Data Scientists vs Data Analysts
November 5th, 2019
Step forward “big data”, a term that has evolved to describe exceptionally large, diverse repositories of digital information and the processes organisations use to organise, search and extract the data they contain.
Never slow to spot a revenue opportunity, many of today’s IT giants are big on big data, especially where demand for massively parallel processing (MPP) systems capable of handling those huge databases dovetails neatly with these vendors’ existing server and storage hardware platforms.
Evidence of how highly those vendors rate that opportunity can be seen in EMC’s cash purchase of data warehouse specialist Greenplum last year, for example, with rival IBM acquiring business intelligence and analytics company Netazza for $1.7bn in September 2010, and HP grabbing Vertica for an undisclosed sum last March.
Many other companies, including Microsoft, Oracle, SAP and Endeca are looking to sell enhanced database, analytics and business intelligence tools based on the big data concept, though the very definition of the term tends to be manipulated to play to individual product strengths in each case, meaning big data remains a moving target in many respects.
Handling large data sets is certainly a real problem for many organisations, and it’s one that’s getting bigger by the minute. IDC’s last Digital Universe study estimated that the total volume of data being stored in the world will reach 35ZB (one zettabyte is equal to a trillion gigabytes) by 2020, although much of that will be stored in personal, rather than corporate, systems and not used for business analytics or reporting purposes.
Of more relevance to this particular discussion, perhaps, is recent research from McKinsey Global Institute (MGI) which estimated that organisations across nearly all sectors in the US economy had at least an average of 200TB stored somewhere within their IT infrastructure, with many storing more than 1 petabyte.
Some industry experts, including Microsoft CEO Steve Ballmer, believe that big data should focus less on size and more on the type of data being processed and analysed, including information stored outside the corporate firewall.
Data being searched for analytical and reporting purposes could be anything from internet text, search indexes, call records, medical records, digital images, high definition (HD) video archives, surveillance footage and e-commerce transactions, for example, as well as datasets created by academic, scientific and research departments or by development projects that process large volumes of information.
And all of that information could be unstructured, or distributed in flat schemas with little or no cross-reference relationships, and could also involve time stamped events extracted from log-files, sensors and social networks.
“The true challenge is not one of big data but the more complex issues across all dimensions of information management… variety, complexity and velocity of data are equally significant,” wrote Gartner analyst Stephen Prentice in a research note published in May.
Read more at source: Computing.co.uk