At the heart of business exists a raw form of information called data. Through data transformation, companies can convert raw, unstructured data into another format or structure that can be used to glean life-changing insights.
Forbes reported that less than 0.5% of data is analysed and used. This is partly because the sheer amount of information is too overwhelming ever to get a handle of, but also because a lot of data isn’t useful or complete. That’s why the process of data transformation is so pivotal in first categorising data in terms of what it can be harnessed to produce.
Here, we will break down everything you need to know about data transformation and best practices such as using automation tools to position your business in the right place to reap the infinite power of good data.
What is Data Transformation - A Definition
Data transformation is the process of changing data’s format, structure or value. Data transformation is an essential step in data integration and data management. The amount of work that’s needed to transform data depends on how it starts and where it is stored.
Data begins in its source (or initial) setting and then can be transformed into target (or final) data. The process between source data and target data is called data transformation. Data transformation may be done manually, automatically or with a hybrid of both. Nowadays, it’s unlikely for businesses only to transform data manually because many automation tools (like SolveXia) exist in the marketplace to help your business transform data in a timely and accurate manner.
Data transformation can happen on-site if a business has an on-premise data warehouse through the use of ETL (extract, transform, load) process. Or, data can be transformed into cloud-based data warehouses in just seconds.
Data Transformation Process
The process of data transformation follows a few key steps. No matter how data transformation occurs, there is an outlined set of tasks to take, summarised as follows:
Data discovery: First, identify the types of data you have to work with. You can do this by looking at your source data and the information you have collected. Some examples of source data include CRM platforms, customer log files, web application data, accounting software, mobile app usage statistics, etc. During discovery, take note of what format the data is in (columns and rows), what information is contained and how everything is labelled.
Data mapping: Then, you’ll want to perform data mapping, or define how the data from different sources connected. In essence, each data source has a data organisation structure. So, you want to see how each source structures its data to map how they relate to one another. Data mapping is like an instruction sheet of how to merge information from disparate data sets into a single configuration.
Code generation / Create workflow: Code generation is the creation of executable code (Python, SQL, to name few). You can either code your workflow or use a software solution that does it for you so that you can customise your data transformation process. You set up parameters to define how the system should transform data.
Code execution / Run workflow: Once you’ve defined the workflow, you can execute it and run it as many times as you need to. In an automated solution like SolveXia, you can store your process, and anyone with access will be able to view a visual representation of the process. This means that anyone can easily understand your logic and see why you set up the workflow as you did. With code execution, the system will return values in your desired format.
Data review: Once you’ve run the workflow and data transformation is complete, it’s a good idea to have a data analyst or developer review the outputs to identify potential anomalies or errors.
How to Transform Data & Solutions
Data transformation can be constructive, aesthetic, destructive or structural. No matter what kind of data transformation you wish to apply, automation solutions maximise efficiency and reduce errors. Before we get into how automation solutions help, here are a few methods by which you can transform data. The way by which you transform data will rely upon your goals and the source/target destination.
Scripting: Scripting is a coding language that is intended for a run-time environment. It automates the execution of tasks so that systems can carry out the work instead of humans fulfilling each step. However, it requires a programmer to code Python or SQL.
Extraction: In the ETL process, the first phase is extraction, which means pulling information from a data source and then copying it to the target destination. This can occur with on-premise ETL, meaning that the tools are hosted within your organisation’s physical location. Alternatively, you can use cloud-based ETL tools, which allows you to leverage a vendor’s infrastructure.
Translation and mapping: The mapping and translation of data are one of the most basic data transformations that can be performed. Translation means you convert data from the format in one system to the format of another system.
Filtering aggregation, summarisation: To make data more manageable, you can filter out unnecessary fields or aggregate data together (i.e. take a time series from hourly sales to daily sales counts).
Enrichment and imputation: You can take data from multiple sources and combine it to enrich the information. For example, long fields may be split into various columns, or you can impute missing values. Corrupt data can be made into useful data through enrichment and imputation.
Index and ordering: If you want to order data logically or fit in into a data storage schema, then it can be transformed by creating indexes. In this way, you better manage the relationship between different tables.
Anonymisation and encryption: One of the biggest concerns with data is its security and the protection of private information. In most industries, exceptionally regulated industries like finance, data must be encrypted. Additionally, before data is propagated, it can be made anonymous.
Modelling, formating, renaming:Without changing any data values, you can transform whole datasets by renaming schemas or columns for clarity. Or, you can convert data types to adhere to compatibility constraints or format data values to all match one another.
Benefits of Data Transformation - Why Do It?
Every business has their reasoning behind why they transform data. In most cases, it’s done to make data compatible with other data, to combine data or move it to another system.
As such, data is only useful when it fulfils its intended purpose. To get to that point, data transformation is one of the first steps. With an automation tool such as SolveXia, you can carry out data transformation in a hassle-free, and quick approach. The software solution connects with all your data systems, provides consistent data cleansing, and mapping can carry out the transformation process to provide your team with the highly accurate information and deep insights they need. This allows for your finance team to focus on high-level analytical tasks rather than waste their time handling monotonous, and repetitive data entry and transformation tasks.
Here are some common reasons why data transformation is performed.
Organisation: To make data better organised for either computers or humans to use.
Improves data quality: The quality of data is an integral aspect to get right so that applications and processes run to produce the desired outputs.
