According to analyst house Gartner, Inc. report titled “2018 Gartner Magic Quadrant for Analytics and BI”; augmented analytics is all set to rule 2019’s top strategic technology trends. The researcher has highlighted the significant topics that will attract the maximum emphasis from organizations in 2019, a list that included topics like Edge Computing, Autonomous Technologies, Blockchain and Digital Twins.
Augmented analytics, the new talk of the town uses machine learning to change how the analytics content is developed and deployed encompassing modern analytical capabilities like data preparation, business process management, data management, data science and process mining. Additionally, augmented analytics gives organizations the power to embed insights into their own applications. Augmented analytics automates technological processes to eliminate the need for data scientists
Though augmented analytics may sound futuristic making BI and advanced analytics vendors march ahead to make it here and now a reality, integrated with search-based querying, automated machine learning, and other emerging technologies having a common goal to streamline and simplify the analytics process for the end users.
The Age of Citizen Data Scientists
Augmented analytics and its cohorts make it easier for business users, business analysts, citizen data scientists and other workers who have not studied formal data science but are into building predictive models themselves to learn by practice drilling down into datasets to gain valuable insights from BI and analytics applications.
These emerging technologies have been helping citizen data scientists to embed machine learning algorithms and other advanced analytics functionality into BI tools for valuable business insights. Augmented analytics or augmented intelligence, points the user toward relevant data to help them prepare for data models and creating data visualizations. On the other hand, automated machine learning platforms also assist analysts with limited expertise to build and train machine learning models for more advanced predictive analytics uses and data mining experts.
Business enterprises have embraced augmented analytics to drive technological disruption; Chevron Corp. being an early adopter of Google’s Cloud AutoML technology, designed to assist users with less machine learning expertise to build and train analytical models. Chevron’s seismic processing and imaging team have used the alpha version of an AutoML Vision image analysis tool to analyse internal documents to evaluate new opportunities for oil drilling.
To segregate documents that may contain useful geological information about prospective oil locations in the Gulf of Mexico, the Chevron team initially undertook a search on the term geologic map to pinpoint documents that had embedded map images. The next step consisted running an analytical model built on AutoML Vision which was trained to recognize more than 60 geologic labels against the map images. The team then used Google’s pre-trained Cloud Natural Language API for data classification found by the model to make the information searchable.
Augmented Analytics & Automation
In tandem with business enterprises, other top BI and analytics vendors have also embraced augmented analytics and other forms of automation. Tableau Software, is offering a recommendation engine that suggests relevant data sources to its BI and data visualization software users. Tableau has also developed AI based fuzzy clustering algorithms assisting users to group related datasets together into the Tableau Prep data preparation tool.
On an acquisition spree, Tableau recently acquired an AI start-up, Empirical Systems in June 2018. Empirical Systems is the developer of analytics engine that can automatically model data for analysis to identify data trends and outliers. Tableau plans to integrate the Empirical engine into its BI software with an aim to provide users with intelligent data insights.
In the times to come, Tableau is also looking to use NLP technology to help business users intuitively ask questions about data which is to be analysed without taking the help of a data scientist according to Tableau’s chief product officer, Francois Ajenstat.
Tableau’s top self-service BI rival, Qlik, is working to incorporate augmented analytics capabilities into its Qlik Sense software. Recent updates to Qlik Sense earlier this year included feature addition that recommends chart designs on the basis of the data fields selected by the analysts.
Another software vendor, Tibco Software has added the capability to use NLP queries to navigate through datasets and get AI driven visualization recommendations integrated to its self-service BI software, Spotfire.
Empowering Businesses to find Data-Correlations
The rise of augmented analytics empowers organisations to find data correlations that are critical in achieving operational efficiency and future cost savings. Like any analytics tools, augmented analytics software is dependent on large amounts of good, quality data related to business. If an organisation does not have adequate data infrastructure bringing analytics will be a tough task altogether. Leveraging augmented analytics into insurance sector points that children under 12 are the biggest cost drivers, making ambulance companies earn by charging per person in the vehicle, and children generally have one parent accompanying them to the hospital.
To leverage the hidden potential of augmented analytics, data literacy will be a critical driver, to enable data teams understand the responsible data and what to do with the insights they encounter.
AI automation will change the way technology works, and the way people do their jobs making companies plan for possible changes to worker roles, responsibilities and skills.