Do you have a big data strategy yet?
The future is wrought with greater automation, greater machine-to-machine (M2M)—Internet of Things—communication and greater reliance on machines by humans—data and analytics—than ever before. While in one sense the human race is becoming digitally sophisticated, in contrast we are moving into an era in which humans may increasingly rely on machines to think and make decisions. Handheld calculators were the first iteration of this transformation—when was last time you manually added up a grocery bill? Even if you did the mental calculation, you likely still used a machine to check your result. In the future, this machine-supported check will likely be applied to rudimentary decisions as well.
Glimpsing the world’s foreseeable future
Humans are likely to continue to increase their dependence on machines to perform tasks that are achievable through M2M communication or because of insights drawn from data. Already, refrigerators connected to the Internet have the ability to place orders for groceries online. This function can be extrapolated to many other use cases such as booking a liquid petroleum gas (LPG)—for domestic cooking)—refill in India, which is required in many households. Imagine a device that automatically registers an online refill request based on LPG consumption. For another example, imagine elevators in offices being connected to your digital calendar to automatically take you to the floor where your next meeting is scheduled.
More than likely, we’ll all have some sort of wearable, smart device, perhaps as a wristband, that continuously performs real-time analytics on all our daytime activities. Do you remember when you forgot to write down the name of that new restaurant your friend mentioned during a previous conversation? Or what about those important notes you should have taken on suggested investment options discussed with your advisor by phone? The smart device can be like having a digital assistant with natural language processing (NLP) capabilities, allowing it to differentiate between facts and pun. See the blog post “Can IBM Watson gain wisdom?” for more about IBM Watson’s cool services.
When driving home at the end of a busy day, a smart digital assistant could, for example, log in to email and social media accounts and read out audio updates during the commute. Commuters could then make decisions based on simple gestures as they drive, such as a tap on the steering wheel. Even being messaged—through a Short Message Service (SMS) component—with offers from restaurants and retail stores while driving through a particular area is already not uncommon today. Check out other location-specific possibilities for analytics and explore how analytics helps organizations gain insights for a competitive advantage.
Devices with connectivity to Internet of Things data provide critical information for today’s targeted marketing and advertising campaigns. Marketing and online advertising have already overtaken other media such as television and print, and the data points being gathered to inform current strategy can only become more valuable as time goes on. Data is the new natural resource, and businesses are likely preparing to pay for and invest in these data points to glean invaluable insights from them.
Handling next-generation revenue streams
We already find ourselves amidst massive paradigm shifts from the business of recording and reporting to one of analyzing and predicting. As always, only a few organizations are expected to be truly prepared for these large and often costly shifts. An inability to take advantage of insights from the deluge of data could certainly be disastrous, potentially disrupting the market standing of businesses against the competition.
Next-generation revenue streams from data are expected to be based on solutions from a combination of big data, analytics, cognitive computing and the ability to develop insights using existing structured data along with new, unstructured data streams. IBM BigInsights for Apache Hadoop helps organizations cost-effectively manage and analyze big data—the volume and variety of data that businesses and customers create and collect every day.