Evolution Of Data Technologies


A long time ago in a gal…uh…Southern Florida city far, far away…

A bunch of college kids were pulling off a “Miracle” in upstate New York. Dr. Carl Sagan, the American astronomer, was educating “billions and billions” of people on the expanse of our universe. A loaf of bread cost 50 cents and Luke was learning his ancestry in the Empire Strikes Back. That is when my technology career was starting, in a cold computer room of a bank in Southern Florida.

Over the decades the miracles disappeared in lieu of dream teams. Dr. Sagan passed away. Bread got more expensive. Luke made up with his dad and found his sister. Over those same decades there were definite patterns in how technologies evolved.

Patterns in Technology…

As the old saying goes, “The more things change the more they stay the same”.

Consider the devices computer users have worked with. Back in the day this was what people used to access a computer.

These “dumb terminals” as they were called accessed large mainframe computers in nearby computer rooms. Personal Computers (PCs) were in the nascent stages.

In the mid-1980s PCs were introduced to enable a more elegant user experience as well as the ability to offload processing from the hyper-expensive mainframes.

A decade later the advent of the browser, in combination with faster networks and servers that were cheaper, allowed processing to be pushed back to a secure, central location, such as a computer room.

In the early 2000s, this “circle-of-life” was completed with the introduction of desktop virtualization. With more powerful and inexpensive servers, and a greater focus on security of data, desktop virtualization enabled the ability to use dumb terminals to access user sessions running on servers in computer rooms. Data was now well protected, and the virtual desktop terminal was cheaper and lasted much longer than a PC. We ended where we began.

Just plug in a monitor, keyboard, and mouse and access your user session in the cloud. Just like that old dumb terminal, but smaller.

We see a similar pattern with software architecture. In the 1980s and 1990s client/server computing was used to leverage the power of the local device. Then the internet and the browser empowered the user to do more so the devices operated thin-client technology. Mobile technology is following the same pattern. First it was mobile apps installed on the local user device. However, with HTML5 and faster mobile networks users don’t have to install apps on their phones. It’s the modern version of client/server giving way to thin-client technology.

Data Analytics technologies following a pattern…

Modern Analytics architecture began to mainstream in the early 2000s. This included Data Cubes, analytical reporting platforms, data visualization, ETL, and Data Warehouses. All these data technologies were designed on the premise that source data would come from a system-of-record with a database of Structured Data.

Through Content Analytics the world is now beginning to understand the value of Unstructured Data. As I covered in previous articles 80-90% of a company’s data is in Unstructured form. So, all those Analytical platforms designed and built over the last 2 decades only process a small subset of the company’s data. That means these companies are making decisions based on incomplete information.

Here is where the pattern is revealing itself. Text Analytics, bringing structure to unstructured data, is being used in Data Analytics platforms now. However, the next step, Content Analytics, which uses AI and Natural Language Processing to understand the context and meaning of the content, as well as apply metadata, is the logical next step.

Imagine What You Can Do by routing that 80-90% of Data You Didn’t Even Know You Had to your Data Analytics platforms. Your organization can make better decisions based on a lot more company data.

As you make business decisions in the coming weeks think about the value Content Analytics could bring to your company through your Data Analytics platform. All these unstructured data sources contain a gold mine of information such as file shares, intranets, emails, websites, and application logs. This is what tells the true story of your company.

In my next article I will talk more about Text Analytics.

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