Throughout the recent decade, data gathered by companies has become essential for fuelling nearly every business process. Data-driven enterprises enjoy higher stability, face fewer risks, and showcase higher efficiency of operations. How?
Data lets you:
- Verify and back up your assumptions, make informed decisions, and often avoid and negate potential risks long before they pile up and become a problem
- Plan strategically and proactively instead of reacting to short and mid-term challenges
- Understand your customers’ needs and business processes as if they were an open book.
However, there is always a ‘but‘: gathered data is often raw, disparate, and can hardly be used straight out of the box. This is when data transformation comes into play.
Why is data transformation important?
Validated and properly transformed data is the best source of valuable business intelligence. Research by Gartner shows that poor data quality costs companies around 15 million in losses, annually.
Due to several reasons, the quality of raw data is often poor. A variety of sources and a great number of tools for data processing and analysis leads to compatibility issues, duplication, and corruption. Because of this, data extraction becomes an expensive, excessively lengthy, and error-prone process.
So, the importance of data transformation is caused by a simple need to refine the acquired data and improve its quality, to increase the efficiency of business operations and decision-making.
Benefits of data transformation
Some businesses treat data transformation as unavoidable evil: a resource-intensive and time-consuming process that diverts your effort from the immediate data analysis.
But, the truth is that your business can already gain several benefits at this stage of working with data:
- Improved comprehensibility. Data specialists find it much more convenient to work with the data that has been transformed
- Reduced risks. High-quality, standardized data can help you avoid financial and reputational losses stemming from inaccurate planning and decision-making caused by inconsistent, disparate information
- Refined metadata. The ever-growing volumes of information cause chaos in metadata, making data management significantly more challenging. Transforming your data on time makes it easier to manage, organize, understand, and use your information assets
- Reduced computational challenges. Neatly organized and standardized data reduces computational errors that may occur in applications due to incorrect indexing, duplicates, null values, etc.
- High compatibility. Datasets sharing a similar structure and format can be processed by different systems and tools
As you can see, the transformation makes it easier for your human employees and computer systems to work with large arrays of information. The main question now is, “What does it mean to transform data and how can you do it right?”
What is data transformation, exactly?
Let’s take a closer look at the data transformation process, its definition, and some real-life cases that most of us face daily.
Data transformation definition
In terms of data, transformation means converting raw data from one format or structure (XML, Excel, SQL, and so on) into another, cleansed and ready-to-use, format or structure.
The most basic example of data transformation is a mundane conversion of DOC files into PDF format. When you need to preserve text formatting intact across all devices and software versions, converting to PDF is a viable solution.
Another typical example involves your smartphone’s voice assistant. In this case, data transformation happens every time you use your phone’s text-to-speech function to input text messages or search for something on Google.
Let’s look at some more real-life examples, shall we?
Data transformation examples
In business environments, data transformation can happen in a multitude of situations. Here are three common scenarios:
One of the most complex types of data transformation jobs, and one that probably requires the largest amount of work, is a corporate merger. During mergers and acquisitions, companies that are mostly large corporations with sizable data responsibilities have to develop a solution that unifies their databases.
Often, that involves migrating client portfolios and historical sales data. Not only can these datasets be stored in different formats, but not all data from the acquired company will serve the same role after the acquisition. For instance, personal client data can be added to the common sales database. However, sales data cannot be used for historical analytics together with data from a new company, as it does not reflect the current sales team’s work. However, that data can still be useful.
This means a lot of data has to be moved, sometimes to different parts of the database, and much data has to be transformed. One such example is Jones Lang LaSalle, acquired a smaller competitor to obtain a new portfolio of corporate clients. The main challenge of this case was to combine the databases of the acquired company and JLL in a way that allowed historical data analysis and sales data integration.
The company doesn’t provide the bulk of the details, as details of database organization are a corporate secret, but we can extrapolate the amount of work needed to be done during M&A from the known details. This comparatively small acquisition took three months to execute, and that’s considered to be fast.
Migrating from an in-house database to the cloud can elevate your company, as it allows greater scalability and reliability of the system. However, many SaaS companies have the unique challenge of moving a functioning system to another platform while keeping it up or reducing downtime to a minimum. That also involves improving the existing data systems and changing formats as a self-hosted system migrates to the cloud.
Betabrand achieved this by conducting multiple test migrations and making use of staging environments to fine-tune not only the end result of the migration but also every step and every transformation that needed to take place first. Thanks to rigorous preparation before the migration, the final process of transforming all data and getting systems back online only took two hours.
E-commerce systems integration
Modern e-commerce systems do not function on one platform only, and often require integration. A company may use Shopify for managing online sales, Hubspot for tracking multichannel marketing, and Quickbooks for accounting. Bringing all that data together means data transformation tasks have to be done every day, preferably automatically.
