Learn Data Transformation Techniques and Fundamentals
Data quality has a direct impact on a business's bottom line: Gartner reports that bad data costs enterprises a whopping $12.9 million each year. But what is "bad data," exactly?
Consider this: most of the data that enterprises generate daily exists in an unstructured, haphazard, and raw form. It must be processed and consolidated before it can go into machine learning models or for any other use.
Data transformation is what converts this raw, mixed data into usable forms by employing several techniques like cleaning, categorizing, etc., thus improving its quality for further use.
In simpler terms, data transformation makes enterprise data more comprehensible for further complex operations that depend on it. It is an ongoing, real-time process that functions best when automated.
Fundamentals of Data Transformation
The raw data an enterprise generates consists of wide differences and redundancies that must first be compiled into comprehensible forms. The first step of data transformation involves taking source data and running it through SQL and Python algorithms for cleaning, aggregation, segmentation, and application of business logic.
It helps to create high-quality datasets that can then be suitably exposed to business intelligence software or machine learning models.
Data transformation makes its significance known in helping maintain data pipelines that are technically clean and streamlined. In fact, according to an iMerit report, 59% of the respondents state that high-quality data is of primary importance when developing or training AI/ML models.
For businesses to be able to provide clear, consistent, concise, and relevant datasets for the machine learning models, data transformation is the cornerstone of data quality enhancement for advanced business intelligence operations.
Techniques for Effective Data Transformation
In addition to improving the quality of data, transformation helps enterprises create enriched datasets with organized complexities that inform their machine-learning systems. The automation process creates consistency and accuracy of data that comes from enterprise data houses every day.
Below is a list of four key data transformation methods with high impact.
1. Data Cleaning and Preprocessing
Data preprocessing involves three basic activities: cleaning the data, transforming it, and then reducing its dimensionality and complexity for relevance.
Cleaning corrects data issues pertaining to irrelevance, duplication, noise, missing values, erroneous values, multi-collinearity, etc., before it can be further processed for transformation.
2. Standardization and Normalization
Transformed data must be rendered into a universally acceptable format that can be accessed, shared, and interoperated across all enterprise systems. The process that converts data into standardized formats so that ERPs can exchange it and communicate freely.
Standardized data must then be normalized to make it analysis-accessible and readily usable for business operations.
3. Encoding and Handling Categorical Data
Unstructured data can have any type – media files, text, audio, etc. In order for machine learning systems to understand this data and process it effectively, it must be converted into binary code.
Encoding also involves data categorization that converts string values into corresponding integers and uses imputation to populate missing values. Some data scientists also leverage one-hot coding to handle categorical data.
4. Feature Engineering for Advanced Analysis
Feature engineering is a part of data transformation that involves further streamlining the transformed data into features that can be fed into machine learning systems.
It helps drastically enhance the relevance of input features and improve and control the prediction capacity of the ML models.
Data Transformation Tools and Libraries
The world generates 328.77 exabytes of data every single day. It is no ordinary feat for an enterprise to collect all its consumers’ data and assess it for usability to generate business insights.
Implementing data transformation requires long-term planning that incorporates a mix of the right transformation tools and resource libraries.
1. ELT Tools
ELT tools perform the operation of extracting raw data from the source, loading it into warehouses, and transforming it from where enterprise systems can access it for perusal. Listed below are some of the most performant ETL tools in 2023:
- IBM DataStage: This tool supports models designed to extract, load, and transform data, along with providing data integrations across multiple apps and sources. Data specialists can create tasks on a Windows client and execute them on a centralized server repository.
- Coupler.io: This is an all-in-one data tool for enterprises that empowers organizations to leverage their data to their full capacity. You can collect, transform, and analyze enterprise data on custom workflows using no-code ETL solutions. It is possible to blend and export data from across sources into spreadsheets and other apps as well.
Data Transformation Libraries
Libraries are collections of open-source resources that enable data professionals to manipulate their data. Data transformation libraries are extremely flexible and offer expanded toolsets for accomplishing ELT tasks.
Pandas: The Python Data Analysis Library is an open-source library that provides fast DataFrame objects for data manipulation. Users can perform data operations like data alignment, reshaping, slicing, fancy indexing, subsetting of larger datasets, size mutability, merging and joining, and much more.GitHub Libraries: GitHub is a developer platform with tons of resources pertaining to data transformation. Users can join this community to search for data transformation tools, code packages, and a lot more to enhance their data manipulation operations easily and economically.Data Transformation Example
The data transformation need across organizations is universal. Regardless of the industry a business operates in; there are huge amounts of data being produced that must be organized before it can be used.
One stellar example is the case of RingCentral, a company that specializes in cloud-based business communication solutions. The business has hundreds of systems running and in use at any given time, which necessitates flawless and robust data operations for success.
RingCentral incorporates thorough data integration solutions in its HR wing that include ETL operations. It helps automate key data standardization processes, freeing their workforce to focus on critical tasking with enriched data.
Effective Data Transformation Best Practices
Data transformation can be more effectively streamlined by following the best practices below:
Data Quality Checks
Factors such as redundancies, duplication, and erroneous values drastically reduce the prediction capacity of ML models or any other business intelligence operations.
Data quality checks help avoid issues that arise due to data inconsistencies and incompleteness.
2. Documentation and Versioning
Process documentation is a critical activity that maintains a log of workflows that comes in handy when pinpointing bottlenecks and problematic processes. By versioning the changes made to ML processes and the data trail, it is possible to revert harmful changes and reprogram the system easily.
3. Continuous Improvement
CI/CD may be a development term, but it comes in handy across all business processes in that the continuous feedback and improvement iterations help maintain system performance without affecting output timelines significantly. Continuous improvement in data quality is reflected in improved ML predictions.
Today, data is empowerment. For digitalized businesses, poor-quality data impacts the digital ecosystems directly, generating inaccurate analyses, forecasts and predictions that impact business decisions and revenue negatively.
To enhance and empower your data workflows with AI, MarkovML offers an easy-to-use, no-code AI platform that provides you with robust data transformation features.
It empowers enterprises to take control of their ML models through the Auto Data Analyzers that identify data gaps, deviations, and trends to inform their modeling decisions.
Businesses can take down their data siloes by establishing collaboration-friendly dataflows throughout their ecosystem on the MarkovML platform.
Data management can be simplified using a Data Catalog that enables easy organization of data, metrics, and other analysis attributes for quick discovery, recall, and further analysis.
To explore MarkovML's robust AI solutions, visit the website today.