The killer app of big data is predictive analytics, which gives businesses the ability to anticipate and predict consumer behavior, and then immediately adapt. Today Apigee welcomes the InsightsOne team, and in so doing doubles down on big data predictive analytics and our ability to help enterprises understand consumer preferences, enhance the customer experience, and optimize business processes.
The rate of change in the API economy is accelerating as more and more organizations understand the power provided by apps and APIs and the agility provided by big data analysis. Here are five changes that will shape the API economy in the coming year.
With all the emphasis these days that’s placed on combing through the piles of potentially invaluable data that resides within an enterprise, it’s possible for a business to lose sight of the need to turn the discoveries generated by data analysis into valuable actions. Sure, insights and observations that arise from data analysis are interesting and compelling, but they really aren’t worth much unless they can be converted into some kind of business value, whether it’s, say, fine tuning the experience of customers who are considering abandoning your product or service, or modeling an abuse detection system to block traffic from malicious users.
API programs have become commonplace at nearly all big retailers who offer multi-channel experiences to their customers through mobile apps, in-store kiosks, the Web, and personalized in-store services, among other things. Analyzing the anatomy of a typical retail API program uncovers some interesting patterns.
In this customer-driven world, more and more businesses rely on data to derive deep insights about the behavior and experience of end users with a business’ products. Yet end-user logs, while interesting, often lack a 360-degree view of the “context” in which users consume a business’ products and services. The ability to analyze these logs in the relevant context is key to getting the maximum business value from big data analysis.
With shrinking budgets, changing consumer expectations in a hyper-connected age, as well as constant pressure to extract maximum business value from big data and deliver strong return on investments, the Central Marketing Officer (CMO) is fast becoming the catalyst to help the enterprise’s data science evolve to be business science.
Data science in isolation, without business value in sight, can reveal very interesting patterns, trends and observations. However, the best data science is purposeless unless the data science and the data scientist can recommend actions for the business owner with the express purpose of creating value . . .
Portability, usability, and quality converge to define how well the processing power of the Big Data platform can be harnessed.
When talking about Big Data, most people talk about numbers: speed of processing and how many terabytes and petabytes the platform can handle. But deriving deep insights with the potential to change business growth trajectories relies not just on quantities, processing power and speed, but also three key ilities: portability, usability and quality of the data.