The 4 Data Eras

We’ve divided the contemporary history of data into four phases, from when it was stored in notebooks to data science and the amazing buzzwords we use today.

The 4 Data Eras

Modern society is surrounded by data. We collect it, we visualize and try to analyze it to make decisions, not only in the business world, but also in our daily lives. Just think about personal finances. We’ve got to the point where data is a product, people sell it and buy it, and we use it in exchange for products and services. Or do you think Facebook is actually free?

Getting back to the business world, in the 21st Century everything is data, and we can’t get away from it. At least once everyday we hear our colleagues say “the data shows x” or “the data doesn’t show y”, and things like “we need to get more data to prove z”.

When we look at companies that specialize in data analytics of any kind it’s always the same bumper stickers. “Get actionable insights from your data”, “Drive business decisions with real time data”, “Solve complex problems with a data-driven approach.” And it’s always the same thing.

But how did we get to this point? What’s the story behind it and what’s coming next for data in the business world?

To understand this, we’ve divided the contemporary history of data into four phases, from when data was stored in notebooks to data science and the amazing buzzwords we use today.

4 big companies that demonstrate the power of data visualization
With data visualization, companies around the globe have been able to become game changers in their industry.

1st Era - The Emergence of Business Intelligence and Databases 1920-1990

The 1920s saw the beginnings of modern data storage. In 1928, the German-Austrian engineer, Fritz Phleumer, created a groundbreaking method, in which he stored information magnetically on tape. This method is the principle of how we store digital data today on computer hard disks.

Later on, the 1950s gave birth to “Business Intelligence” a term still widely used today, which was coined by IBM. Originally, the term was defined as the ability to apprehend the interrelationships of presented facts in such a way as to guide action towards a desired goal. A definition that is not far from the one we still use today.

As with the term “Business Intelligence”, IBM led many important innovations related to data in this period, perhaps the most important being the creation of the relational database framework. This type of database stores information in a hierarchical format, making it more accessible than previous database structures. This framework is still the model for modern databases. A great example is of course the world-famous CRM, Salesforce, which runs on a platform, a powerful relational database.

2nd Era - Storing Data in a Digital Environment 1990-2000

The 1990s became a turning point in the way we store and manage data with the emergence of the internet. In a post done in 1991, Tim Berners-Lee set out the specifications for a worldwide interconnected web of data, that could be accessed by anyone from anywhere.

The emergence allowed for storing large amounts of data in the digital world, plus realizing that it was a lot more cost effective than doing it on paper. At that point, researchers around the world started wondering about how much data was actually available in the world.

In the year 1997, Michael Lesk wrote a paper called “How much information is there in the world?” and he theorized that 12,000 petabytes was not an unreasonable guess. He also mentioned that the web was increasing ten-fold each year, but that the vast majority of the data would never be seen by anyone, and therefore, no useful insights would be gathered from it.

3rd Era - Big Data and Data Scientists take the stage 2000-2020

As mentioned earlier, in the previous era experts started wondering about how much information there was and how it was probably going to go to waste, since there was no way to analyze it properly. Well, the rise of the Web 2.0 made the problem even worse, since this one was going to be user-generated, meaning that users would be able to upload and share their own data.

More and more data was flowing through the web and through companies’ databases. According to a 2009 McKinsey report, the average US company with over 1,000 employees held more than 200TB of data.

These astronomically high numbers, combined with the lack of insights, resulted in the birth of what the Harvard Business Review titled “The sexiest job of the 21st Century”: Data Scientists. These are genius mathematicians, staticians and others that are able to swim through piles of data and deliver insights to business leaders. Data scientists are still in high demand, and countries like the US are short of people with these technical skills. Whenever a business leader, of any department, needs to go through data to find insights on their product, market or anything else, they probably need to go to one of these data scientists, or engineers at the least.

An Approach to No-Code… In The 80’s
A program generator marketed as “The end of programming”.

4th Era - Welcome to Data Democratization 2020-Today

The year 2020 came with the Covid-19 pandemic, and the rules of the business world changed. People were forced to work from home, and many brick-and-mortar businesses that used old practices to gather data and sell their products or services, had to adopt digital practices in order to survive. For various months, the world has mainly been living online, with people spending more time here and doing more activities that provide data, such as shopping, banking, and communicating on social media. As a consequence, the actual amount of data has increased rapidly over the past year.

In 2020 another force started to grow, No-Code software. Coding has been one of the most desired skills in the business world, but not many people actually learn it or adopt it. Those who don’t have the coding skills to build software have actually boomed the use of No-Code tools, which are simply drag-and-drop interfaces that allow them to create software of various kinds, such as mobile apps, websites, internal tools, and data visualizations.

The use of No-Code has exploded. In fact, it is projected that by 2024, 65% software development will be No-Code. However, it is important to note that the objective of No-Code is not to replace software developers and engineers as a whole, but rather to provide non-technical people the opportunity to build the same tools they would do with code. More importantly, No-Code tools actually free up software developers and engineer’s time, by allowing business users to do complex workflows, build interfaces, and create visualizations without having to ask them for help. This allows developers and other technical folks and organizations to actually focus on what really matters, making their products better.

Getting back to the subject of data, No-Code will not only have an impact on developers, as we’ve established, but also on Data Scientists. Product Managers, Marketing leaders, Customer Success Managers and other people in the organization won’t have to ask a Data Scientist to search for data they need to gather insights. With No-Code tools, this data will be easy to Collect, Aggregate, Understand and React to, with just a few clicks. And this, like with developers, will allow data scientists to get more in depth with more, niche, complex or urgent matters.

Data has been accessible for a long time for regular business users, but until now, they haven’t had the right tools to actually get what they need in an easy manner. In the majority of cases, they have to be familiar with the standard query language, and know how the database is constructed, which takes a lot of effort and time. Important product and business strategy is left unworked on because of the lack of insights and the huge amount of friction there is in the process of creating a simple visualization.

Welcome to the age where everybody in an organization can access data and play around with it (yes, I’m serious). Where there are no barriers to entry apart from having a computer and access to the internet. Where data visualization is created in an actual visual and intuitive way, that you can actually understand and react to it quickly.

Welcome to the Data Democratization Era.