1. The evolution of the world of IT — The foundation

The world began doing business well before IT came into existence however as the world grew the business grew, as the business grew the scale of execution grew, and that lead to the evolution of Technology and manual was no longer the way to cope-up with rising demands of business. However, it wasn’t too long that businesses realized the importance of Data which was generated by the applications that were designed to support the businesses.

The data was helping the businesses in finding the insights like -

• Monitoring the growth of the business
• Understand the Flaws in the processes.
• Finding out if a wrong business decision was taken.
• Finding out the areas of improvement in the business.
• Finding out trends — as it was derived from the historical Data.

and all this was done through Decision Support System (DSS), as they were called back in the days, during early 70’s.

So, in a nutshell the entire IT can be classified into 2 buckets:

I’m not going to talk about the difference of OLTP vs OLAP as that’s not the intent of this blog but I’m here to demystify the Data & AI landscape.

If you are interested in learning the difference between OLTP and OLAP then there I’d recommend you to begin with Bill Inmon’s — Building the Data warehouse.

However, the intent of this blog is to understand the evolution of DSS into today’s Modern Datawarehouse (MDW) or Real-Time Analytics Platform (RTAP). The foundation of which was built on Data warehousing Lifecycle.

It is believed that the analytics (using stats) came after business intelligence (where analytics predominantly meant “Descriptive analytics” aka reporting & dashboarding). If you think that, then you are wrong!! Let me give a quick summary on evolution of technology in Data landscape.

The Evolution:

Analytics:

• Alan Turing — 1940–1945 (crated Bombe) (also known as Father of Artificial Intelligence)
• Statistical Package for the Social Sciences (SPSS) — 1975 (Official) (Now IBM)
• SAS Institute Inc 1976

Data Management: Era of IT Biggies Evolution

• 1972 — SAP SE (Apps Era) Not storing data on punch cards instead on logical DB.
• 1975 — Microsoft
• 1977 — Oracle (SDL)
• 1979 — Teradata & Oracle was renamed to RSI (Relational Software Inc)
• 1983 — Teradata (DBC/1012) MPP (First DW DB-official)

Internet:

• 1990 (Information Exchange)

Big Data:

• 2000 — BigData — Distributed Computing — LexisNexis Group C++ based file storage system.
• 2003 — GFS (Google File system) came.
• 2008 — Apache.org made their Hadoop project live.

Cloud:

• 2006 (by AWS) (EC2, S3),
• 2008 Microsoft Cloud
• 2012 Oracle

Followed by Technologies like — IoT, Machine Learning, Deep Learning and AI, Robotics, Blockchain & Quantum Computing.

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Lets stop counting the dates!!!

But fascinating isn’t it.

Now, if you follow the history of data closely, you’ll notice that the DSS systems or the MDW or RTAP as known currently, have not only became mandatory instead of good to have but its because of the “data” management needs the Technology has started evolving over last decade or 2.

So today the IT can be broadly classified into the following categories:

In my Next blog, I will talk about the journey of DSS to Data & AI, we will also cover the MDW, RTAP in brief and evolution of legacy apps to smart apps.