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.
According to experts in this space, BI is prime for a paradigm shift in 2021. Here IT leaders predict key changes they see shaping up for BI in 2021.
BI advances to a new stage. 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.—Ramesh Panuganty, CEO of MachEye.
Accelerated data movement to the cloud will disrupt existing BI infrastructure. While this trend has already begun, it is likely to pick up steam in 2021. “On premises” data stores fail to scale compared to the explosive growth in data assets. Businesses from restaurants to healthcare organizations are embracing the cloud for better agility and scalability. 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. However, from a SQL execution standpoint, existing BI infrastructures fall short of meeting new cloud data store requirements. To adapt to the cloud ecosystem, organizations will have to upgrade to advanced BI platforms.— Dhiren Patel, MachEye’s chief product officer and head of customer success.
More BIs Doing AI. The COVID-19 pandemic has slowed down AI investments during 2020 for most enterprises. Although AI is still one of critical technology areas, enterprises need an efficient way to scale their AI practices and implement AI in business to accelerate ROI in AI investment. 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 class of BI-based “AI developers” 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 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 useful models.—Ryohei Fujimaki, Ph.D., founder and CEO of dotData.
BI and AI blend together. BI and AI will deepen their liaison. Whether scoring BI data sets against ML models and visualizing the predictions, or leveraging natural language processing for generating visualizations, insights, and summaries, AI and BI will increase their synergies. And as conventional BI capabilities continue to commoditize, vendors will need BI+AI as a new front in the innovation wars.—Andrew Brust, analyst, Gigaom.