What is Data Transformation?

Explain Data Transformation

Data transformation is the process of modifying the data format so that systems to use in the decision-making process and human analysis of the data structure is simpler. Usually, transformation results from data extraction or loading (ETL/ELT).

Sometimes data transformation can be used prior to storage or presentation. Cloud migration, business process integration, and many modernizing solutions using corporate data all depend on this fundamental component. Within the framework of this concept of data transformation, the process usually entails data cleansing or merging such that it is in a format suitable for analysis using either human knowledge or software.

Transformations usually consist of turning your unprocessed data into one that has been vetted and cleaned so that systems or humans may use it. Data transformation is hence absolutely essential for data management, data integration, data transfer, data wrangling, and data warehousing systems. Transformational data can be:

• Constructive: data copies, adds, or replays.
• Destructive: records and fields are erased.
• Aesthetic: data is homogenized to satisfy standards.
• Structural: renaming, merging, or shifting columns reorders data.

Data Transformation Entails :

• Turning information into a format an application would find simpler for usage or understanding.
• Cleaning data to enable or simplify interpretation.
• Organizing information so data scientists may apply it in combinations.

All of these projects make data transformation a crucial part of a company’s expansion plan, particularly if your next development calls for data-powered insights or breakthroughs.

The Function of Data Transformation within Data Management

Data transformation provides your data management systems with more practically relevant data.

For example, suppose a shipping corporation has two facilities, one on the East and another on the West Coast of the United States. Higher level management needs to equip the decision-makers at every site with the tools required for performance evaluation. All the data is merged, though, and it is impossible to determine which products were supplied from which source.

By use of data transformation, one can design a system that detects shipments based on the fulfillment center from which they originated. This data can then be automatically kept in a data lake utilized by the ERPs of both sites. Now when decision-makers open their ERPs, they only find data related to their plant.

Data transformation can thus help to convert messy, difficult-to-use data into a tool for process improvement.

Data Change in Use

For a basic illustration, let’s say you work for a manufacturer of auto components and have a spreadsheet showing the variety of units sold over a year. You want to know when and which kinds of products sold most. However, if the data is jumbled, one cannot quickly determine which kind of products sold.

You could classify the goods into groups like “Drive train,” “Steering,” “Suspension,” “Wheels and tires,” etc.

These days, you create various opportunities:

• For every category, you may rapidly total the sales figures.
• At first glance, you can tell which kinds of products sold more and when.
• Charts for every type of item you sold can be created.
• Pulling sales data from another spreadsheet or an enterprise resources planning (ERP) system allows you to combine it with another system, like an inventory management tool.

Converting, organizing, and cleansing your data can all be time-consuming and prone to mistakes. Searching hundreds of lines of code to fix a problem can drive your project past its deadline and postpone dependent procedures.

Process of Data Transference

Extraction of data from a particular source, conversion, and subsequently delivery of the converted data to a destination constitute the data transformation process. You draw data into a repository from one or more sources during the extraction step. By now the data is still raw and useless for the procedures or individuals depending on it. You have to follow a set of procedures to make it useable; some of these can include data cleansing before transformation. This can remove fields from your working data sets with missing values or inconsistent data sets.

Information Finding

Analyzers examine a dataset and determine through the data discovery process:

• The information they wish to leverage.
• Overcomes obstacles required for the change process.
• Which systems or individuals will gain from this data and how might it be best transformed for their use?
• Details regarding the data that ought to be taken into account, including which applications or persons acquired it, when, and any other relevant information.

Once the data discovery process is over, the team knows how to exploit it and what has to be done to change it.

Data Mapping

Data mapping goes into great length to explain the transformation processes. It comprises a map showing the sources of the data as well as its directions. Additionally planned at this phase are the sort of transformation and the present structure. You also create business rules instructing the system on the management of specific types of data (updated, joined, aggregated, etc.).

Schema design, which determines how the converted data will be arranged, closes the data mapping process. This technique gives data usage by individuals or programs first priority.

Generation of Codes

After completing data discovery and mapping, you know exactly what has to be done. Writing or gathering the code required to finish the task comes next. These are some of the more typical tasks analysts complete using code:

• Operations: Code execution lets the data be turned into the desired structure. Following intended transformation during the mapping stage—aggregation, format conversion, and merging—the data is then entered into the target system, which can be a data warehouse or dataset.

