Kinetica Predicts AI and IoT Use Cases will Drive Demand for Next-Gen Databases in 2018
Kinetica’s CTO and Cofounder Nima Negahban has come out with his top technology predictions for 2018. Today’s analytical workloads require faster query performance, advanced analysis methods, and more frequent data updates. For real-time analysis of massive data sets, particularly for use cases where time and location matter, enterprises are turning to new next-generation databases to explore data faster and uncover new insights.
“Based on its enormous potential, investments in AI can be expected to increase in 2018, while investments in IoT will need to show measurable return,” said CTO and Cofounder of Kinetica Nima Negahban. “The ability to operationalize the entire pipeline with GPU-optimized analytics databases now makes it possible to bring AI and IoT to business intelligence cost-effectively. And this will enable the organization to begin realizing a satisfactory ROI on these and prior investments.”
There are four major trends that are driving the adoption of next-generation analytical databases in 2018, according to Nima Negahban, CTO and cofounder of Kinetica.
These include:
Trend #1 – Organizations Demand a Return on Their IoT investments
Companies continued to invest in IoT initiatives in 2017, but 2018 will be the year where IoT monetization becomes critical. While it is a good start for enterprises to collect and store IoT data, what is more meaningful is understanding it, analyzing it, and leveraging the insights to improve efficiency. The focus on location intelligence, predictive analytics, and streaming data analysis use cases will dramatically increase to drive a return on IoT investments.
Trend #2 – Enterprises Will Move from AI Science Experiments to Truly Operationalizing it
Enterprises have spent the past few years educating themselves on various AI frameworks and tools. But as AI goes mainstream, it will move beyond small-scale experiments to being automated and operationalized. As enterprises move forward with operationalizing AI, they will look for products and tools to automate, manage, and streamline the entire machine learning and deep learning life cycle. In 2018, investments in AI life cycle management will increase and technologies that house the data and supervise the process will mature.
Trend #3 – Beginning of the End for the Traditional Data Warehouse
The traditional data warehouse is struggling with managing and analyzing the volume, velocity, and variety of data. While in-memory databases have helped alleviate the problem to some extent by providing better performance, data analytics workloads continue to be more compute-bound. In 2018, enterprises will start to seriously re-think their traditional data warehousing approach and look at moving to next-generation databases either leveraging memory or advanced processors architectures (GPU, SIMD), or both.
Trend #4 – Building Safer Artificial Intelligence with Audit Trails
AI is increasingly getting used for applications like drug discovery or the connected car, and these applications can have a detrimental impact on human life if an incorrect decision is made. Detecting exactly what caused the final incorrect decision leading to a serious problem is something enterprises will start to look at in 2018. Auditing and tracking every input and every score that a framework produces will help with detecting the human-written code that ultimately caused the problem.