Last Updated on April 29, 2020
Companies need to analyze structured and accessible data for generating the best results. Unfortunately, ad hoc development of multiple legacy systems has given rise to information silos that encompass redundant as well as inconsistent data.
In order to harness data and make it valuable to the enterprise, it’s essential to integrate these data silos and use IT teams, to form flexible, agile enterprise systems. Data transformation plays an important role here.
Learn what data transformation is and how it allows organizations to drive their business forward through improved decision-making.
Data Transformation Explained
Data transformation involves a process of altering various aspects of data, including the structure, format, or values of data. In other words, data transformation allows the conversion of data from one format or structure into another. It includes a number of activities such as conversion, cleansing, enriching, and more.
In order to execute data analytics or integration projects, data may be transformed at two significant stages of the data pipeline. Here are two stages:
First Stage Involves:
- Using methods such as data discovery to identify sources as well as data types.
- Determining structure and data transformation that one needs to perform.
- Carrying out data mapping to define how individual fields are mapped, joined, modified, filtered, and collected.
Second Stage Involves:
- Extracting data from the authentic source. The nature of sources can vary, from structured to unstructured.
- Performing transformation that includes gathering sales data or converting date formats, editing text strings or merging rows and columns.
- Sending data to the respective target or a data warehouse that is responsible for handling structured as well as unstructured data.
Companies need proper tools to transform their data as, and ETL data mapping is one of them. A lot of enterprises that employ on-premises data warehouses rely on ETL data mapping tools to transform data with ease and precision.
Nowadays, the majority of companies make use of cloud-based data warehouses, which can scale and store resources in seconds or minutes. ETL (extract, transform, and load) data mapping tools help companies preload transformations and load raw data into the data warehouse. They allow enterprises to change the structure and format of raw data as per their requirements.
Data transformation involves a number of other processes such as
- Data integration
- Data warehousing
- Data wrangling
- Data migration
ETL data mapping and transformation software play a significant role in automating the transformation process. Let’s find out what are the benefits and challenges of data transformation.
Data Transformation: Benefits and Challenges
In general, companies need to transform their data to make it compatible with other data or move it to another system. They also transform data to merge it with other data or aggregate information.
For instance, if a company has purchased another business unit, they will need to deploy some tools to merge information for Human Resources Units. Because the purchased company may have a different database as compared to the parent company, and hence data transformation becomes necessary.
In addition, data transformation has some other important benefits, such as:
- Data transformation software can help organizations move their data to a new data store. Suppose, you want to move data into a cloud data warehouse, you can easily do it by transforming it or changing the data types.
- It can help companies add information to their existing data to enrich it and perform aggregations.
- Data transformation software allows companies to become more organized. The transformed data can be easier to use for humans and computers.
- Next, data transformation helps companies enhance data quality and safeguard applications from potential landmines such as null values, indexing, unexpected duplicates, and incompatible formats.
- Data transformation software allows companies to facilitate compatibility between applications and systems.
Though data transformation offers a lot of benefits, it has several challenges as well.
Some of the most common challenges of data transformation are:
- Due to its inherent complexities, this process can cost an arm and a leg. The cost of data transformation depends on the specific infrastructure, tools, and software employed to analyze data. The cost of the data transformation may encompass a lot of other costs due to licensing, resources, etc.
- Data transformation is resource-intensive. Now, creating transformations in an on-premises data warehouses after the loading process is being carried out can result in a huge computational burden that slows down operations. This can cause unnecessary delays that may impact costs further.
- The data transformation process gets compromised if the right kind of expertise is not available. This is because analysts who lack knowledge about the transformation process are more likely to ignore errors or discrepancies as they are less familiar with the process in general.
- A lot of enterprises are involved in carrying out transformations that don’t meet their specific needs. This can pose a challenge for companies that are looking forward to driving their business forward.
All these pitfalls can be avoided using modern data transformation software. Let us find what it does.
Modern Data Transformation is The Solution
Robust data transformation solutions allow enterprises to increase the efficiency of analytics as well as business processes. Not to mention, they play a significant role in improving data-driven decision making. As decision-making capability improves, enterprises can offer delightful experiences to their customers with ease.
As a result, the organization’s ability to generate more sales and hence revenue increases by leaps and bounds. Modern data transformation involves a number of other steps such as:
Extraction and Parsing
The modern ETL process involves data ingestion that begins with gathering information from a data source, followed by copying the data to its destination.
Translation and Mapping
Next, the data is mapped and translated. For instance, a column with integers that represent error codes can be mapped to the relevant error descriptions. This allows analysts to understand column and display a customer-facing application.
Filtering, Aggregation, and Summarization
Data transformation also involves filtering, aggregation, and summarization to improve the quality of data. In doing so, they need popular BI tools.
Apart from this, data transformation includes indexing, encryption, modeling, typecasting, and more.
Simply put, employ a powerful data transformation solution to efficiently process data and use the insights extracted from it to make better business decisions.