Today, it is impossible to understate the value of data. In other words, data now governs everything. Our world is becoming more and more data-driven. The quantity of data being ingested by organizations of all sizes is growing daily, and it is crucial to fully utilize this data to open up new opportunities.
However, the sheer volume of raw data makes the data transformation process difficult. You might be surprised to find that every day, 2.5 quintillion bytes of data are created globally. Another issue is that the majority of the raw data is unrelated to your company’s operations.
What is Data Transformation?
Data transformation, in general, is the act of transforming raw data into a format that is optimized for your unique business objectives, making it usable for your firm.
Your company’s raw data may provide you with a wealth of information about your industry, clients, and rivals, which is essential for businesses to make wise decisions. Data cannot be trusted when provided in its raw form, though. The information presented within is both irrelevant and pertinent.
Raw data is extracted, cleansed, and converted during the data transformation process into a format that is appropriate for integration, analysis, storage, and a variety of other operations.
The format, structure, content, or context of the data can be changed to make it more valuable. Data transformation can be carried out manually or automatically using a data transformation tool.
Why Data Transformation is Important?
Businesses may benefit from data transformation in several ways. The following are a few of the main advantages:
Better Organisation
Computers and humans can use transformed data more easily. Transforming data entails analyzing and modifying information to improve storage and discoverability.
Better Data Quality
There are several hazards associated with bad data. Data transformation can assist your company in removing quality problems and lowering the likelihood of misunderstanding.
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Faster QueriesÂ
By standardizing data and appropriately storing it in a warehouse, query performance and BI tools may be optimized, lowering the barrier to analysis.
Simpler Data ManagementÂ
Metadata and lineage tracking play a big role in data transformation. Teams may greatly simplify data administration by using these methods. This is especially crucial when businesses expand and seek data from several sources.
Broader Use
By standardizing and improving the usability of your data, the transformation makes it simpler to get the most value from it.
Types of Data Transformation:Â Which One Should You Choose?
Several data integration technologies can alter data. The fact that there are numerous methods to approach this procedure is a plus. However, each strategy has particular advantages and difficulties. Let’s examine a few of the standard transformation methods.
Transforming data locally
Businesses can swiftly extract, transform, and load critical data thanks to on-premise transformation. An on-premise data transformation technology also contributes to enhanced security administration and increased regulatory compliance.
The bulk of these transformation tools includes drag-and-drop operations, making it possible for non-technical staff members to modify data and convert it into a format that may be used.
One thing to keep in mind is that on-premise tools are installed locally, so you must make sure you have adequate storage space and the right equipment to execute this kind of software.
Using the Cloud to Transform DataÂ
Most cloud-based data transformation technologies provide a pay-as-you-go option, which allows organizations the flexibility to scale up and down as needed. It is one of the factors contributing to the appeal of cloud-based technologies. Crucial business data being stored on a third-party cloud server does, however, raise certain security issues.
Businesses may change their data without experiencing too many delays because of the greatly shortened installation and configuration periods that come with data transformation on the cloud.
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Manual Data Transformation
There is a lot of code involved in manual transcoding. This implies that you must employ a specialized development resource to complete the task.
Data transformation by hand may be time- and resource-consuming, especially when working with various file formats. Additionally, when data is transformed manually, there is a high risk of human error and unintentional deletion of crucial business data. Therefore, using an automated data transformation tool is recommended.
Process of Data Transformation
The data transformation process consists of the following phases, which are listed below:
Data Exploration
We must first recognize and comprehend the information present in the source files to change the data. Data quality, quality traits, and source data structure must all be taken into account while analyzing source data. Better data analysis and useful business insight may be produced using this technique.
Data Mapping
Analysts determine the criteria necessary to change, match, filter, join, and aggregate certain fields within the collection of data sources as part of this procedure. Extracting business value from several internal and external sources, combining it, and then translating the data into an analytical and operational format are all steps in the mapping process.
Extraction of Data
Data must be transferred from a source system to a destination system as part of the migration process. Both organized and unstructured sources of data, such as databases and event streams and log files, can be used to obtain data.
Transform Data
The final stage in the data transformation process is to convert the data. Data from many sources, whether structured or unstructured, is gathered and transformed into a format that companies can utilize to effectively manage their data.
