Re-Defining Digital Transformation with Data Transformation
In the age of the world wide web, data is the key to everything that goes into the making of a developed and advanced global networking system regardless of its type. Manufacturing and production industries, digital markets, e-commerce markets, retail sectors, healthcare industries, and every other dominant sector in the world use data as a medium to communicate, forecast, and even reciprocate to customer demands and live up to their expectations.
The use of transforming data for digital transformation lays out a roadmap for industries to follow and gain success with analysis and proper processing of the same.
Defining Data Transformation
Data transformation, in general terms, is scaling data by interpreting its values, format, and structure to a readable form. The data pipeline secures transformed data at two stages with on-premises data houses that generally use ETL processes (extract, transform, and load), by using data transformed as a middle step. The second stage stores data and formats it in cloud-based warehouses that minimize latency by skipping preload transformations and transforming raw-loaded data in cloud-based warehouses.
Data transformation is implemented in various processes like data integration, migration, mining, warehousing, and wrangling. It may be constructive like the addition, copy, and replication of data or it may also be destructive like the deletion of files and records.
JK Tech’s Data Transformation solution is an automation-focused, non-intrusive framework that helps businesses improve operational efficiency and profitability. The offering optimizes business operations by leveraging the right data transformation technology.
Benefits of Data Transformation
The transformation of data and simultaneous handling of fragmented data offers benefits in many folds. Better organized architecture and validated data structures yield several benefits as well. Multiple data arrays serve different purposes and are sorted and stored abiding by the same conditions. Data analysts, data engineers, and data scientists use scripting languages like Python or domain-specific languages like SQL for transforming and storing data on cloud-based or local servers.
Some advantages of data transformation include:
- Improves Data Mining and Quality: Data transformation, being an essential data processing step, calls for better data mining patterns, and thus improves data mining all over and enhances the quality of data. Raw data is hard to interpret, keep track of, and understand. Transforming data into a suitable format eases data mining processes and gives access to clean usable data through ETL processing systems. Quality data patterns are integral in attaining essential data assets and predicting future situations.
- Better Organization: Organization of transformed data into definite structures and architectures of similar data makes data systems interpretable and readable which gives data handlers access to volumes of organized data and helps them conduct further proceedings accordingly. Organized data is easier for humans and computers to read and implement. Sequential or grouped data segments can streamline data processes and make data processing much smoother.
- Transformational: Transforming data formats, values, and parameters to make them accessible to different data processing steps is an essential step in making sure that the interpreted data is extracted, mapped, and stored in structured data sets. Most of the transformed data is distributed among cloud-based servers with high-grade encryption systems to protect the data. Data transformation ensures cross-platform compatibility between applications, systems, and data types and rectifies common mistakes such as null values, unprecedented duplicates, and faulty indexing for incompatible formats.
Data Transformation and Digitalisation
Mordor Intelligence predicts the digital transformation to be valued at $767 billion by 2026, from a whopping $263 billion in 2020, with a growth rate of 19.48%. The digital transformation of global systems has put forward many propositions from multiple parties in multiple industries to adapt to hyperautomation and data transformation changes.
How to transform data for effective digitalization:
- Data Extraction: The extraction of data through different channels, pertaining to the task at hand, is one of the most important and primary steps in digitization. The ultimate goal of achieving hyperautomation with AI is founded on the concept of extracting and leveraging data, and acting to consequences accordingly.
- Translation and Data Mapping: Storing data is meaningless unless it is interpreted, formatted, and translated into a readable form. Data engineers and handlers usually use scripting or domain-based languages to translate data into organized readable formats or further applications.
- Filtering and Aggregating: Filtering data is an essential part of data analysis as it picks out the useful data and discards the useless parts, thus avoiding data overflow and maintaining a clean record. Filtering them further leads to distinct groups of individual types of data and makes access easier for users.
- Customized and Enriched: Data is read and formatted into readable formats with specific values that are often user-specific and modified into custom packets. These data packets are then used for different prospects such as AI analysis for future market forecasts and patient data analysis for symptom tests and potential disease predictions.
- Indexing: Extracted data are numbered serially according to indexing sequences and are listed in sorted formats accordingly. This makes navigating through data easier and more seamless.
- Data Encryption: Protecting the extracted data is an important part of the digitization process and is essential for maintaining the security of user data and the sanctity of the online data storing system.
- Modeling, Formatting or Renaming: The extracted data is formatted and modeled, and renamed to be readable to the common masses.
Data Transformation in Digital Transformation Strategy
Data is one of the biggest assets in the digitization of the modern and advancing scenario. Data provides information and insights on problems and predicaments faced by the user/users and offers solutions accordingly.
Digital Transformation relies on Data to:
- Create Insights: Data sets make patterns visible, on being organized, and help users to solve certain problems with easy access to tallied solutions. Most organizations that employ data to create business solutions, use AI systems that analyze sorted and recorded data to predict future market situations and provide appropriate solutions.
- Enable Digital Intelligence: Data transformation is the greatest key to complete AI manifestation. Most artificial intelligence and machine learning algorithms in development these days are based on the amount of data that is strategically input and what that data represents. With coded parameters and complex data structures, natural language processing systems augment human intelligence.
Areas to focus on for efficient Digital Transformation:
- Data Strategy: For efficient digital transformation, a fail-proof data strategy is absolutely essential. Pre-planning on how to extract, sort, and manipulate data to gain insight into problems that may arise due to a lack of efficient processing, makes solving these problems easier.
- Data Management, Data Unification and Integration: Managing sorted data and integrating them to structure data networking systems and unifying them to incorporate them into algorithms that proceed with said data as parameters.
- Privacy: Digital transformation completely switches from the primitive analog pen and paper processes, but it also makes the data vulnerable to digital attacks. To protect the data, quality encryption is necessary to ensure user privacy and data security.
- Data Integration and Optimization: Optimizing data to adapt to different situations and offer solutions to any predicament is necessary for digital transformation. Integrating data fragments is an important factor for cross-platform, and multiple applications of datasets.
- Customer Mapping and Experience: Recording customer data in commercial sectors encompasses every aspect of customer satisfaction, by collecting feedback and suggesting appropriate solutions to ensure maximum output. Mapping customer experiences is necessary as it is the sole purpose of any B2C organization.
To conclude all that has been stated above, digitization of the modern industrial sector is highly dependent on the handling of big data and how it is structured. Though most of it is done through automated processes, refining data structuring and its analysis are important in building any organization and its future.
JK Tech is equipped with one of the best-in-class cloud storage servers that are essential parts of the Hyperautomation movement. The JK Tech innovation ecosystem comes with mobility, big data, artificial intelligence, cloud computing, social media, and analytics fundamentally transforming businesses and making organizations future-ready. Increase your brand competitiveness with personalized automation services with us today!