You run the scripts you have created to execute the data changes described in your mapping documentation during this phase. You would also fix your code during this step should flaws cause improper execution of the code.

Some forms of change consist of:

• Filtration: Data filtration is choosing which columns of data to load throughout the conversion process.
• Boosting: Usually, this entails gathering already available data and enhancing its value. A column including full names, for instance, can have its data split into three independent columns: first name, middle name, and last name.
• Combining and Splitting Columns: One column can divide out into two or more varieties of data. One can divide a column headed “State,” for example, into three independent ones: Eastern, Midwestern, and Western. You might also perform the reverse, combining several columns into one.
• Combining Information from Many Sources: This can entail compiling a single, cohesive dataset from two or more separate spreadsheets or business processes.
• Eliminating Redundant Data: Eliminating duplicate data helps you to organize your data and simplify working with both people and systems.

Check Over

You assess the data you have changed to ensure the conversion was effective and fits your objectives during the review stage. Should you not get the desired results, the evaluation procedure also entails making corrections.

Transforms Data Benefits

Especially since it turns sometimes useless data into insight gold, data transformation can enable success from many different directions.

Enhanced Data Quality

Particularly for advanced business intelligence or analytics, data transformation helps you greatly enhance the quality and usability of your data.

Your customer relationship management (CRM) system, for instance, might automatically populate depending on what users enter on an online form. Some people, meanwhile, register more than once using several email addresses. This could cause an inaccurate assessment of your system’s lead or customer count.

Data transformation allows you to clean this data, removing duplicates, therefore determining exactly the number of registered users.

Lowered Mistakes

Human mistake can render otherwise useful data confusing or even misleading. Data transformation lets you fast fix current errors. In fact, human mistake cannot affect data-based systems.

Imagine, for example, a sales team entering each sale into a form that subsequently provides that data to the finance division. Create a data transform rule whereby the salesperson receives an alert when an item they sell exceeds 125% of the sale price of the unit. This might stop users inputting figures adding an extra zero.

Efficient Management and Data Organization

Those who need it know where it is and how to utilize it since transformation provides you with a structure for data organization.

Going back to the sales and finance example above, assume the finance team has to know the typical monthly sales statistics so they can determine which interest rates are reasonable while they negotiate interest rates with a bank. Data transformation allows them to have this information immediately in their system and, with a few clicks, evaluate the monthly sales volume of the business.

Enhanced Compatibility for Application Use

Many applications may ingest and analyze data, which facilitates data inclusion into other systems or business-critical decision making. Businesses might, for instance, leverage IBM i data sources in an effort at integration. The team can convert the generated data inside IBM i into a compatible format such that an outside program can use it. This refutes one of the most often held beliefs regarding IBM i: that it stunts creativity.

Data transformation also guarantees compatibility between databases, applications, and systems that must access the data.

For example, a hospital may utilize an optical character recognition (OCR) technology to read prescription handwriting and convert it into data that software could handle. Instead of personally entering the data, a human might simply have to rapidly check how the software views each scanned image to ensure it’s accurate.

Accelerated Data Handling

Because you don’t have to physically go over reams of data before using it, transformation speeds data processing. Using real-time data, this allows one to construct systems analyzing performance, manufacturing rates, and other useful parameters as well as automate corporate choices.

Correct Understanding and Forecasts

Much of the data you handle is meaningless without data transformations. Duplicates can invalidate big sets of data, as in the CRM example above. But you can also have valuable data right at your hands that, with development, can provide forecasts and insights supporting income generation.

Assume, for example, that a retailer has a web app handling consumer reward points. Purchase data mixed with geolocation data can help them identify which goods are more often used in certain geographical areas. After that, the business can forecast sales quantities or even change its offers tailored for particular areas.

Methods for Data Transformation

The method you apply for data transformation will vary depending on how you intend to use your data. Still, one or more of these approaches usually help analysts and the companies they work for.

Data Smoothing

Data smoothing removes anomalies and outliers that could distort your analysis. Moving averages let you, for example, find the average of nearby data points. This produces a more seamless curve with less extreme peaks or valleys.

