Data, AI, and the Future of Analytics Technology
Paul Appleby, CEO of Kinetica, explains which technology is key for today’s enterprises, which is the most challenging, and where analytics and data management are headed this year and into the next.
- By James E. Powell
- August 23, 2019
Data is king. The fourth Industrial Revolution is predicated on it, in fact, but are enterprises making the most of what they have? We asked Kinetica’s CEO, Paul Appleby, what technology he sees as critical in today’s competitive marketplace, what emerging technology has him excited, and where the world of data and data analytics is headed.
Upside: What technology or methodology must be part of an enterprise’s data strategy if it wants to be competitive today? Why?
Paul Appleby: The next year is make or break to compete effectively in the digital marketplace, and the key to doing so is being data-driven. In the fourth Industrial Revolution, we must bring together streaming and stored data as well as human and artificial intelligence to offer smart, interactive, custom analytical apps that drive effective business decision making in real time.
For example, if demand for rain gear spikes in Los Angeles when unusual precipitation is in the forecast, artificial intelligence algorithms built into an inventory management smart app can send out more of those products from warehouses in Nevada.
On the customer-facing side, personalized recommendations will be based on the huge volume of historical user data, from purchases to browsing history, that must be processed in real time by smart applications in order to make recommendations while the customer shops.
What one emerging technology are you most excited about and think has the greatest potential? What’s so special about this technology?
I expect to see the broad-based adoption of artificial intelligence and machine learning. This technology used to be the domain of the data scientist, but now-common AI capabilities will be incorporated into business applications such as smart shopping recommendations, financial risk assessments, and predictive maintenance.
Take real-time tracking and analysis of live viewership, for example. With minute-to-minute analysis and response to live events, we can understand how the audience watches media, whether on TV or mobile devices. We can then tailor advertising or develop and target programs to specific demographics using detailed data about device, location, and timing.
What is the single biggest challenge enterprises face today? How do most enterprises respond (and is it working)?
The fourth Industrial Revolution is predicated on data. Success depends on recognizing data as the most valuable corporate asset. From smart cities to autonomous vehicles, logistics to retail, finance to healthcare, organizations that build smart analytical applications to make data-driven decisions that instantly shape markets, threaten incumbents, and drive new business models centered on data.
Most architectures to deliver smart analytical applications at massive scale are too complex to be effective. Traditional approaches to analytics (passive analytics) were designed before the rise of the Internet of Things (IoT), artificial intelligence, and location intelligence. Businesses are left with assorted analytics technologies that struggle to align and apply advanced analytical techniques effectively. Duct-taping solutions together doesn’t cut it in an instantaneous, data-driven world anymore.
A cloud-ready platform that unites the key elements of active analytics (historical analytics, streaming analytics, graph analytics, location intelligence, and machine learning–powered analytics) is essential. Enterprises should use such a platform to build smart analytical applications that assess and act on data instantaneously.
Is there a new technology in data and analytics that is creating more challenges than most people realize? How should enterprises adjust their approach to it?
We are just beginning to see businesses meld artificial intelligence with business intelligence. Business intelligence analysts have been using software to help them make predictions and manage business risk for years, and corporations have been outsourcing these tasks to expensive consultancies ever since.
However, this old model of studying the data the next day or the next month to make improvements for next time is too slow for real-time business.
This year, we will begin to see AI integrate into business intelligence operations, processing relevant data in real time so BI experts can make decisions that change business outcomes now. The robots are not taking BI analyst jobs, but they will certainly make analysts work smarter and more efficiently.
Where do you see analytics and data management headed in 2019 and beyond? What’s just over the horizon that we haven’t heard much about yet?
From healthcare and tech to agriculture and IoT, AI models will dramatically improve the human condition.
We are already well acquainted with IoT, but in 2019 we will see the pervasive adoption of algorithms and models across the industrial IoT that will lead to significant improvements in quality of life.
Healthcare is on the cusp of a renaissance. With the help of machine learning models and algorithms, research and testing timelines years long will decrease significantly, speeding new drugs to market and clearing the docket for new studies to begin. Algorithms will likewise help with diagnosis, drawing data-driven conclusions from data too massive and complex for the human mind to process.
Companies collecting huge troves of satellite imagery are using it to revolutionize 21st century agriculture. With the help of models and predictive analytics, this data can help farms, communities, and regions decide which crops to plant, when to water and harvest them, and how to avoid environmental damage and pests. This technology won’t just help business; it will provide a healthier, more stable, and increasing food supply to society while minimizing environmental impacts.
Describe your product or solution and the problem it solves for enterprises.
The Kinetica Active Analytics Platform lets you combine and analyze billions of live and historical data points continuously and automatically to shape your decisions instantly. Unlike the disparate, passive analytics solutions of the past, Kinetica brings together all key elements of active analytics in a fast, highly scalable, unified platform: historical data analytics, streaming data analytics, location intelligence, and artificial intelligence.
[Editor’s Note: Paul is the CEO as well as a board member of Kinetica in San Francisco, a startup that analyzes billions of data points continuously to make dynamic business decisions. Paul has built and led global teams across the United States, Asia, and Australia as president of worldwide sales and marketing at BMC Software, EVP of global sales at Salesforce, CRO at C3 IoT, managing director of EMEA and APJ for Travelex, and in senior executive positions in the Asia Pacific region at both Oracle and Siebel Corporation. Earlier in his career, Paul was CEO of Gocorp and director of financial services and telecommunications for SAP. Paul is a strong advocate of design thinking and project-based learning and regularly writes and speaks about how to prepare society for a post-industrial future driven by AI and emerging technologies. He serves on Forbes Tech Council and the World Economic Forum’s Center for the Fourth Industrial Revolution, and is an advisor to corporations and nonprofits in the U.S. and Australia. ]
James E. Powell is the editorial director of TDWI, including research reports, the Business Intelligence Journal, and Upside newsletter. You can contact him
via email here.