From the Printing Press to Modern Databases: How Automation is Increasing Productivity and Improving Lives

Mankind has long dreamed of the promise of automation. Our earliest ancestors imagined it as something the gods could wield. In Homer’s The Odyssey, Hephaestus, the Greek god of smithing and craft, uses automation to complete simple repetitive labor. Buddhist legends speak of automated guards who watched over the Buddha’s relics. The entire point of windmills and watermills was to automate arduous labor, like grinding grain or fulling wool. More recently, my own grandmother was thrilled every time she tossed the laundry into the washing machine, saying “I used to do this by hand but now a machine does it for me!” Today, our ancestors would be amazed at how many devices they imagined in myths are real—from ones we use every day to those that go beyond what they ever could’ve imagined.

We have long eclipsed simple automated mechanical technology, like the washing machine, and have begun automating many more complicated tasks. The explosion in popularity of AI through platforms like ChatGPT has upended the way everyday people think about automation. What was once only a dream of myths or science fiction is slowly becoming more and more real.

If we look at the history of automation, we can see that the development of new technologies has aimed at making us more productive and freeing us from routine and repetitive tasks. For us in IT, we have seen the benefits of automation, often called AIOps, or the collection of technologies, tools, and processes used to manage IT operations at enterprise scale. These tools help us make our teams more effective and free us from often routine tasks to pursue more valuable, creative, and fulfilling work.

The Printing Press: Freeing Us to Write
One of the earliest and most impactful examples of automation was the invention of the printing press. While the original printing press was used by hand (later to be further automated by steam engines), it automated an incredibly crucial task: transcription. In order to create copies of texts, large numbers of scribes were required. In the day, these were often some of the most intelligent people, as they knew how to read and write. However, due to technological limitations, they needed to transcribe text by hand.

This was the equivalent of our modern IT departments, where DataOps teams and engineers put their combined brainpower toward turning raw data into insights. Just as the printing press freed scribes to focus on creating their own writing instead of copying someone else’s work and kickstarted a scientific and social revolution, IT teams will be similarly unburdened and able to turn their time and attention toward innovation or capitalizing on fresh opportunities.

Mass Production and Increasing Productivity

Automation, or the reduction of human intervention in processes, typically invokes the image of a factory. With the invention of the steam engine and the innovation of standardized parts came the proliferation of factories and production, meaning goods that might take weeks for one artisan to craft could now be completed in a fraction of the time. This freed people from the long production process, granting them more leisure time while simultaneously allowing for products to be more widely accessed as more were made.

This model of production was exemplified in the Ford factory, with the introduction of the assembly line, where parts were brought directly to workers. This created an even greater level of production than ever seen before. Since then, there have been further advances, including introducing robots and computers into the production process.

This has happened in IT as well, with much of the routine work needed to make networks, databases, and applications run smoothly has been automated through scripting and Infrastructure as a Service technologies. This means that IT teams today have the tools to be more productive and efficient than ever before.

Databases and the Lessons of Automation

The printing press and the history of mass production point to two important takeaways of the benefits of automation. It can vastly increase productivity, in the case of factories, and free us from the heavy lifting, arduous tasks that do not add value. Now, we are free to spend time on tasks that are fulfilling and valuable to our organizations, just like with the printing press. This is exemplified by the use of automation and AI in databases.

Databases are essential to a successful IT environment. With the vast amounts of data being collected every day from all our various applications and devices (close to 328.77 million terabytes a day now) we need a way to store and make sense of it all. This led to the creation of databases and with it, the difficulty of managing and making sense of the complex data they store. However, years of work to innovate how databases function and are administrated has led to automation playing a key role in most enterprise IT organizations, helping teams to be more productive and freeing them from the routine tasks associated with database management, such as backups.

The original databases needed to be managed manually, and users often needed to navigate the entire database in order to find the data they needed. However, the advent of early database management systems, like IBM’s Information Management System (IMS), allowed for some basic administration that would help to lay the groundwork for more robust database automation. The development of the Structured Query Language, or SQL, represented the next major step towards automation. SQL standardized the way organizations interacted and administrated their database. This made automating data queries a possibility, and in turn led to the establishment and growth of relational database management systems such as Microsoft SQL Server, Oracle, MySQL, and PostgreSQL, which in turn allowed for the automation of tasks like backup, recovery, and high-availability.

As more and more data is being collected and stored every day, databases have only grown more complicated and difficult to manage. AIOps has helped to take automation a step further by helping to detect performance anomalies, monitor for security threats, optimize performance, and improve decision-making based on data analysis. This helps IT teams to tackle the difficulties of database administration. AI is also being used in database observability, which can provide greater insights and offer a view into the “black box” of databases. This additional visibility is especially important as SolarWinds data found that one-third of tech pros are managing more than 300 databases in their organization’s environment. For these overworked tech pros, AI can detect and remediate issues before they even arise, saving teams additional time.

For large database estates that still manually manage databases, with the mass amounts of data being collected every day, entire business lines could grind to a halt simply due to rapid growth of their databases. When an issue does arise, it can be nearly impossible to remediate and fix them in a timely fashion. However, database automation has allowed for teams to prevent issues and, when they do surface, address them quickly. This not only saves IT teams time, it also saves the organization money, since downtime is directly correlated to revenue. And makes IT teams more productive, by freeing them up to work on more rewarding and complex tasks, like innovating and designing new products.

Such as the printing press or the factory, database automation is changing the way IT teams work, increasing their productivity, and creating more fulfilling and rewarding work for teams. As we think about where the future of automation is heading, especially with questions around the use of AI, we must not forget our history. Automation has helped us become more productive and lead more fulfilling lives. This must be the guiding principle that defines the future of automation.