The Internet of Things (IoT) has gained impressive momentum over the past several years, with recent data indicating enterprise IoT spending could hit $1.5 trillion by 2020 with more than 7 billion enterprise IoT devices in use. IoT sensor devices, such as connected manufacturing machinery, product parts or even vehicles, are fundamentally transforming the way organizations do business with other companies, and the data those devices produce can provide a distinct competitive advantage to those understand it.
In this interview, Rohit Gupta, co-founder and CEO of Sentenai, will discuss the potential of IoT and sensor device data, and share which industries stand to benefit the most. He’ll also outline how businesses can make their IoT data actionable, in order to realize demonstrable business value.
1. There’s a lot of talk about the potential of the IoT. How can companies realistically derive value from the IoT and its network of connected device data?
It’s true, there’s enormous value within connected device data and making that data actionable to accelerate business growth. However the trouble is, businesses haven’t had access to the right tools to make their IoT or sensor data actionable. In fact, examples indicate just 1 percent of operational data is being used in enterprises.
The majority of IoT device data includes time series data, or sequential events indexed by time. Whether it’s data coming from a connected machine on a factory floor or the trunk of a self-driving car, time series data exists in uneven intervals and different formats that vary across datasets, making it tricky to work with.
The key to getting value out of time series data is recognizing that it’s very different than CRM data, log data or general analytics, so force-fitting IoT data into general analytics tools isn’t going to work. Instead, businesses should create useful metrics by looking at high-level features that may not be visible in the raw data itself and combining multiple data sources. For instance, rather than just looking at a temperature value, try to pinpoint degrees/hour change. Also, recognize the unique behaviors time series data will reveal, and be ready to test as many combinations as possible.
2. Are there certain industries that stand to gain the most from their connected device data?
Any business that’s been collecting IoT or sensor data for years stands to gain incredible value from making that existing data actionable. Manufacturers, in particular, fit this mold well. For example, by making their connected machine data actionable, manufacturers can gain a competitive advantage by streamlining their operational processes, optimizing their demand forecasting and better understanding their customers’ propensity to buy. They can also leverage their connected device data to build predictive maintenance programs, which can significantly decrease machine downtime and waste, leading to greater operational efficiencies.
3. What are some common obstacles organizations face in leveraging large amounts of IoT data?
Continuing the example of manufacturers, using IoT data to monitor machines in real-time is essential. However given the massive amounts of data connected machines continually produce, a huge amount of alerts are going to continually pop up, which can lead to alert fatigue. Alert fatigue can cause serious problems to go unnoticed and even have a negative effect on workforce morale. Overwhelmed IT teams can experience anxiety and stress, and as a result, lose interest in solving problems or helping their colleagues. Worse, in an attempt to keep up with the constant inundation of data, manufacturers and their IT teams can become so desensitized to critical issues that they start accepting errors as normal.
To combat alert fatigue and avoid any resulting instances of costly downtime, make sure all connected device alerts are actionable, clearly classify the severity of all alerts and adjust alert thresholds when needed. Also, use alert data to move away from conducting routine, scheduled device or machine repairs and instead use that intelligence to anticipate if/when device or machine fixes or upgrades are needed.
4. IoT and data science-based initiatives can be overwhelming. Where should an organization begin, especially if this is their first foray into the field?
Overwhelming to say the least! A crucial first step for any business is to conduct a data audit. A data audit is a way to fully understand what data a business or specific departments are generating and how that data can be leveraged into digital initiatives. The primary goal of a data audit is to fully document and understand the available data being used in each, individual business unit, and to perform a complete mapping of the data’s availability.
Once you’ve identified the available data being used as well as all data sources and any current workflows, it’s important to identify a specific use case that can drive business value and prove the efficacy of a data-driven approach. For instance, in a manufacturing organization, it might be helpful to start by understanding and predicting any instances of downtime at a single factory, or even on a single production line.
5. What existing technologies or processes can organizations leverage to get a better handle on their IoT data?
Agile reporting is key, as it allows businesses to have continual, real-time visibility into their IoT data and access forward-looking insights. Since many businesses have multiple data sources (like ERP, CRM, maintenance logs and IoT), agile reporting can be even more valuable as those different data sources can be fused together to pinpoint correlations between departments, processes and/or events.
Data forensics is a technology that works well alongside agile reporting, as it allows businesses to detect anomalies across multiple data streams and make one-off discoveries. For example, data forensics technology can enable a business to answer, “Why did this shipment fail?” Or even, “Why did my customer success rates dip at this point in time?”
Machine learning can also be incredibly helpful when it comes to working with IoT data. For instance, by tapping into existing maintenance or equipment logs, a business could apply machine learning to predict which connected devices or machines will be in need of servicing or forecast required inventory levels across warehouse locations.
Rohit Gupta is the co-founder and CEO of Sentenai, an emerging data science technology company. Rohit has been on both the investing and operating side focused on software infrastructure related companies. He previously served as a Director at Techstars Boston, where he helped select, invest and mentor early-stage startups. He holds a BS in EE from MIT.
