Putting data to work by extracting useful information for competitive advantage is the goal for many companies. Why else amass huge stores of data if not to use it to understand customers’ needs better, foresee new opportunities, and ward off impending threats.
However, there are challenges that stand in the way of reaching the desired result. Few companies feel entirely confident about their data from all sources, and they are notably less confident in data gathered from social media and public cloud applications than they are in internal structured data according to a Unisphere Research study sponsored by IBM, “Governance Moves Big Data from Hype to Confidence.”
Fortunately, there are more options than ever for deploying technologies for big data statistics, machine learning capabilities, predictive modeling, visualizations, data analysis and mining, model building, scoring and deployment - either on premises within the enterprise walls or through services accessed in the cloud.
One of the key ways today to succeed with analytics is to automate as much as possible to free decision makers up to tackle high level strategic decisions. With automated tools tied to rules engines, patterns or nuggets of data that are of material importance can emerge so that decision makers can spot key information more easily. In addition, making analysis as self-service as possible and providing visualization or 3D graphics interfaces will go a long way in making analytics solutions compelling and easy to use for business users, thereby increasing adoption.
HERE ARE THE WINNERS OF THE 2015 DBTA READERS' CHOICE AWARDS FOR BEST ANALYTICAL PLATFORM (Overall)
Amazon Web Services