17 Business Sectors Most Ripe for Disruption Through Data Analytics
Industrial sectors such as construction, rail, aviation and energy are swimming in data, much of which comes off sensors on machinery and infrastructure. Yet most of this data isn’t utilized to its full potential. While data science has been used in the industrial sector for some time, many businesses have yet to go beyond basic analysis of historical data. They’re stopping short of capturing real-time data to gather insights and connect them to suggested actions that improve business productivity, equipment reliability and operational safety. In this eWEEK slide show, Uptake, a fast-growing, $2 billion startup that provides predictive analytics software for industries, examines the sectors it believes will be most heavily impacted by predictive analytics applications and the internet of things (IOT) in the next couple of years.
The energy industry continues to optimize existing processes and infrastructure while integrating a new wave of technologies. As this happens, connectivity and data analysis are having an immense impact on the industry ecosystem. The companies that most effectively leverage their data will be the ones that dominate their respective markets. Two Berkshire Hathaway Energy subsidiaries—BHE Renewables and MidAmerican Energy Company—are utilizing data analytics solutions to optimize productivity, particularly through insight into operations and maintenance of their wind fleets. The technology enables engineering and operations teams to quickly discover, investigate and track high-priority problems impacting downtime across a diverse fleet; detect, analyze and resolve underperformance issues; and respond to machine learning recommendations to optimize production.
For the aviation industry, data analytics can improve speed, efficiency and value across manufacturer and airline operations. There’s an incredible amount of both on-board and off-board data to be mined. Intelligently handling the increased data volumes generated by aircraft is becoming increasingly complex and challenging. By connecting the aviation ecosystem to a unified data platform, there’s potential to improve almost every aspect of the aviation business’s performance—from routing to maintenance to operations. For example: Airspace restrictions can interfere with route efficiency. Data analytics platforms can enable real-time traffic pattern monitoring to streamline flights. They can also help pilots to be storm-ready by going beyond the standard radar and dispatch methods pilots use to stay updated on weather patterns. This gives pilots and air traffic control more lead time, enabling safer route planning.
Data science in construction paves the way for increased uptime of assets and fleets, helping operators manage things that impact their bottom line, like fuel spend, equipment performance and, most importantly, safety. In fact, a Financial Times article from August 2016 referred to a report conducted by a large manufacturer of construction and mining equipment in which they studied a large mining company’s malfunctioning machine. The issue resulted in 900 hours of downtime, according to the article, which equated to $650,000 in repairs. The article goes on to note that if predictive analytics had been applied in the scenario, the machine would have only been down for 24 hours resulting in a mere $12,000 in repairs.
The rail industry has spent billions of dollars outfitting its equipment with sensors for the past decade. Nearly every component on locomotives and the railways they traverse are steadily pumping out data that could increase reliability, productivity and safety for rail operators. But there’s been far too much data for most rail companies to effectively analyze, and in most cases, it’s stored in disparate silos. It’s a perfect example of data deluge defeating the initial purpose of data collection. Predictive analytics applications can improve asset utilization and reliability and thus enable railroads to minimize unplanned downtime. The technology monitors locomotive performance and status information in real time to provide diagnostics, prognostics and repair recommendations to eliminate unplanned failures.
Agricultural operations have become significantly more connected and digitized in recent years. This has enabled data analysis to play an increasingly important role across the agricultural ecosystem. Farmers and companies can analyze farm and crop conditions, identify mechanical and maintenance needs on equipment before costly breakdowns take place, and inform planting, harvesting and fertilization decisions. In fact, IoT expansion in agriculture is expected to increase food production 44 percent by 2021. A more digitized world will yield greater amounts of data, which will enable smarter planning and optimization for farmers, drastically increasing production capacity and safety.
Retailers are inundated by hundreds of millions of data points on any given day. Applying predictive data analytics capabilities in retail can help major businesses transform that abundance of raw data into measurable insights. A few specific examples include optimizing complex supply chains, maximizing the uptime of critical machinery like registers and illuminating growth opportunities through store traffic trend analysis.
Predictive analytics in supply chains helps enterprises keep a closer pulse on inventory planning during peak times of the year as demand ebbs and flows. Keeping store shelves appropriately stocked during the holidays, for example, while also avoiding having money tied up in overstock is walking a fine line that is best dictated by real visibility into demand. Retailers no longer need to operate blindly; predictive modeling enables them to anticipate specific levels of demand, or lack thereof.