How cognitive mobility can transform operations analytics
As mobile applications are beginning to gather more and more big data, companies need to find new, effective ways to analyze all this information. Thankfully, the leading technologies behind today’s analytics solutions are capable of processing incredible amounts of data, more so than marketing departments could analyze manually. In fact, the best tools are even able to supplement human decision-making, resulting in faster, more efficient management of data insights and the resulting actions.
Much of this new analytics technology involves the use of cognitive mobility, which makes analytics insights more valuable than ever through the use of advanced tools that are able to learn and evolve over time. Cognitive computing also makes it easier for CIOs to pose questions and ask the analytics engine for specific insights. And, as time goes on, that engine will only get smarter, essentially engaging in a conversation with marketers and helping them to improve the organization’s overall strategy. While this new technology has yet to be perfected, it’s a major game changer for operational analytics.
Big data, big solution
Though it might seem like the work of science fiction, cognitive mobility is actually a very practical answer to one of today’s information problems. Since mobile applications have been able to provide an increasing amount of data, more and more improved tools for gathering this data have come along. As a result, developers are now much more aggressive about using mobile apps as a channel for acquiring valuable consumer information.
But the volume of this data can overrun modest analytics tracking tools, which may struggle to sort, categorize and analyze these mountains of information. Larger, newer analytics tools are designed for a far more robust workload. They can handle the volume, and they have the means necessary to use that mass amount of information in new and exciting ways.
Continuous tracking and ‘learning’
As SiliconANGLE points out, advanced analytics tools like those powered by cognitive computing can do incredible things that humans would be hard-pressed to match, and certainly not with the same efficiency. Cognitive mobility tools can, for example, sort through millions of credit card transactions to identify patterns of fraud, which can then be used to identify or even predict future crime. By analyzing these patterns, the cognitive tools grow even smarter.
All of this work is automated, which allows marketers to focus their efforts on other related tasks. But these cognitive tools aren’t smart enough to make decisions on their own quite yet. As Forbes points out, decision-making managers should use this technology as a source of information rather than a full-on blueprint.
Rethinking your mobile application’s design
Before a company can use cognitive computing tools — or other similar analytics tools, for that matter — it must install a mobile app equipped to provide the necessary volume of data. Without the app-based capabilities to gather data in a high volume, cognitive computing and other big data solutions aren’t able to reach their ceiling in terms of productivity and analytics results. This type of obstacle could prevent this technology from becoming a true improvement over earlier analytic tools. Though it may be challenging, the key to cognitive success is driving that volume.
As such, marketers should work with developers to create a version of their mobile app that takes in data wherever possible. This process involves building out new channels for data acquisition and laying out a blueprint for how data will be acquired, categorized and then distributed to operational analytics tools. Once a foundation is laid for this next-generation approach to analytics, brands can really start to see what the future has to offer.