Ahead of the 2013 CIO Summit on 31 July in Singapore, Kimberly Nevala, Director, Thought Leadership Delivery, SAS Best Practices, shares her views about the challenges confronting CIOs hoping to ride the Big Data wave.
Photo: Kimberly Nevala
Q: Based on your interaction with customers, what are the top five (or three) priorities of CIOs (in name and/or in function) today?
Kimberly Nevala: CIOs are confronting a new level of urgency, not only around emerging technologies, but around new ways of thinking about how to do business. Top of mind priorities include:
- Collaborating with business partners to identify opportunities in which technology can drive business innovation. This starts with establishing a common understanding of the strategic value of emerging business analytics, big data, social and mobile capabilities (to name a few). It ends with aligning the business and technology tactics required to get there.
- Supporting emerging business processes with data and analytics. This covers a diverse gamut of capabilities from providing data-rich environments to support knowledge discovery to mechanisms to publish and/or operationalise insights to influence customer behaviour and business outcomes.
- Transforming existing information management practices to create a more robust and extensible infrastructure that supports the acquisition and integration of new 'big' data and digital sources while providing mechanisms to synthesise and deliver actionable information to the business where, when and how it is needed. In a very real sense, data is still the hard part.
What would you say are the best approaches/technologies for solving their problems, and meeting their objectives in this context (bearing in mind the concerns/priorities as cited in the previous question)?
It is probably fair to say that all companies should be investing in their business analytics and information management practices. The good news is that technology has progressed tremendously. Traditional barriers to data access, integration and usage due to the size, scope or structure of the data in question have largely been eliminated.
Advances in data access and integration allow high volumes of data—big and small, structured and unstructured—to be quickly and cost-efficiently captured.
High-performance analytic platforms subsequently allow easy, unfettered knowledge discovery across both structured and non-structured (text, social media, and other digitised) content. The result is quick, iterative discovery cycles and faster time to value.
In-memory and in-database processing also significantly reduce the time required to both create and operationalise analytic models. Operations like customer segmentation and scoring, identifying potentially fraudulent transactions and behaviours, or identifying the next best action occur in seconds rather than hours. This allows key decisions to be made in real-time based on an event trigger or at the time a customer interaction occurs.
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