According to a new report by analyst house Gartner, Inc., augmented analytics is the most disruptive (and pervasive) data analytics trend right now. In fact, the researcher believes augmented analytics will be the dominant driver of new business intelligence and data science purchases by the end of next year. The prediction comes from Gartner’s paper entitled Top 10 Data and Analytics Technology Trends That Will Change Your Business. Augmented analytics has the potential to completely overhaul the industry, and we’re already considering it to be one of the most common use cases for enterprise BI.
Augmented analytics uses machine learning to change how analytic content is developed and used. The technology encompasses other modern analytical capabilities like data preparation, data management, business process management, process mining and data science. Organizations can also embed insights from augmented analytics into their own applications. Augmented analytics automates these processes to eliminate the need for data scientists.
Specific use cases for augmented analytics span a variety of industries and verticals. In banking for example, augmented analytics has enabled firms to target a younger demographic for wealth management services. This is a scenario where the data to support this practice hadn’t been available previously using traditional business intelligence. The same rings true in the healthcare setting where insurers have found the main cost driver of ambulance transportation costs to be under 12-year-old patients. These are two basic examples of what can happen when non-technical users are allowed to drive the analytic process.
Gartner recommends organizations already using business intelligence to complement those efforts by pairing them with augmented analytics, especially for high-value business problems. The more manual and time-consuming the better, as this is where augmented analytics can really make a big difference like we’ve outlined in the examples above. It’s also important to monitor augmented capabilities and roadmaps of established BI providers and startups alike. Some of the major players in the industry are already hard at work adding augmented functionality to their portfolio (see Qlik, Oracle, Sisense and TIBCO Software).
Data and analytics leaders should start the process of augmentation by building trust in machine-assisted models and analysis by facilitating collaboration between expert and non-technical users. This way everyone involved in the process can understand limitations of the technology and see which algorithms work best against the organization’s business goals. Managers are also advised to look into deployment, setup and other factors like the openness and explainability of machine learning models, accuracy and number of variables supported.