Top 10 BIG DATA challenges

Big data” is one of the core component of the Future of Procurement and has major challenges to overcome. The meaning of the expression has been evolving since it appeared in the 90’s for the first time. If “Big Data” has always been associated to the size of data to manipulate and speed for doing so, the definition has been dramatically updated since then and is now in agreement with the following and commonly named the “3Vs definition

“Big Data represents the Information assets characterized by such a High Volume, Velocity and Variety to require specific Technology and Analytical Methods for its transformation into Value.

Additionally, a new V “Veracity” is added by some organizations to describe it.  If  the definition embed in itself a set of very visible challenges, it doesn’t for all. Below is a table summarizing the top 10 challenges in Big Data and their implications for Procurement. A view that helps identifying the new skills and processes required.

Top 10 challengesWhat it means for Procurement
Merging data from different sourcesHow to align spend data from different IT systems and get to a global spend cube enabling to see who bought what when and how?
Enabling explorationHow to enable users to make spend analysis?
Creating a single vision of the truthHow to manage daily buying events?
Architecting blended data set for more complete analyticsHow to enrich spend data with complementary contextual data which can add value?
Expanding the use of dataHow to identify and enable a usage generating incremental savings?
Creating advanced analytical environmentsHow to display spend analytics unleashing smart analysis ideas?
Supporting applicationsWhich technologies to use?
Controlling access to dataHow to associate spend-data access-rights to user profiles, especially when sharing data with other companies?
Managing the analytic lifecycleHow to use time stamp, when comparing spend data (like price for example)
Ensuring complianceHow to curate spend data and secure accuracy?

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