What Is Data Transformation? Uses of Data Transformation in Analytics
"Big data” isn’t just a word but a challenge that every data-driven organization is facing in the present time. The variety and volume of data are growing at a tremendous rate, making it difficult for organizations to break the complex data silos and drive insights.Data has become a thing, that if transformed correctly, can become a game-changer for all-size organizations. This factor alone calls for the need to incorporate the best data transformation practices to speed up your analytics process. But before moving on to the uses of data transformation in analytics, we must first learn what data transformation is.
What is Data Transformation?
Data transformation is a process of converting raw data into a single and easy-to-read format to facilitate easy analysis. To turn your data into something meaningful, you must have the right data transformation tool by your side.
Data transformation is also known as ETL (Extract, Transform, Load), which sums up the steps involved in transforming data. As per ETL, the data is first extracted from multiple sources, transformed into a required format, and then loaded into a data warehouse for powering analysis and reporting processes.
DataChannel offers a data integration platform that helps you get relief from the tiresome and manual process of data transformation. We provide you a scalable warehouse with the level of customization you need to transform all your data from different sources into a preferred format. The platform is designed to work best with any cloud-service provider so that you can access your sensitive business information from anywhere and at any time. With our services at your end, you can easily extract, transform, manage, and utilize large volumes of data like a pro.
There are mainly two stages of data transformation, which are as following:
Stage 1 – Understanding and mapping the data:
The first stage of data transformation involves the identification of the data sources. Once each data source is identified, the next step is to determine their structure and what type of data transformation will be required to integrate them. You can connect your data sources based on the kind of information they contain or how the information of one source is related to another. After combining all your data, the next step is data mapping, in which you will define how the fields of all data sources are connected and the kind of transformation they require.
Stage 2 – Transforming the data:
In this stage, you have to perform the different transformations you mapped to the fields of your data sources. You can use different strategies for transforming the data, such as:
- Hand-Coding ETL Solutions: Earlier, the ETL process was set up by hand-writing code in Python or SQL. The task can be carried by offsite developers but is time-consuming. Additionally, it often results in unintentional errors and misunderstandings as developers, sometimes, fail to interpret the exact requirements.
- Onsite Server-Based ETL Solutions: These solutions work through onsite servers to extract, transform, and load information into an onsite data warehouse. Although big data companies have moved to more advanced cloud-based ETL or data warehousing solutions, onsite ETL still holds its value.
- Cloud-Based ETL Solutions: Cloud-based ETL solutions, like DataChannel, have simplified the process of data transformation. Instead of working on an onsite server, they work through the cloud. With these solutions at your end, you can link your cloud-based SaaS platforms with any cloud-based data warehouse. This will help you access your crucial business information from anywhere and at any time. You can even integrate your onsite business system with the cloud-based data warehouse to control and manage all your data much more efficiently.
Why is it necessary to transform data?
Every business generates a good amount of data daily, but the same is not useful until it is transformed into a useful format. To get benefitted from raw data, its transformation is necessary. With data transformation, you can make different pieces of data compatible with one another, move them to another system, and join with other data to drive useful business insights.
Here are other few reasons stating why data transformation is necessary:
- To move your data to a new store like a cloud data warehouse, you first need to change the data types.
- To add other information to your data like geolocation, or timestamps.
- To combine unstructured data with unstructured one.
- To perform aggregations like comparing sales data from different regions.
Raw data is like unrefined gold, precious to businesses, but to derive value from it, the same needs to be transformed. By getting your data lined up in a specific format, you can have a unified view of your business operations that further helps you to make result-oriented business decisions.
How to transform data?
Data transformation acts as a power booster for the analytics process and helps you make better data-driven decisions. The process of data transformation begins with extracting the data and flattening the curve of its types. This is done to make the data compatible with your analytics systems. The further process is carried by data analysts and data scientists that work on the individual layers of data. Every layer helps in designing or outlining specific sets of tasks that help you meet business goals. The use of data transformation in analytics and how it serves the various functions of your analytics stack.
Extraction and Parsing
Data aggregation starts with extracting the data from multiple source systems and copying the same to its destination. The transformation process starts with structuring the data into a single format, so it becomes compatible with the system in which it is copied and the other data available in it. Parsing is a process of analyzing data structures and confirming the same with the rules of grammar.
Translation and Mapping
Translation and mapping are part of the basic steps of data transformation. Data translation is a process of converting big amounts of data from one format to a preferred one when it is transferred from one system to another. At the same time, data mapping is all about finding matching fields between two distinct data models.
