Each product includes the specific technology stack matched to the analytics engine required, as well as the change-management support required for generating the targeted impact. Several elements are important in product management:
- Product offering and marketing. Create the product map by understanding the requirements from different parts of the business and synthesizing them to create a small set of more standardized offerings that will meet these needs.
- Product engineering. Architect and engineer the major elements of each product. This includes everything from the right data-management platform (for example, do we need systems for high-volume transactional analysis or ones to extract information from volumes of unstructured data?) to the analytics tools and models that business users may apply. Balance the needs of business agility and cash flow against privacy and security, as well as performance in an on-premise solution or cloud (or some combination of the two). Determine what technologies are required for developing the product and build, buy, crowdsource, or assemble.
- Product supply chain. Use the right vendors and winnow out the hype surrounding emerging tools and technologies while placing a few bets to allow for experimentation and the experience it will provide. Big data tools are evolving, and few have a track record upon which CIOs can depend. As such, it’s not surprising to find the vendor landscape is rife with hype. To separate reality from marketing, CIOs should be in constant contact with their biggest vendors, but they should also regularly meet with venture capitalists to keep up with start-ups and track new technologies that could bring business value. Additionally, they should scout the ecosystem of external data sources to identify those that would further enable businesses to draw valuable insights and find ways of tapping into these resources.
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