2.1 THE “OLD” BUZZWORD: “BIG DATA”
The term “big data” was first described in 1944 as “information explosion.” In 2001 an article published by the Meta Group, “3D Data Management: Controlling Data Volume, Velocity, and Variety“, first described what has been generally accepted as the 3 defining definitions of big data (the “3 V’s”). For you history buffs, you can read an interesting story published in Forbes entitled “A Very Short History of Big Data” here.
The sheer volume of data generated by applications and infrastructure is increasing beyond comprehension: however for the first time, teams will be embracing an algorithmic approach – known as “Algorithmic IT Operations” (AIOps) – to see what’s happening in the network in real time, diagnose the issue and then automate a fix.
For decades, companies have been making business decisions based on traditional relational enterprise data, such as transactions. Then, “big data” came into the picture. Along with “big data” came massive volumes of both structured and unstructured data that’s so large it’s difficult to process using traditional database and software techniques. In fact, there’s more unstructured data in the world today than structured. The volume is too big, it comes from many different sources in many different formats, it moves too fast, and it normally exceeds processing capabilities available on-premises. But this data, when captured, formatted, manipulated and stored pulls powerful insights – some never before imagined – through analytics.
The focus has now shifted from “advanced analytics” to “advancing analytics”, which will be brought into self-service tools. With more users advancing their analytics, AI will play a bigger role in organizations.
In 2017, “big data” will be subsumed into the topic of Artificial Intelligence (AI). Big data is an enabler of AI and not an end in itself.
The shift is an increased valuation of critical thinking in the workplace as people realize there’s not a deficit of data in the enterprise, but a deficit of insight. The question for big data is “what can I learn from it?, or “where can I make meaningful insights?” AI and machine learning (ML) will be the big players, and companies will need to ask questions that their data can answer through these 2 transformative technologies.
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