What steps comprise the data preparation process?

What steps comprise the data preparation process?

Gathering, collecting, formatting, and organizing data for use in business intelligence (BI), analytics, and data visualization tools is known as data preparation. Processing data, profiling, cleaning, validating, and transforming are all parts of data preparation. Additionally, it might entail merging information from numerous internal systems and outside sources.

The information technology (IT), business intelligence (BI), and data management teams prepare the data as data sets are integrated into a data warehouse, NoSQL database, or data lake repository and as new analytics tools are developed using those data sets. Additionally, self-service data preparation technologies are being used by business users, data scientists, data engineers, and other data analysts more and more.

Data prep is a colloquial term for the process of preparing data. Although some experts use the term “data wrangling” in a more restricted meaning to describe cleaning, organizing, and converting data, such usage separates the data wrestling stage from the data pretreatment stage.

We will examine data preparation in this paper, including what it is, how to perform it, and the advantages it provides to businesses. Additionally, there is information about vendors, best practices, and typical issues with data preparation as well as data preparation tools. Links are also provided to other guides that go into further detail about the topics addressed in this one.

Data preparation: Why it is done and for what purpose

Making sure that raw data is correct and consistent before processing and analysis so that the outcomes of BI and analytics programs will be valid is one of the key uses of data preparation. Since diverse data sets frequently have different formats, it is necessary to reconcile them before combining them. The data is frequently produced with missing numbers, inaccuracies, or other problems. Correcting data problems, confirming data quality, and consolidating data sets are major components of data preparation efforts.

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To ensure that analytics applications deliver useful information and practical insights for business decision-making, data preparation also include the search for pertinent data. Data is frequently improved and optimized to make it much more informative and useful. For instance, merging external and internal data sets, creating new data fields, eliminating outlier values, and dealing with imbalanced data sets that may skew the results are some examples of how data is improved and optimized.

The data preparation procedure is also used by BI and data management teams to provide data sets for business users to analyze. This will facilitate and direct business analysts, employees, and executives who use self-service BI tools.

What advantages does data preparation offer?

Many data scientists are dissatisfied with the amount of time they spend organizing, cleaning, and preparing data for analysis. They and other end users will be able to focus more on data mining and data analysis, the tasks that add value to the company, thanks to an efficient data preparation process. For example, data preparation can be sped up and ready data can be automatically supplied to users for ongoing analytics programs.

If done correctly, data preparation can also assist a company with the following:

Make that the analytics applications’ data produces accurate findings.

Determine and correct data problems that would not be seen otherwise.

Aid operational staff and company executives in making more informed decisions;

Lower the cost of analytics and data management; When developing data to be used in various apps, avoid duplicating your efforts. and maximize the return on investment from BI and analytics projects.

Effective data preparation is especially helpful in big data contexts because it holds a mix of structured, unstructured, and semistructured data, frequently in raw form, until it is required for specialized analytical needs.

These applications utilize advanced analytics such as machine learning (ML), predictive analytics, and other sorts that typically require preparing massive volumes of data.

For instance, Felix Wick, corporate vice president of data science at Blue Yonder, is quoted as noting that data preparation lies at the core of machine learning in a report on preparing data for ML.

The stages involved in the data preparation process.

There are various phases involved in data preparation. The steps in the data preparation process may vary depending on the data professionals and software providers who use them, but they often comprise the following tasks:

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Gathering data.

Operational systems, data lakes, data warehouses, and other data sources are where the pertinent data is gathered. Data scientists, members of the BI team, other data experts, and end users who gather data should confirm that it is appropriate for the purposes of the anticipated analytics applications throughout this stage.

Profiling and data discovery.

The acquired data must next be examined to determine what it contains and what needs to be done to make it suitable for the intended usage. In order to address discrepancies, missing numbers, abnormalities, and other problems, data profiling analyzes patterns, other attributes, and linkages in the data.

The data being cleaned.

Following the discovery of data flaws and issues, complete and accurate data sets are produced. Correcting inaccurate information, filling in blanks, and harmonizing discordant entries are a few examples of how to clean up data sets.

Data structuring.

The data must now be structured and modeled in order to meet the criteria of analytics. For instance, in order to make data available to BI and analytics apps, data stored in CSV files or other file formats must be transformed into tables.

Data transformation and data enlargement

In addition to being structured, the data often needs to be converted into an organized and practical format. For instance, data transformation could entail adding new columns or fields that compile values from already-existing columns. Additionally, data enrichment involves adding and enhancing data to data sets in order to improve and optimize them as necessary.

Both publishing and data validation.

The data is validated for accuracy, completeness, and consistency using automated processes in the last phase. The prepared data is either utilized directly by the person who prepared it or made available to other users after being stored in a data lake, a data warehouse, or similar repository.
Data curation work, which creates and manages ready-to-use data sets for BI and analytics, might incorporate or benefit from the data preparation effort.

In order to make it easier for users to find and access the data, curation of data includes duties like archiving, classifying, and indexing data sets and the metadata that goes with them. The data curator collaborates with data scientists, other users, business analysts, it and data management departments, and has a formal job in some organizations. In other locations, data custodians, database administrators, data engineers, data scientists, and business users themselves can curate the data.

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What is the difficulty in gathering data?

Data preparation is by its very nature difficult. Data quality, consistency, and accuracy challenges are likely to be numerous when combining data from various source systems. In order to make data valuable, it must also be modified and weeded out of the mix.

It’s a lengthy procedure, as was already mentioned: In analytics programs, the 80/20 rule typically applies, with roughly 80% of the labor allegedly going into data preparation and collection and only 20% going into data analysis.

Rick Sherman, managing partner of consulting company Athena IT Solutions, outlined the following 7 obstacles along with suggestions on how to overcome each of them in a study on typical data preparation challenges:

Profiling of the data is insufficient or nonexistent.

Analytics may be flawed if mistakes, irregularities, and other issues cannot be found in the data due to improper data profiling.

Data is lacking or incomplete.

Missing values and other types of incomplete data are present in many data sets. These problems must be taken into account as potential errors and, if so, corrected.

Data values are incorrect.

Misspellings, incorrect figures, and other mistakes are a few examples of invalid entries that need to be fixed in order to increase the analytics’ accuracy.

Keeping the name and address the same

The data from different systems may contain names and addresses that are inconsistent, and these changes may affect how other companies and consumers are viewed.

Data consistency across enterprise platforms is a challenge.

Data preparation is also greatly hampered by other irregularities in data sets received from several source systems, such as unique identifiers and disparate language.

Enhancing the data.

Making decisions on what to add to a data set, for example, is a difficult undertaking that calls for a solid understanding of the business requirements and analytics objectives.
directing and growing the data preparation processes.

On a continuous basis, the task of data preparation turns into a process that must be maintained and enhanced.


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