Coupler.io provides a fast and easy solution to data integration, helping your organization transfer and transform data from the multiple, disparate systems it uses to a single data analytics hub like BigQuery. The integration process can be fully automated, giving you the reassurance that once mapped and configured, your data will arrive at its destination and can be transformed further if necessary.
These are just some of the examples. As you can see, transformation happens all the time. But how does the actual process occur?
Data transformation process: how do you perform it?
Although there is no uniform procedure to transform data, there is a general sequence of typical steps that many specialists perform. Let’s take a closer look at them.
Data transformation steps
Your analytics and engineers usually execute the following routine when transforming data:
- Data discovery and interpretation. Before any manipulations take place, you need to understand what data you already have and what you need to change. Computer systems tend to automatically interpret retrieved data based on the file extensions in which it comes from the source. Instead, you should use data profiling tools that allow you to peek inside files and databases to determine what exactly you are dealing with.
- Data mapping. This is a preliminary step for many operations with data, including transformation, database consolidation, and so on. Here, you need to decide what elements of the datasets you need to change, for what reasons, and how you are going to do it. Also, a good idea is to develop a plan on mitigating potential data losses that can occur during the transformation process.
- Code generation and execution. To transform data, you need to launch the code executing the whole process. This code is usually generated by transformation platforms and other tools, but your data specialists can write the script manually, too.
- Actual data transformation often involves the restructuring of the whole set of data to present the information in a new way. Depending on the nature of this information and your particular needs, you can do it in different ways. We will cover the most popular of them below.
- Review and send transformed data to the target location.
This is not an exhaustive list. Depending on the situation, your data specialists can take additional, custom measures, such as data transfer or data export.
For example, you have your data stored in BigQuery and you need to move it to Google Sheets. You can export the data as a CSV file, upload it to Google Sheets, and then transform it as you need.
On the other hand, you can import a specific query from BigQuery to Google Sheets using Coupler.io which will transform the data before it reaches the destination. So, you can get the ready-to-use data set right away.
Coupler.io is not limited to BigQuery and supports many other sources including Airtable, PipeDrive, Dropbox, and many others.
Types of data transformation
In general, transformation can happen in the following ways:
- Constructive. Analysts copy and replicate parts of the dataset, or add something new to it
- Destructive. Data entities or their parts are deleted
- Aesthetic. To make information comprehensive and easily accessible, a data specialist trims and standardizes it according to specific requirements
- Structural. Analysts reorganize and optimize datasets
Each of these data transformation types corresponds with specific transformation methods, some of which we are now going to look at closer.
Data transformation methods
Speaking about specific techniques, there is a wide range of methods you can use to cater to your needs. In particular, we are talking about:
- Integration. To create a unified, consistent picture of a customer, business process, system, or whatnot, you need to perform data integration. This means that you gather information about a subject from different sources (e.g. customer info stored in the databases of sales, marketing, and accounting departments) and combine it into a form of a summarized report.
- Filtering. To siphon off specific information from a data flow, you can set special filter conditions to separate unnecessary data. Then, you can use this subset for analysis and send it to the target location instead of a large batch of disparate data.
- Scrubbing. Data that has just been retrieved from the source is not always usable at once. Often, data specialists have to work with information that is corrupt, incomplete, or irrelevant. To fix this, they check the records for fragments, syntax errors, noise (meaningless information), and typos to ensure that only clean and accurate data gets to the target location.
- Discretization. When working with continuous data, a good idea is to break it into smaller intervals to create a limited number of possible states. By creating interval labels you can turn large continuous datasets into finite, categorized ranges. This method simplifies further analysis of continuous data and helps you to get a clearer picture of trends and long-term averages.
- Generalization. At its essence, generalization is transforming data from a low-level to a high-level format. It can be very useful for two of the most basic tasks of data transformation: simplifying large data sets into an easily analyzable format, and anonymization of data. Here’s an example of data generalization:
|Address||810 W 1st St, Springfield, OH 45504||Springfield, OH|
|Data of purchase||04.11.2021||April 2021|
- Duplicate disposal. Not all information you retrieve is unique. Often, there are multiple copies of the same data instance. If you do not get rid of them, it will hit your storage capacities hard. The solution? De-duplicate your data to ensure its uniqueness before storage.
- Attribute construction. This technique allows you to organize information by creating new attributes for datasets.
- Normalization. This is an organizational procedure that aims to standardize the way your database records are structured, used, and visualized. Normalization greatly improves data usability, and performance, and optimizes the sizes of datasets.
Data transformation best practices
Here are some ideas to help you make the whole process as smooth as possible.
