The global predictive maintenance market is expected to grow to $6.3 billion by 2022, according to a report by Market Research Future. However, a new paradigm is required for analyzing real-time IoT data. Predictive maintenance, which is the ability to use data-driven analytics to optimize capital equipment upkeep, is already used or will be used by 83 percent of manufacturing companies in the next two years.

In the era of Peak TV, there is probably no more emblematic version of worthy binge watching than “Game of Thrones”. At the cusp of the series’ eighth and final season, the internet is buzzing with some painstaking analysis – using algorithms, AI, and big data sets, to find hidden Easter eggs. The numbers capture the mind-boggling detail author George R. R. Martin weaved into his novels and how easy it is to get lost in a fictional world as intricate as our own.

Business Intelligence (BI) tools have taken the business world by storm. According to new research, over 80% of executives believe that tools such as advanced visualization, dashboards, and reporting are critical tools when it comes to parsing data. However, many end users aren’t bringing in those dashboards because they really use them, rather they are hoping to get a sense of security (incorrectly) that they will know everything about their business.

One of the world’s leading bed banks – a wholesaler of hotel allocations to B2B and B2C clients – recently experienced a surge in bookings. Great news, right? Not really. A glitch caused room prices from one of their hotel suppliers to drop from $100+ per night to JUST $8. Imagine how much this glitch could have cost them if left unchecked.

Glitches happen. Even to the best of us. And rather than promote others’ misfortune, we created this list to highlight the importance of anomaly detection – and as a warning against denial. Because if it can happen to the world’s most-recognized companies, it can certainly happen to you.

It is 5 a.m. Tuesday. The ETL job that populates revenue data into your organization’s data warehouse fails midway through the process. When the CFO opens the mobile dashboard to review the last day’s results, he immediately notices that the data is wrong – again. For a few hours, the on-call ETL Architect determines what caused the data-load failure, fixes the issue, and restarts/monitors the job until it successfully completes.

Simple enough to be embedded in text as a sparkline, but able to speak volumes about your business, time series data is the basic input of Anodot’s automated anomaly detection system. This article begins our three-part series in which we take a closer look at the specific techniques Anodot uses to extract insights from your data.