In the news, dotData CEO Dr. Ryohei Fujimaki P.h.D. discusses how Automated Feature Engineering and the advent of new automation platforms will allow Business Intelligence Professionals to begin contributing to their organization’s AI development efforts by creating predictive analytics dashboards quickly and easily.
Modern BI tools present vast opportunities for organizations, allowing businesses to unearth new insights, efficiencies, and innovations and become more proactive in carrying out daily operations. 2.5 quintillion bytes of data are produced by people every day, and it will grow faster each year. Business intelligence leaders are already struggling to translate this explosion of complex data into actionable insights. As a result, there will be a significant demand for more advanced, easy-to-use data translation tools.
Accelerated data movement to the cloud will disrupt existing BI infrastructure. “On-premises” data stores fail to scale compared to the explosive growth in data assets. As a result, more and more analytical and reporting data stores will also move to the cloud. 2021 will see a “Hockey stick” adoption of cloud data stores. From a SQL execution standpoint, existing BI infrastructures fall short of meeting new cloud data store requirements. More BIs Doing AI. The COVID-19 pandemic has slowed down AI investments during 2020 for most enterprises. As organizations face increased pressure to optimize their workflows, more and more businesses will begin asking BI teams to develop and manage AI/ML models.
This drive to empower a new BI-based “AI developers” class will be driven by two critical factors: First, Enabling BI teams with tools like AutoML 2.0 platforms is more sustainable and more scalable than hiring dedicated data scientists. Second, because BI teams are closer to the business use-cases than data scientists, the life-cycle from “Requirement” to the working model will be accelerated. New AutoML 2.0 platforms that help automate 100% of the AI/ML development process will allow businesses to build faster, more valuable models. AI and BI will increase their synergies by scoring BI data sets against ML models, visualizing the predictions, or leveraging natural language processing for generating visualizations, insights, and summaries. Read more at Database Trends and Applications today“
