What Is Data Transformation? Uses of Data Transformation in Analytics

by | Jul 13, 2020 | Blog | 0 comments

“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 is growing at a tremendous rate, making it difficult for organizations to drive insights from such complex data silos. Data is one big thing, that if transformed correctly, can become a game-changer for any size organization. This factor alone, calls for the need for incorporating 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 all about converting the raw data into a single and easy-to-read format so that the same can effectively be used for analysis. To turn your data into something that makes sense, you must have the required knowledge and skills of data transformation, as the same will assist you in driving valuable, actionable, and timely insights.

Data transformation is also known as ETL (Extract, Transform, Load), which sums up the steps involved in the process of transforming data. As per ETL, the data is first extracted from multiple source systems, transformed into a single & useful format, and then loaded into a data warehouse for powering the analysis and reporting processes.

Data

DataChannel offers a data integration platform that helps you get relief from the tiresome and manual process of data integration and data transformation. We provide you a scalable warehouse with the level of customization you need to transform all your data 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:

    This is the first stage of data transformation, where you will identify the sources of your data. Once the data sources are identified, the next step is to determine the kind of structure each data source has, 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 you need to perform 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 was carried by offsite developers and was time-consuming. The manual process often resulted 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 now 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 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. The solutions help you control and manage all your data much more efficiently.

Why is it necessary to transform data?

Every business generates a good amount of data on a daily basis, but the same is not useful until it is transformed into a specific and easy-to-read 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.

Data Integration

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.

  • When you want to add other information to your data like geolocation, or timestamps.

  • For combining unstructured data with the 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 positive 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 in meeting business goals.

Data Transform

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 the same 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 source systems 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 in doing 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 removing the corrupted values to enrich the available data. This will boost the process of data analysis and provide you relevant and accurate insights into your business operations.

  • Indexing and ordering:

    Data must be transformed so that it looks logical and complies 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.

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 expenses.

How DataChannel can help

Dashboard

Data Channel offers you an efficient cloud-native SaaS platform and cloud services that help you bring out the hidden information from the big data crumbs. Data management has become a crucial aspect in the data-driven marketing world, and to ensure that you make the best out of your data, we have your back. With DataChannel, you can surpass the challenges that different organizations face during the process of data transformation.

Support: Our innovatively designed analytics tool kits will support your team to work on data residing on different marketing and social platforms. Our services help in bringing efficiency to your overall data management process.

Cost-effective: Provides you a cost-effective infrastructure that can scale up or down according to your business needs.

Efficiency: With DataChannel, you can derive valuable and actionable insights from your data in real-time that will further aid you in making better time-sensitive decisions.

Secure: Along with helping you with your data integration process, we also ensure to protect your business data from unauthorized access.

With DataChannel, you don’t have to struggle with any coding or ETL scripts. The cloud-native SaaS platform has so much to offer that you hardly find integrating and transforming data difficult to handle. We offer basic to advanced features that help you have a firm grasp on your data transformation activities.

DataChannel offers more than hundreds of connectors that help you integrate your data from multiple sources and retrieve insights from them in real-time. Having hold of the crucial data at the right time will help you optimize your algorithms and achieve business clarity. We help our customers to experience the power of an efficient data integration platform.

Wrapping up

Data transformation helps in making your data organized. It allows organizations to bring their data from various locations and formats it into actionable insights. 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 practices, you can turn your data into a fuel that will drive your business towards success.

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