The whole purpose of analytics is to translate raw data into something that's understandable to the human eye and can be actioned in a meaningful way. That involves collating that data and actually forming it into something that's easily readable by a person who isn't necessarily versed in parsing complex collections of information. It's not always easy.
One method of making it easier to understand could be on the horizon though: voice commands. Google announced back in July that it was bringing voice controls to its analytics platform, letting users ask natural language questions of their data, removing the need for dashboard navigation and for custom graph making.
One of the key reasons that Google spent as long as three years porting over its vocal translation services to its analytics platform was to enable anyone within a company to make use of the reams of data they store and analyze. You don't need to be an expert in analytics to ask a question of the system, which means less middle-management between individual employees and more direct access to the data which can make a huge difference to how people do their jobs.
This should come as no surprise though. With the expansion of consumer products such as Amazon's Alexa, Apple's Siri, Microsoft's Cortana and a host of other smart, digital assistants, voice commands are part of a distinct evolution of how we interact with our devices -- there should be no reason we don't do the same with analytics.
Indeed there's a true crossover of consumer and data analytics with services such as Sisense Everywhere, which recently integrated its analytics engines with Amazon's Alexa, and Slack. As VentureBeat highlights, it's made it possible for users to make natural language requests of their data. It does point at the potential limitations of such a system -- visual representations of data are still far more efficient than auditory ones -- but the interaction itself is certainly enhanced through voice commands.
Another aspect that will be certainly bolstered by this sort of interaction is machine learning. Although a system can learn our preferences through the data we look at through traditional means, that restricts learning to users who are already well versed in accessing, curating, and analyzing data themselves, even with the assistance of an analytics system. With vocal commands making it possible for comparative laymen to also make use of analyzed data sets, there's a real potential for analytics systems and the data they spit out to become much more user friendly.
That layman access covers the whole gamut of users, too. While those on the lowest rungs of a business' ladder can make use of it then for their own jobs, regardless of data qualifications, it also means that board members can leverage data more than ever before. They don't need to call in the IT guy just to have them present certain data sets to them. As Economic Times India explains, this could lead to much greater data-driven leadership from the top, which is where it could ultimately be the most impactful.
Data is becoming democratized, not necessarily through its analytics alone, but through a more naturalistic way to read it in a way that's most actionable for the user.