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Data is crucial in large global organisations, governments, and defence setups. Some of the main focus industries contributing to the world of Data Science come from Education and Research, Telecom, Banking, Pharmaceutical, Consumer Products, Internet and Communication; and that is just the tip of the iceberg.

Organisations worldwide have been experiencing the need to structure the receipt of data into their system, structure the storage, analyse the information and process it to qualified or quantified intelligence. This intelligence can further be utilised for business decisions. Added to this speed, accuracy and volume have to be on the mark.

Data now is generated and consumed from many sources like social media, mobile usage, mobile applications, wearable devices, industrial IOT (IIOT), ERP’s and traditional office utilities.

The importance of data today can be seen via some numbers below:

What does this translate into a day of global internet, it is approximately:

 

269 billion emails are sent daily in 2017, and this is expected to grow by 4.4% yearly to 319.6 billion in 2021.

 

One slight of hand could result large scale disruptions in revenues, market capital, branding and loss of consumer share.

This has led to the evolution of modern day:

  • BI & reporting solutions like Tableau, Qlikview, SAP HANA etc.,
  • Industry specific solutions like SAS
  • Programming and statistical tools like Python, R Studio and Spark
  • Cloud technologies ranging from Azure, AWS to multiple SAAS & PAAS tools which are open source
  • Databases of varied architectures from multiple technology majors like SQL Server, Oracle, NoSQL flavors which include MongoDB, Cassandra and many more

Despite so much focus on these tools, it should be noted that these are only implements used for data analytics. These may continue to be a choice or might be replaced by more evolved and adaptive technologies.

Core focus should and will remain on the science behind this, which stays with the very founding principles and fundamentals concepts around statistics, data modelling, analytical models and computational concepts.