Compatibility: Since systems or applications have to talk to one another, data sometimes has to be transformed to make it compatible.
Move it: If you want to move data from one place to another for storage or usage.
Aggregate information: To combine data from multiple sources into one location.
Mix structured and unstructured data: Data comes in different formats and structures. If you want to mix structured and unstructured data to apply it in an application, then, you may have to transform it first to combine the two.
Enrich it: As mentioned before, you could enrich existing data by transforming it to glean new information from existing data.
Comparisons: Sometimes, transforming data can make it easy to compare data from different sources.
Challenges of Data Transformation
Anyone can technically do data transformation, but it is only useful when it is done correctly. Thanks to data automation solutions, you can automate the process of data transformation to reduce the common challenges that business face when approaching data transformation:
Resource intensive: Say you’re a business with an on-premise data warehouse, then you’ll need to transform data before using it for application. This can significantly reduce the efficiency of other operations. Either way, it requires resources like data warehousing or cloud solutions to complete. An automation solution can provide you with the cloud solution you’re looking for to store and manage data in a secure environment.
Time-consuming & complex process: Without a powerful software solution such as automation, the manual process of data transformation becomes very time consuming and complicated. It can require data analysts and developers to code specific workflows for every need. Instead, you can leverage the solution of automation, where coding already is inherent in the system to run the processes your business needs most for data transformation.
Expensive/Costly: The cost of data transformation is dependent on the tools and infrastructure in line to perform the process. Some additional expenses can come from hiring necessary personnel, computing resources, or licensing. Or, you can select a data automation tool like SolveXia, which requires no coding or special IT expertise to utilise. Instead, a cost-effective software solution comes with a library of processes already built out. You can leverage the drag and drop tool or receive support from specialists to help carry out what you need.
Error-prone: Naturally, if the data transformation process isn’t performed by those who fully understand it or are adequately trained, then it can be rife with error. Having any data that is error-prone will negate all efforts to gain valuable insights from the data in the first place. To ensure the accuracy of data, automation solutions provide you with very accurate data transformation that reduces errors.
Might not suit their needs: There’s a risk for businesses to perform data transformations without expertise. This is because they may have to transform data based on the application they want to run. Then, if another application needs data in another format, it’s yet another format. SolveXia’s system can pull data from various sources and format it as required for outputs and processes to run smoothly.
Limitations of Data Transformation
Before software solutions were designed to transform data into business insights, the process was rife with limitations. Most notably, business owners had to provide data and business questions to developers that could code transformations and execute them.
Since the developer would do most of the work, they would have to interpret business leaders’ needs and requirements to form the logic. With any slight misinterpretation, the data could provide answers to the wrong questions. As such, self-service automation solutions allow business leaders to define precisely what they need, let the system automate the work, and provide the required information in an easy to understand (and often) visual format.
Best Practices to Transform Data
In a world filled with massive amounts of data, where do you begin in your data transformation journey? It doesn’t have to be hard if you follow these best practices:
Start with the end in sight: Before you start transforming data, the best thing you can do is define the business problem and questions you want to solve. This way, you can design the target format in a process known as dimensional modelling.
Data profiling: When you know what business process needs analysing, you will be pointed towards the data sources you need. Data profiling helps you know what raw data is required and the amount of work you need to perform to get it ready to analyse.
Cleanse your data: Data can have missing values or information that is irrelevant to what will be analysed. By cleansing your data, you are one step closer to performing accurate data transformation.
Conform data to the target format: In most cases, data from different sources is stored in other formats and structures. So, you’ll want to perform the steps above to structure data as needed to create an analysis that matters.
Dimensions-first approach: Dimensions provide data with necessary context. For example, when analysing sales, the results are facts, and the dimensional context is things like customers, dates and products. It would be best if you loaded dimensions first so that facts can then be linked to its dimensions.
Record audit and data quality: At every step of data transformation, an audit tracker will capture the number of records added and what happened next. Automation solutions such as SolveXia’s system’s audit trail does this with every step of automated processes so that if a stakeholder or external third party needs to understand the validity of data, they can quickly review what has taken place.
Refining the Transformation Process
To use data for analytical insights, it must be stored in a data warehouse that’s designed to perform analytics. So not only is your method by which you transform data necessary, but you must also choose a system that is capable of servicing your needs. Most businesses are opting for cloud-based data warehouses that can securely store, transform and analyse the information.
How Automation Helps Transform Data
Automation solutions like SolveXia are powerhouses that can become your one-stop-shop for all your data needs. Tools like SolveXia provide you with data solutions that can save your business time and money.
Cost-effective: With the cloud-based software, you don’t have to pay developers to code complex ETL tools or IT experts to help business leaders know what to do.
Fast: Your data is loaded and transformed in real-time, so your business-decisions will never be put on hold because you lack the right information to make them.
Accurate: Removing the need for human intervention, SolveXia reduces the chance for human error and performs data transformation processes accurately.
Secure: Our solution offers bank-grade security, and as such, many of the top financial institutions rely on it to provide them with data needs. Sensitive information is encrypted and stored securely to meet all regulatory requirements.
Support: Receive support at any point in time, or utilise the brevy of information available on resources sections of automation websites.
Data Automation tools like Solvexia can provide you with everything you need from data transformation to real-time insights neatly displayed in reports and dashboards. You can transform your team’s capabilities by gaining all the benefits that come along with data automation.