We use machines to automate tasks so we can achieve more with fewer resources. We judge the success of machines or systems by how well they automate those tasks; highly successful systems free us to concentrate on our objectives – they are simple to learn, simple to use and simple to own. Less successful systems are unable to mask complexity in the job at hand; they distract their operators from what they are trying to achieve while drawing them in to how the system itself operates. These systems are complicated to learn, complicated to use and complicated to own.
Bringing big data under management and making these assets available for analyses are complex, daunting challenges. Hundreds of organizations have escaped the frustrations of their first-generation data warehouses by replacing older database technologies with IBM PureData™ System for Analytics, powered by Netezza® technology. The systems that were replaced forced their users to deal with too much complexity; their warehouses demanded constant administration from highly trained specialists. This complexity is doubly corrosive: the costs of administration spiral upwards and out of control as data volumes grow, while the business, distanced from its data, must seek technical expertise to manage its interaction with its information. Disenchantment can lead business units to abandon the warehouse in pursuit of their own business intelligence initiatives: creating data silos, duplicating technology stacks, and introducing unwelcome risk to data governance and security
