Enthusiasm for and investment in Big Data and the Cloud is spurring innovation in a suite of new technologies that seek to transform information into knowledge at reduced costs. But the potential of Big Data and the Cloud is threatened by security, privacy, legal and regulatory constraints which prevent data integration and information sharing.
While the costs to capture, store and exploit data are declining, the costs of mishandling data are rising for every enterprise; and threaten to extend the data-poor environments in which we have long operated, forcing continued inferences and limits on data insights.
Technology leaders like Google,Facebook and Target have reshaped their industries using Big Data, but each is facing increased scrutiny over data handling. The result has created an atmosphere of concern and trepidation and has deterred many in the Fortune 1000 from embracing Big Data.
The relationship between Big Data security and Big Data innovation is not zero-sum, but rather they are mutually reinforcing concepts. Traditional data security approaches, which have proven inadequate, deal with disequilibrium by seeking counterbalance. In this case more security, more privacy, and more constraints lead to limited data access, continued fragmentation of data sets, and missed opportunities.
Instead of addressing these challenges as an afterthought or applying solutions around the edges, solutions that bake in and address security, privacy, legal and regulatory constraints from the onset enable new insights, while simultaneously building trust and transparency. Such a data-centric security model promotes adaptability and re-conceptualizes the relationship among data, users and applications and reduces administrative burdens and risks. Simultaneously it unlocks the potential for innovation and serves as a mechanism for supporting the integration of disparate data sets and for more complete information sharing.