What is data cleanup and data transformation?
In this post, we’ll look at the phases of data preparation — data profiling, data source research, and data cleaning — in detail.
Data creation and consumption become second nature to people. In 2017, IBM reported that 2.5 quintillion bytes of data were produced worldwide each day. The majority of this data is kept on the internet, making it the world’s largest database. Google, Amazon, Microsoft, and Facebook have 1,200 petabytes (1.2 million terabytes).
However, there are drawbacks to using data. According to MIT Sloan Management review reports, financial losses due to faulty and low-quality data account for 15% to 25% of a company’s revenue. According to an IDC Business Analytical Solutions poll completed in 2018, data processing experts spend 73% of their time preparing data for activities such as analytics and forecasting.
Companies attempt to take advantage of data analytics in order to increase their profits and retain potential consumers by understanding the principles of data cleaning and transformation.
A large number of dirty and unorganized information may be found during the process of web page analysis. Web data integration (WDI) is concerned with data quality and control. Web data integration, similar to Excel, incorporates conversion methods that allow you to straight integrate data in the web application. It allows you to extract, prepare, and combine the information in the same place. This way, you’ll be able to trust and rely on your data.
What should I do before cleaning and converting the data?
Analysts sometimes want to skip over data cleanup in order to move on to data cleaning. The actions outlined below aid in the preparation of raw data for analysis, allowing the analyst to find all data elements (but only ones that he will use in the future).
1. Defining business objectives
The first step in properly converting your data is to understand your company’s goals. Well-defined business activities ensure that the company’s mission is met, that client issues are addressed, and so on. All of these factors aid in determining the necessary and superfluous data for analysis.
2. Data source research
A well-defined data model should include information on potential data sources, such as websites and web pages. A comprehensive study of data sources includes the following:
- Determining the data required for business tasks
- Understanding whether this data will be integrated directly into the application or business processor will be used for analytical research
- Determining what exactly Your colleagues expect to see when collecting web data
- Cataloging possible data sources and managing these data
- Understanding the delivery mechanism and frequency of data updates from the source
It’s also quite possible that the value of web data will increase over time, allowing you to analyze time series and data trends. This way, the decision-making process improves, and you get a greater understanding of how significant events like celebrity endorsements and reviews or a sale impact your business.
3. Data profiling
This is the stage at which you become acquainted with the data and prepare it for transformation. Profiling discovers information structure, missing records, undesirable data, and potential quality concerns. A complete examination of the data may assist in deciding whether a certain source is suitable for further conversion, any issues with data quality, and how many conversions are necessary to perform analytics.
The process of defining business operations, obtaining data, researching the data source, and searching and profiling sources is critical in filtering out data sources. All of these processes will aid in the organization of the processing work and make this information useful. The cleanup of data is the next stage.
After assessing and profiling the sources, you may begin cleaning up the data. From a database management standpoint, all applications for cleaning, transformation, analysis, and data discovery should be looked at from the perspective of Internet data. We view a website as a data source, and we use terminology from this perspective rather than the typical ETL (Extract, Transform, Load) approach to corporate data management.
General recommendations for data cleaning may include (but are not limited to) steps:
What is a data quality assurance plan? A data quality assurance plan might include discussing issues such as “what is our data extraction standards” and “what capabilities do we have to automate the data pipeline.” “which data elements are key for subsequent products and processes,” “who is responsible for data quality assurance,” nand “how do we determine accuracy,
Exchanging data with other sources. Checking the validity of the information. One method to verify accuracy is to use checks and balances to ensure that data is entered correctly at the collection point, for example, if a website has changed and no longer offers value for your company, or if because of a special offer.
Duplication is a necessary evil. There isn’t a single data source that is ideal, and systems may produce duplicate strings from time to time. It’s worth noting that each record has its own “natural key,” which is either a field or fields used to uniquely identify each row. If a new record that shares the same natural key as one or more old records is inserted, all subsequent rows can be deleted.
If empty values are represented as “N/A,” “Null,” or other similar designations, employees will have no idea which values to select and agree on. You may also choose and accept a single value for such a field so that there is no confusion when the data is used by employees. This method involves using filled cells in a column to make an educated guess about missing values, for example, finding the average value for filled cells and assigning it to empty cells.
Moving fields. If the data fields of the source data are in YYYY/MM/DD format, and you need them to be in MM-DD-YYYY, alter them to match your structure.
Checking the threshold level. This is a more sophisticated method of data cleanup that compares the current set with prior values and the number of records. Let’s assume we have hard data on hand that specifies that the worldwide average monthly application quota is 2 million rubles, with each person receiving 100 thousand rubles. If the new data source generates a monthly income of 10 million rubles and 500 thousand rubles per person, these amounts exceed the typically expected threshold level. As a result, this data must be verified further.
Pre-cleaning data improves the accuracy and consistency of data throughout subsequent processes and analytics, which leads to increased client confidence in that data.
Data Transformation / Data Manipulation
Data transformation / Data manipulation (from the English “data wrangling”, “data munging”) is the practice of converting raw data into a regular model for a specific business task for subsequent work on them.
The following are some of the most common data conversion methods:
Start with a modest sample of data for the experiment. Working with big data, in particular during the early stages of transformation, is one of the challenges.Start with a random sample of data rather than dealing with 500 million rows, so you may analyze them and layout further phases of change. This approach will significantly accelerate the research of the data and lay the groundwork for additional operations.
Create a data category for each column. Create unique names for your columns, and use them to label. Columns and data types must be identified in this stage. Make sure the data values stored in the column are actually what you believe they should be. A column called “date_of_birth” should, for example, be formatted as DD/MM/YYYY. Using this method with the profiling described above can assist the analyst in better understanding the data.
The source data is visualized. Using standard graphical tools and visualization approaches may help bring your current raw data to light. Histograms indicate the distribution, scatter plots assist in identifying outliers, pie charts display the proportion of the entire population, and line charts can show long-term trends in key areas. A visual representation of data is a fantastic approach to educating non-technical workers about research conclusions and required adjustments.
It is important not to overthink things. Simply focus on the most essential data elements. At this point, well-articulated company goals come in handy. Because many of the source datasets have far more columns than are necessary for your work, it’s critical to work only with those columns that are truly required for your tasks.
Turn data into actionable information. The procedures described above are concerned with editing, mathematical operations, and formatting the original web data into a format suitable for business. An expert evaluator can turn the data into useful practical information that may be used by the company to grow.
The quantity of data, the sort and effectiveness of that data, and the fact that it is readily available today provide businesses a lot of possibilities to enhance their earnings, market share, competitive advantages, and customer interactions. Traditional Internet parsing may not be adequate, however. Inadequate attention to data cleaning or quality is accompanied by bad data, incorrect judgments, and the loss of faith in them. As a result, the value of old-fashioned Internet parsing in this context remains marginal.
This is where you’ll need to utilize the web data integration function. Web data integration allows you to use data to its full potential through well-designed, rigorous, and consistent data cleaning and processing. You can trust the data and make it available to the right people at the right time if you invest in the proper tools.