Data. From the barcodes on products to our digital footprint, data is a fundamental part of how we live our lives. For organisations, data helps them to develop – not only to survive but thrive in a world where understanding customer behaviour can not only drive sales. It also helps create a long-lasting relationship between businesses and their customers.
Data is often a challenge to handle, though. From a suite of corporate systems to the increasing variety and variability of data, data engineers have long had to wrestle with the challenges of a constantly growing data environment. It doesn’t take a Master of Data Science to understand just how challenging data can be.
Data engineers often turn to automation to solve complex data challenges. An important process in the data lifecycle, data automation can have many benefits, from streamlining complex data systems to automatically creating curated data warehouses for business use and making data more meaningful for end users. Let’s explore data automation and understand how this seemingly benign process greatly benefits organisations.
What is Data Automation?
Data can be handled in two main ways: manual, which typically involves physically manipulating data in platforms, and automated, which uses data processing systems to enable automatic data processing.
Historically, manual data manipulation has presented challenges for businesses. Even a tiny error can have drastic consequences when critical processes depend on manual handling. For example, contact tracers in England experienced this firsthand when the collation of Excel datasets led to the missed detection of tens of thousands of potential Covid-19 infections.
Data automation uses a three-stage process: Extract, Transform and Load (ETL). Automated processes work across various organisational platforms to extract data, transform it into a meaningful format, and then load it into different data warehouses for analysts and other employees to consume.
Why is Data Automation Critical?
Data automation is a critical part of any data user’s toolkit. Without it, any form of analytics would first require data extraction from various systems. Automation is more than just getting data into one place – it’s about providing standardised structure and meaning so that analysts and other users can be confident in the quality and veracity of the data when they access it.
By leveraging automation, data engineers help eliminate repeated manual tasks and structure data to enable further analysis and investigation by data and business analysts. In a world where data is vital for business operations and expensive to store, finding ways to streamline and optimise data flows can benefit both by creating accessible data for everyone and managing costs so that such access is efficient.
The Role of Data Transformation
In Australia, Flybuys is a household name. As one of the largest loyalty programs nationwide, it processes large amounts of customer data daily. This data can take various forms, such as transactions at retailers, redemption data from partners, and even the way users use the Flybuys app – the list is extensive.
With more than 7 million transactions tracked by Flybuys each week, data transformation and automation can help paint a picture of a customer’s unique preferences and interests. This level of automation can then be fed into other processes to provide personalised marketing that addresses organisational needs while also being highly relevant to customers.
For example, data transformation can help Flybuys create segments of customer information. For example, some customers may make regular purchases of pet food or frequent pet care rewards. A data engineer may set up a process to automate a flag for customers interested in pet products, which marketers could use to send appropriate offers.
While there may be dozens or even hundreds of underlying source systems that help to depict a customer’s behaviour and interests, automation makes the work of end users (in this case, marketers) much more straightforward – instead of orchestrating many datasets, instead, they have a single, automated point to use.
Benefits of Data Automation
While data automation may seem benign, the benefits can really stack up. Consider, for example, analysts across multiple teams who conduct analysis on a number of datasets. By having data engineers develop an automated process to create a curated dataset, analysts can spend less time compiling data and more time understanding the insights that are available from it.
By providing automated datasets, analysts spend less time transforming data, giving them more time to generate insights. This can be useful for businesses using data to make rapid decisions. Rather than acting on a whim, they’re supported by business processes that give them the best possible information to make an informed decision.
There are scalability benefits, too. Take, for example, the analyst who has to build their datasets. As data becomes increasingly complex and extra data sources are added, using manual processes to undertake a task can become increasingly challenging.
On the other hand, a data automation process can help make this process work efficiently and effectively, saving the company time and reducing the risk of potential manual handling errors.
Limitations of Data Automation
It’s important to note that while data automation can significantly benefit organisations, it’s not a silver bullet for all data challenges in a business. If automation fails or something goes wrong, a data engineer may need to manually intervene to remediate or repair the problem.
Data quality, by design, is often only as good as the data ingested. As the idiom goes – if you put bad data into a dataset, don’t expect great results from it – after all, garbage in, garbage out.
While data automation can scale, it’s also key to recognise that if not properly managed, the cost of automation can sometimes outweigh the benefits it can bring to a business. In some cases, data automation is more than building out a dataset, particularly when huge datasets are involved. Nuance is often required – building what is necessary rather than what is on a company wishlist.
Ultimately, data automation can present enormous benefits for companies that look to take it on. While the letters ETL can seem like a particularly simple process, the benefit that it can bring to an organization often goes beyond the processes themselves.
As data continues to become increasingly complicated, the relevance of automation will only continue to grow. It’s fascinating to imagine how it could shape how analysts and end users use data – if effectively managed.
Could data automation signify a new industrial era – that of digital automation? Only time will tell.