Construction Attribute

Attribute construction is adding fresh qualities or attributes from ones that already exist. Assume, for instance, that a manufacturer is working with a dataset and two of the fields are “Money-Paid” and “Products-Assembled.” You might then create a new attribute named “Salary_ROI,” with a formula like this:

SalaryROI = Money Paid / Products Built

Generalizing Data

Low-level features in data generalization are turned into high-level properties. By means of a more wide perspective on the insights or trends your data offers, you can develop a more extensive classification of it. Assume, for instance, that your company’s personnel’s ages make up a dataset.

Original data includes 19, 25, 36, 58, 42, 32, 48, 43, 18, 55, 51.

Your generalized data can show like this:

Generations:

• 18 to 20 (2)
• Twenty to thirty (1)
• Between thirty and 39 (2)
• Forty to forty-nine (3)
• Fifty to fifty-nine (3)

Data Combine

Data aggregation is grouping data into higher-order categories. You may split demographic data, for instance, based on client age or city of residence. With this type of aggregation, you can focus on how you might better satisfy the demands of particular groups of people or on what is working for them.

Data Discretizing

Using data discretization separates continuous data into bins a system can examine separately. You could design a system, for example, whereby data entered into bins of the same size—units of 10,000. One could alternatively arrange the information in equal intervals. You might divide users, for example, into ten-year age groups ranging from 21 to 30, 31 to 40, 41 to 50, and so forth.

Normality of Data

In the context of data transformation for an organization, data normalizing is the process of translating source data into another format in a way that lowers the duplicate data count. Unstructured data and repetitions are deleted during the data normalizing process.

Data normalizing allows you, for a dataset used in a customer relationship management (CRM) system, to eliminate duplicate phone numbers, street addresses, and website URLs. Should one phone number be entered as “617-359-2117” and “6173592117,” for example, the normalizing procedure can show it in one common format, like “(617)359-2117.”

Data Integration

Data integration seeks to combine several sets of data so you may access all at once. Multiple databases can, for instance, automatically transfer data to one central application.

You might also design a system whereby you gather data from hundreds of people into one analytical system.

Data Editing

Data manipulation helps you to alter data based on particular standards. You can create a single table from several datasets, for example, combining their data. You might also create subsets of data based on the criteria you program into the system by means of filters.

Tools for Data Transformations

Although data transformation solutions abound, it’s crucial to choose the correct one for the demands of your company.

Problems with Data Transformation

While building your solution, keep in mind some of the challenges that data transformation brings as well. Several of these consist in:

• The great cost of transformation tools and professional knowledge.
• With the intensity of some on-site transformation projects having the ability to slow down other operations, data transformation can consume notable computational resources.
• Given data specialists are among the most sought-after employees in the present corporate environment, it can be challenging to find and keep the qualified experts needed for this position.
• Correctly matching data transformation initiatives to the data-related priorities and needs of the company can prove challenging.

Does Data Transformation Help Businesses ?  

Data transformation is necessary for businesses since they generate daily massive amounts of data. However, until it is in a format they can apply, they cannot use it to create insights or assist company development. Several of the most convincing arguments for data transformation consist in:

• It promotes harmony across several pieces of data in several formats.
• Because it converts source data into a format the target destination can use, transformation facilitates data migration.
• Whether organized or unstructured, data consolidation aids in both organization.
• Transforming data can improve its quality, therefore facilitating the use of it to produce insights.

Actual Case Study of Data Transformation

Data transformation occurs constantly in the corporate world, even although many companies retain their data transformation figures as company secrets. Here are a few instances of data transformations:

• UPS turned standard GPS data into knowledge they might utilize to find more effective paths using data from the travel paths of its vehicles.
• T-Mobile teamed with Google Cloud to gather then convert gigabytes of consumer data to measure sentiment. The behemoth in communications was able to interact with consumers more quickly and also leverage data to teach artificial intelligence-powered dialogue.
• Using “Validation” first then “Enrichment,” Netflix changes data on cybersecurity events. This helps the streaming leader create a risk score that professionals could examine and apply to stop threats.

Conclusion

Data transformation helps companies to convert vast amounts of data into insights with use. Data discovery, mapping, code generation—then code execution—are all part of the transformation process. Particularly if you have to manually combine all of these components, designing a transformation process might be labor-intensive.