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Data ReviewÂ
After the data has been transformed, you must recheck it to make sure the transformation was successful. The procedure for reviewing is comparable to the procedure for quality control.
Industries That Require Data Transformation
Data transformation is a must for any business, regardless of its size or industry, to flourish. However, we’ve provided a few illustrations of data transformation apps that may help a business the most.
Healthcare
Healthcare is leading the way among all the sectors undergoing digital transformation. Artificial intelligence is being used by thousands of advanced hospitals and healthcare institutions to diagnose potential ailments and run their operations.
Financial
Various sources provide financial institutions with data about their clients. This client data cannot be used to generate sales directly. As a result, data transformation is necessary to transform the data from its raw format into useful information.
E-Commerce
Every day, e-commerce companies generate a large amount of data, and how they use that data to get insightful knowledge is a key factor in their success. For eCommerceservice providers, the significance of data transformation is therefore inescapable.
Banking
The data is also crucial to the banking industry. Banks used to utilize a huge quantity of data, from customer information to make personalized offers to clients. Banking institutions can gain useful insights from raw data with the use of data transformation.
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Top 10 Essential Digital Transformation Trends for 2023
AI for Reducing OPEX
One of the top studies reveals that the media and entertainment sector is seeing higher spending those results in lower earnings. Operational expenses (OPEX) for the sector are higher than profit margins. To make quick money or get an advantage over competitors, spending has gotten out of hand. The time has come for the sector to reconsider its approach.
Trust and Privacy
The two crucial factors that will determine how far an organization gets on its road toward digital transformation services are privacy and trust.
In one of the more than 4000 brand-consumer polls conducted globally, 61% of firms acknowledged losing the confidence of their customers due to a lack of data and information protection.
The growing divide between customers and brands is something that needs greater attention from businesses if they are to advance. In addition to social media and the government, traditional media was listed among the three most mistrusted sectors.
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Organisational Resource Planning
When discussing digital transformation services generally, technology is the first thing that comes to mind. But it’s possible that’s not always the case.
Service providers for digital transformation focus more on fostering a new culture within an organization. People, procedures, and technology are all involved. Therefore, organizations should equally focus on developing intelligent Enterprise Resource Planning (ERP) rather than just creating effective media arms or channels to promote services.
Personalization
In 2023 and beyond, the success of an organization’s digital transformation services providers will be heavily influenced by the personalization of services. No free or universal access to the material! Wiser strategies will deliver wiser outcomes. Therefore, instead of just pushing content for traction, organizations will need to consider personalizing their approach to capture the best from their customers.
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User Connection Experience
An organization’s average number of applications is growing quickly. When using numerous channels, such as Mail or mobile service portals, consumers want consistency.
Given this necessity, firms should focus on providing a connected experience as part of their attempts to use digital transformation consulting services.
Customers who use many channels report staying online longer than those who just use one channel.
Data-Driven Enterprise
Without question, data is the driving force behind today’s digital transformation service providers. In oreder to boost leads and drive business, you need to understand the potential to data, and streamline the process to provide new and better product and services.
Effective data utilization is significantly responsible for the efficient transfer of resources and smooth functioning throughout organizations.
Cloud Implementation
Today, the majority of businesses use multiple cloud environments. However, one of the biggest difficulties is managing several clouds, particularly when shifting workloads across clouds. Containers and API-led development are two answers to this issue.
Efficient IT
Today, a business’s IT staffs is a crucial enabler. By making wise and effective decisions about the application of cutting-edge technologies, the department assists in maximizing the potential of technology to provide more. Therefore, organizations must scale beyond their current IT capabilities to succeed in the digital transition.
Associated Ecosystem
As a company grows, forming strategic partnerships becomes increasingly important. The seamless integration of products and services into the consumer experience is a key component that supports digital organization in transforming.
5G and IoT
The term “5G” refers to the fifth generation of mobile network technology. Some of 5G’s key characteristics include multi-peak data rates, low latency, a better user experience, greater connection and availability, and enhanced network bandwidth.
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Conclusion
When a business adopts digital technology, it experiences disruption and increases revenue. Digital business transformation is the name given to this process. Promising developments in the digital business transformation that might hasten the expansion of businesses are seen every year. Digital transformation has several advantages, including allowing software monetization, generating data-based insights, improving customer happiness, boosting agility, and more.