Filtering, aggregation, and summarization
Data combined from different sources may bring unnecessary columns, fields, and records with them. What if we tell you the same can be avoided by applying the necessary filters? Yes, you read it right. Irrelevant data can be omitted from the extraction process by using data filtering.Data can also be summarized or aggregated by, for example, transforming a time series of customer transactions to daily or hourly sales count.Business Intelligence (BI) tools can help you to perform filtration and aggregation. In case you want a more efficient approach, it’s better to do the transformations before a reporting tool accesses the data.
Enrichment and Imputation
Data from diverse sources can be merged to create enriched information. For example, merging the customers’ transactions with their information table can make the process of customer analysis more efficient. The long fields can be split into multiple columns to fill the missing values, or corrupted values can be removed for enriching the available data. This will boost the process of data analysis and provide you relevant and accurate business insights.
Indexing and Ordering
Data must be transformed to become logical and comply with the data storage scheme. You can create indexes to optimize the performance of a database. It will also help you to locate and access the required data in a database quickly.
Anonymization and encryption
Data anonymization refers to any piece of data that cannot be reversibly transformed. It is done to protect the identification of a particular set of information or individual. Now, the level of competition among organizations has become tough and calls for the encryption of private data. You can encrypt data at multiple levels, ranging from individual databases to entire records.
Modeling, typecasting, formatting, and renaming
A whole bunch of transformations that help you reshape your data into the desired format without changing the content. It makes your data compatible by casting and converting data types, renaming columns, tables, and schemas for better clarity, and adjusting times and dates with format localization.
Refining the data transformation process
Before transforming the data, it’s important you replicate it to a data warehouse built for analytics. If you want to make the most out of your ELT solution, it’s better to opt for a cloud data warehouse, like the one provided by DataChannel.
Challenges in Data Transformation
Everything has its pros and cons, and the same goes for data transformation. There are certain challenges in the process of data transformation, which are as follows:
- Slow: The extraction and transformation of large volumes of data are difficult to be processed in one go and can become a burden on your system. Therefore, the same is carried in batches, which means that the next batch has to wait for hours until the first one is entirely transformed. This thing can delay the making of crucial business decisions and result in missing growth opportunities.
- Time-consuming: Cleansing of unstructured data can take a lot of time before it becomes ready for a transformation. This is one of the biggest complaints of data scientists or analysts working with unstructured data.
- Expensive: The size of your infrastructure will impact your data transformation requirements. With a bigger infrastructure, you will require a team of data experts to manage the data, resulting in more expense.
DataChannel – An integrated ETL & Reverse ETL solution
- 100+ Data Sources. DataChannel’s ever-expanding list of supported data sources includes all popular advertising, marketing, CRM, financial, and eCommerce platforms and apps along with support for ad-hoc files, google sheets, cloud storages, relational databases, and ingestion of real-time data using webhooks. If we do not have the integration you need, reach out to our team and we will build it for you for free.
- Powerful scheduling and orchestration features with granular control over scheduling down to the exact minute.
- Granular control over what data to move. Unlike most tools which are highly opinionated and dictate what data they would move, we allow you the ability to choose down to field level what data you need. If you need to add another dimension or metric down the line, our easy to use UI lets you do that in a single click without any breaking changes to your downstream process.
- Extensive Logging, fault tolerance and automated recovery allows for dependable and reliable pipelines. If we are unable to recover, the extensive notifications will alert you via slack, app and email for taking appropriate action.
- Built to scale at an affordable cost. Our best in class platform is built with all ETL best practices built to handle billions of rows of data and will scale with your business when you need them to, while allowing you to only pay for what you use today.
- Get started in minutes. Get started in minutes with our self-serve UI or with the help of our on-boarding experts who can guide you through the process. We provide extensive documentation support and content to guide you all the way.
- Managed Data Warehouse. While cloud data warehouses offer immense flexibility and opportunity, managing them can be a hassle without the right team and resources. If you do not want the trouble of managing them in-house, use our managed warehouse offering and get started today. Whenever you feel you are ready to do it in-house, simply configure your own warehouse and direct pipelines to it.
- Activate your data with Reverse ETL. Be future-ready and don’t let your data sit idle in the data warehouse or stay limited to your BI dashboards. The unidimensional approach toward data management is now undergoing a paradigm change. Instead, use DataChannel’s reverse ETL offering to send data to the tools your business teams use every day. Set up alerts & notifications on top of your data warehouse and sync customer data across all platforms converting your data warehouse into a powerful CDP (Customer Data Platform). You can even preview the data without ever leaving the platform.
Data transformation makes data organized. It allows organizations to bring their data from various locations and formats it into meaningful information. The formatting process not only improves the data quality but protects applications from making errors like null values, incorrect indexing, unexpected duplicates, and incompatible formats. The right data transformation practices will help you ensure compatibility between your systems, applications, and types of data. Different types of data have different transformation needs, and by incorporating the best solution, you can turn your data into a fuel that will drive your business towards success.