Improve the data literacy of your staff
A crucial element for any data-driven company is the employees’ skills in handling and working with data. Gartner defines the ability to comprehend, communicate, analyze, and apply accumulated information as data literacy, and it is something you might want to promote across your organization.
Essentially, data literacy is about facilitating the extraction of valuable intelligence from data for everyone, not just data analysts. As a result, the chances of data misinterpretation drop at all levels of management, and it becomes much easier to detect and prevent possible operational problems.
Practice thorough data profiling
Roughly put, profiling means high-level scrutiny of your data: its examination, analysis, identification of trends, detection of quality problems, risks, and so on. This process occurs on multiple levels:
- Structural. Everything related to data formatting and datasets structure.
- Content. Examination of individual entries and fields, searching for null values, noise, ambiguous or duplicate data.
- Dependencies. Revealing how different datasets relate to each other (e.g., overlapping).
Overall, data profiling lets you:
- Identify and fix data quality issues before they impact the decision-making process
- Improve the searchability of your data
- Evaluate its quality, value, and scope of application
- Enhance your data management and governance capabilities
- Have a better understanding of the potential challenges related to integration, and more.
Use proper software
Operations with data often require a lot of resources and effort. To sugar the pill, we recommend using transformation tools designed specifically for making data enrichment, modification, and restructuring faster and easier. There are many solutions available on the market, and you can choose whatever suits your transformation purposes the most. Some of the most popular tools are:
- SAP Data Services is a real Swiss knife in terms of transformation, as it is a complete toolkit for changing, refining, combining, and moving your data. SAP’s functionality includes data audit, geocoding, changed data capture (CDC), joins, filters, and more.
- Matillion ETL is a cloud-native ETL solution for transforming and loading data, built for Amazon Redshift, Snowflake, and Delta Lake.
- Azure Data Factory by Microsoft is a cloud-based ETL tool that lets you create pipelines for scheduled data ingestion, enabling data monitoring, management, orchestration, and transformation at larger scales.
- IBM InfoSphere Information is a data integration tool aimed to help you extract more value from disparate, heterogeneous data spread across multiple systems.
- Coupler.io. A no-code consolidation tool integrating with many popular systems and data visualization tools. Coupler’s main purpose is to pull information out of a CRM, accounting app, or another source and export it into Google Sheets, Microsoft Excel, or BigQuery. The service does it automatically: you only need to set the criteria for exporting data, choose the target destination, and set up intervals for automatic data refreshes.
As you would probably expect, a process as complex as data transformation cannot occur without difficulties. Although it is impossible to foresee and prevent all of them, you can prepare yourself for some of the most common ones.
Data transformation challenges
Here is the list of typical issues that many companies face:
- The proper choice of data to work with. The “transform all data” approach can do more harm than good. Instead, stakeholders must prioritize their goals and needs clearly, so that technical specialists could align data acquisition and transformation accordingly.
- Prolonged testing. When data is transformed, it is essential to ensure that the implemented changes are meaningful and increase the overall quality and value of a dataset. This procedure requires a number of tests that can be time-consuming, especially when it comes to large information volumes.
- Governance and compliance issues. Today, most companies use cloud data warehouses to store their information assets. However, due to a variety of solutions available in the market, companies experience difficulties when choosing a vendor. Mostly, problems stem from different standards for data preparation, audit, documentation, catalogs, data lineage, and more.
- Potential data losses when moving to the target destination or warehousing. Also, sometimes analysts run into data that is non-normal. Such abnormalities can provide valuable insights into the ongoing processes but can be easily eliminated during transformation.
- Compatibility issues. Organizations that use legacy ETL systems may experience problems with extracting business insights from acquired data in real-time. The reason is simple: a lot of new data sources such as IoT are incompatible with older ETL systems.
Successfully dealing with these challenges means cleansed, high-quality, valuable data in your warehouses.
Data transformation meaning for your projects
By data transformation, we mean the process of converting raw data from one format or structure into another, cleansed and ready-to-use. Without it, you would have to wade through the chaos of unstructured, noisy, irrelevant, and corrupt data.
Over recent years, the number of devices, gadgets, and mechanisms that generate data has grown exponentially. Such a variety and number of sources lead to a broad range of formats in which information is acquired, making raw data inconvenient to use and incompatible with target systems. To store, manage, wrangle, process, and integrate it effectively, first, you need to transform it.
Transformation boosts decision-making and enables proactive strategic planning that would hardly be possible otherwise. Transformed data helps you avoid compliance and financial risks, decreases computational loads on your systems, and simplifies the work of analysts significantly.
Although data transformation can be complicated and time-consuming, you can facilitate it by promoting data literacy across your enterprise, accurate data profiling, and the use of proper tools. Transformation and automation tools such as SAP, Azure, or Coupler.io have proved to be especially effective when working with data.