Posted by Rajeev Ronanki on February 24, 2017
The notion of a 360-degree view of the customer has been around for at least 25 years. Yet the nature of that “view” and the tools available to construct it have changed dramatically, especially in the past 10 years as e-commerce, mobility, and analytics have spread.
I recently blogged about the growing pervasiveness of cognitive assistants, the intelligent agents that interact with and perform tasks for people. Soon, beyond setting wakeup alarms, adjusting thermostats, and recommending movies, these marvels of modern artificial intelligence (AI) will provide access to complex information and perform many digital tasks that humans do today in areas like customer service, patient care, citizen services—the list goes on.
Effective deployment of intelligent agents relies on a clear picture of the customer and his or her journey—that 360-degree view, if you will. Gaining that view means having access to vast amounts of data that provide the “dots” intelligent agents connect, draw inference from, and make intelligent decisions about as they interact with customers, employees, patients, or other stakeholders.
The good news is there is now almost an endless supply of public or semiprivate data available for this purpose. The hard part is making the connections to your own customer, because matching that broadly available data to your enterprise data set is still a somewhat challenging process. This is where graph databases, data lakes, Big Data infrastructure, analytics tools, and data science come into play. The combination of those building blocks help enable you to create the insights and, ultimately, the intelligent agents needed to engage customers, continually collect more data, find patterns in it, and translate those patterns into the individualized contextual nuggets that guide new engagement decisions.
The best news is that all of these building blocks—including the data-aggregation, data storage and management, algorithms, pattern recognition software, and natural language-recognition and language-generation solutions that go into the creation of intelligent agents—already exist. In many forms, they are already out there doing similar work for other organizations. What matters, then, is how you create the right environment in your organization for those building blocks to come together and make a meaningful impact.
How is your organization advancing its use of internal and external data for analytics purposes? Let me hear from you.