The Augmented Analytics guide makes companies more powerful, regardless of size and resources. Augmented Analytics augment decision making and results. Data is useless for a company unless the company is able to extract significant information from it. Many organizations, especially medium and small ones, are unable to extract relevant information and operational suggestions from the data. Without data scientists or resources available to interpret the data and transform it into commercial actions, the benefits of the data remain blocked. This situation changes with Augmented Analytics.
Thanks to AI, Augmented Analytics solutions look for models in the data or discover other valuable information without the involvement of data scientists. The analysis can therefore be made “speaking” and shared with the “humans”, ie the end users who thanks to the “machine” thus increase their intelligence. Reporting is easy to understand even for non-technical people.
To better understand, here is an example. You may have a range of data available indicating that your sales are down 25% year on year. But there are other questions that need to be answered before knowing what it really means. Is it because your marketing isn’t effective? Does it happen to others in your industry? Is the cause your product or your sales staff? So knowing just the data on the drop in revenue doesn’t make that information valuable to your organization.
A deeper “dive” is needed to find out what this really means. This deeper dive requires looking at information from multiple sources, cleaning up data, analyzing it without preconceptions, understanding the insights that have emerged and then communicating what could be done to improve sales. Augmented Analytics can also help predict a possible drop in sales before there has already been and suggest actions to avoid it.
Before Augmented Analytics, companies needed to hire data scientists or analysts to make sense of the data, and this was only financially possible for some. Even if the money were available to pay a data scientist, this could be difficult to find: the McKinsey Global Institute predicts that by 2024 there will be a shortage of approximately 250,000 data scientists in the United States alone. In addition, a significant part of what data scientists had to do (an estimated 80% of the time) was to collect data from various sources, label it and clean it up, which is a repetitive job that Artificial Intelligence can do quite easily and without errors. Gartner predicts that advances in augmented intelligence could make 40% of data science activities machine based by 2020.
In addition, data scientists rarely have a “business sense” to indicate the business action that must be taken from data analysis. Therefore, a salesperson with that “business sense” should work closely with the data scientist to acquire detailed information and transform it into business actions. And there is often no time to do this.
Augmented Analytics democratize data and allow all companies, regardless of their size, to extract meaningful information from multiple data sources, derive and propose intelligent business driving suggestions. Everyone can quickly get answers (“the vital shift from big data to big answers”).
Data scientists can rest easy: they will continue to be in high demand even with Augmented Analytics, no longer for repetitive data collection and preparation tasks, but for more strategic activities and special projects. Both in smaller and larger companies where Augmented Analytics will significantly speed up the projects that can start from already consolidated outputs.
More and more people (now named citizen data scientist) can be easily “empowered” by speaking data, which thus becomes part of their daily activities and decisions and not just reserved for data scientists in the strict sense and analysts. The data become an engaging and always active environment, which can also be interrogated vocally (conversational analytics). Intelligent assistants can inform decision makers when some positively or negatively anomalous data needs their immediate attention and reaction, as it happens, instead of waiting for it to be sought (see alerts of the Alternative Data Intelligence FinScience Platform).