Market and Business Drivers for Big Data Analytics
We consider the market conditions that have enabled broad acceptance of big data analytics, including commoditization of hardware and software, increased data volumes, growing variation in types of data assets for analysis, different methods for data delivery, and increased expectations for real-time integration of analytical results into operational processes. We examine some contrasting approaches in adopting high performance capabilities, such as simplified execution and application development models, scalable storage, alternative data management schemes, and the alternative hardware and software appliance models (as well as cloud-based or utility models) for instituting the big data platform. We suggest criteria for evaluation, including feasibility, reasonability, value, integrability, and sustainability.
To best understand the value that big data can bring to your organization, it is worth considering the market conditions that have enabled its apparently growing acceptance as a viable option to supplement the intertwining of operational and analytical business application in light of exploding data volumes. Over the course of this book, we hope to quantify some of the variables that are relevant in evaluating and making decisions about integrating big data as part of an enterprise information management architecture, focusing on topics such as:
• characterizing what is meant by “massive” data volumes;
• reviewing the relationship between the speed of data creation and delivery and the integration of analytics into real-time business processes;
• exploring reasons that the traditional data management framework cannot deal with owing to growing data variability;
• qualifying the quantifiable measures of value to the business;
• developing a strategic plan for integration;
• evaluating the technologies;
• designing, developing, and moving new applications into production.
Qualifying the business value is particularly important, especially when the forward-looking stakeholders in an organization need to effectively communicate the business value of embracing big data platforms, and correspondingly, big data analytics. For example, a business justification might show how incorporating a new analytics framework can be a competitive differentiator. Companies that develop customer upselling profiles based on limited data sampling face a disadvantage when compared to enterprises that create comprehensive customer models encompassing all the data about the customer intended to increase revenues while enhancing the customer experience.
Adopting a technology as a knee-jerk reaction to media buzz has a lowered chance of success than assessing how that technology can be leveraged along with the existing solution base as away of transforming the business. For that reason, before we begin to explore the details of big data technology, we must probe the depths of the business drivers and market conditions that make big data a viable alternative within the enterprise.