Using Forward-Looking Insights to Gain Competitive Advantage
Many organizations become interested in predictive analytics as a way to enable them to adapt to a changing conditions and increasing pressure to perform. Intense global competition, new ways of conducting business (especially on the Internet) and difficulty in understanding customers’ behavior and preferences can challenge existing processes and delay decision-making. Businesses also must manage and use effectively a growing flood of data from more sources than ever before.
To realize the advantages of predictive analytics, companies may have transform their ways of working. In regards to people and processes, a more collaborative strategy may be necessary. At an individual level they need to provide analysts with tools and skills to use predictive analytics. This research will examine how organizations can empower their people and modernize their analytic processes.
In the realm of big data analytics, the use of advanced techniques and algorithms can add value. These include neural networks, Bayesian networks, decision trees, k-means clustering, association rules, linear regression, logistic regression, support vector machines and survival analysis. In addition, a knowledge of sampling techniques may be required to avoid statistical bias in the analyses. Using such tools involves creating models, deploying them and managing them to understand when a model has become stale and ought to be revised or replaced. It should be obvious that only the most technically advanced users will be familiar with this complexity, so to achieve broad adoption, predictive analytics products must mask it and be easy to use; the research will determine the extent to which usability and manageability are being built into product offerings.