Data Preparation

What is data preparation?

Data preparation is the complete step-by-step process of gathering, combining, structuring and organizing data to be used in business intelligence (BI), analytics and data visualization models. The entity of data preparation contains data preprocessing, profiling, cleansing, validation and modification; it is also typical to pull together data from both internal systems and external sources.

Data preparation work is typically performed by information technology (IT), BI and data management teams as they set up data sets to then stow at a data warehouse, NoSQL database or data lake repository until they can be used to develop new analytical solutions. Additionally, data scientists, data engineers, other data analysts and business users use self-deployed data preparation tools to gather and organise data themselves.

Data preparation is commonly referred to as data prep. It’s also called data wrangling, although some analysts use that term in a precise sense to refer to filtering, sorting and arranging data.

The data preparation process is also carried out extensively on raw data before processing and analysis. It is a pre-processing step and often sustains reformatting data, making corrections to data and combining data sets to enrich data attributes.

Data preparation is typically a prolonged undertaking for data experts or business users, but it is essential as a prerequisite to set data elements in context to transform them into insights and eliminate bias caused by inferior data quality.


Why Data preparation is needed?

One of the immediate goals of data preparation is to enhance raw data to be readied for processing and analysis. The data being used needs to be accurate and consistent so the derivatives of BI and analytics applications will be without any bias. Data is often seen to have missing values, inaccuracies or other errors, and separately gathered data sets often have dissimilar formats that need to be translated when they’re combined. Updating data formats, analyzing data quality and compressing data sets make up major portions of data preparation projects.

Data preparation also involves discovering relevant data and comparing information to guarantee applications deliver actionable insights and impactful facts for business decision-making. The data also are enhanced and optimized to make them significantly more informative and useful — for example, by combining internal and external data sets, developing unique data fields, removing outlier values and assessing imbalanced data sets that could aggravate analytics results.

In addition, BI and data management teams use the data preparation process to index data sets for business users to analyze. Doing so enables streamlining and guiding self-deployed BI applications for business analysts, executives and workers.


What are the benefits of data preparation?

Data scientists often gripe that most of their time is spent gathering, cleansing and structuring data rather than analyzing it. A large advantage of an effective data preparation process is that data handlers can focus more on data mining and data analysis – the significant part of their job that generates business value. For example, data preparation can be worked on more quickly, and prepared data can autonomously be inputted into analytical applications for recurring projects and homogeneous data.

Done correctly, data preparation also supports an organization in the following:

  • guarantee the data used in analytics applications provides accurate results;
  • determinate and fix data issues that otherwise might go unchecked;
  • enable better decision-making by business executives and operational workers with informed insights;
  • decrease data management and analytics expenses;
  • avoid repetitions in preparing data for inputting in multiple applications;
  • get an increased ROI from BI and analytics projects.

Effective data preparation is specifically advantageous in big data backgrounds that operate on a combination of structured, semistructured and unstructured data, usually in its basic state until needed for analytics uses. Those uses incorporate predictive analytics, machine learning (ML) and other forms of intelligent analytics that generally involve considerable quantities of data to prepare.


Steps in the data preparation process

Data preparation is performed in a series of steps. There are minor variations in the data preparation steps listed by various data professionals and software vendors, but the procedure commonly involves the following tasks:

  1. Data collection. Suitable data is collected from functional systems, data warehouses, data lakes and other data sources. During this phase, data scientists, associates of the BI team, other data professionals and end-users who gather data should verify that it fits adequately for the intended analytics applications.
  2. Data discovery and profiling. The next stage is to examine the collected data to better break down what it contains and what action is needed to prepare it for the intended uses. To assist with that, data profiling identifies patterns, relationships and other qualities in the data, as well as discrepancies, anomalies, missing values and other issues so they can be worked upon.
  3. Data cleansing. Next, the determined data errors and issues are fixed to sort complete and accurate data sets. For example, as part of cleansing data sets, improper data is removed or fixed, missing values are completed and inconsistent values are harmonized.
  4. Data structuring. At this phase, the data needs to be modelled and organized to adequately match the analytics requirements. For example, data registered in comma-separated values (CSV) files or other file formats need to be converted into tables to make it operable to BI and analytics tools.
  5. Data transformation and enrichment. In addition to being structured, the data type must be manipulated to a unified and usable format. Data enrichment further improves and highlights data attributes as needed, through actions such as augmenting and tweaking data.
  6. Data validation and publishing. In this last step, automated initiatives are applied against the data to properly assess its consistency, completeness and accuracy. The prepared data is then shelved in a data warehouse, a data lake or another repository and either deployed directly by whoever prepared it or made open for other users to access.

Data preparation can also include or provide data curation work that develops and manages ready-to-use data sets for BI and analytics. Data curation involves processes such as indexing, cataloguing and maintaining data sets and their relative objects to allow users to locate and access the data.

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