skilled experts, data management, and data quality management.

For example, data preparation typically involves ensuring uniform formats, transferrable values, and removal of redundant records. 

Why is it Important?

Efficient data preparations can ensure that all the functions being performed based on the data are accurate and to the point. It can make an enterprise save millions by saving efforts, space, and processing power. It can ensure that efforts are in the right direction (on the right data) thus ensuring data quality and improving the business intelligence abilities of a firm.

Good data preparation allows for efficient analysis, limits errors and inaccuracies that can occur to data during processing, and makes all processed data more accessible to users. It’s also gotten easier with new tools that enable any user to cleanse and qualify data on their own.

In the past, it was considered a low-level task but ever since companies have adopted a data-driven structure, it has become a critical step to make sure companies are on the path of delivering accurate and revolutionary services.


Remember !

Data Preparation is important as your model will only yield good results if it will be fed good data. If haphazard data will be fed to a system, a system will generate useless results.

10 Main Data Preparation Activities/Steps

To build valuable insights, data preparation analysis involves a wide range of activities. Each activity has its unique purpose that is reflected in the robust abilities of the end-product. Data preparation has the following main activities:

  • Collection: To collect the entire data.
  • Exploration: To understand what the data is about.
  • Profiling: To find patterns and meaning in data values.
  • Structurization: To decide the required columns, rows, and flattening into a usable manner.
  • Cleaning: To remove unnecessary items such as null values, repeated values, baseless values.
  • Transformation: To make data into usable information.
  • Enrichment: To join the data using intersections and unions.
  • Shaping: To optimize the data for the use of the data analysts by using ETL techniques.
  • Validation: To perform test runs on data to check accuracy, completeness, and consistency.
  • Publishing: To store in the desired warehouse where it is ready to be used. This is the information that BI tools will use to make profitable decisions.

Data preparation involves strategic steps that ensure that data is transformed into its most usable form. These actions are performed by data engineers, ETL experts, and senior data professionals.

Benefits of Data Preparation

Business intelligence relies on well-prepared data and robust data warehouses. Data preparation saves time, energy, space, and eventually a lot of money. It also helps generate better revenues.

A well-prepared data set enables the following:

  • Ability to use data science and machine learning models.
  • Ability to use business intelligence techniques.
  • Ability to be used for data analysis.
  • Ability to optimize data and data usage.
  • Ability to fix data anomalies quickly.
  • Better ROI for the firms.
saves time
saves energy
save space
save money

Challenges

These are the 5 most notable challenges of data preparation:

  • It is time-consuming.
  • Requires technical experts.
  • Ignores current data problems.
  • Keeps data engineers busy and overburdens them.
  • Sometimes potential patterns are lost in cleaning.

Self-Service Data Preparation Tools

There are automated tools that may help data workers manage huge amounts of data for the task of data preparation. New softwares allow non-technical users such as sales managers, business managers, etc. to manage data. This ensures that everyone at the firm can use data to gather useful insights.

Since data preparation requires intensive human power, mental power, and resources, the market must provide companies with automated tools to perform these tasks. This is backed by the fact that recent research showed that companies spend 80 percent of the time preparing the data and only 20 percent remains for the actual analysis.
BiG EVAL aims to facilitate its clients with data preparation consultation and automation, according to their individual needs.

A successful self-serving tool is that which requires less level of expertise, is quick, and also works rapidly. A good self-serving data preparation tool has the following traits ideally:

  • Allows the user to access data from various types of sources.
  • Allows easy data cleaning.
  • Allows easy data enrichment.
  • Allows exporting functions.
  • Allows data visualization.
  • Allows easy profiling.
  • Allows versioning and compatibility.

The Importance of Automating Data Preparation

As the development is going very fast, results also need to be generated at the same pace. Human delays need to be phased out and the ability of rapid processing is the need of the hour. The real-time resolution makes a process worthwhile and if there is a lag then accuracy is not of much use anymore.

In a recent study, it was reported that 76 percent of the workers take data preparation as the most boring task, so it is important to fully automate it now. Automation also rules out the possibility of human error and bias.

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