INDAAQ Brings Expertly Sourced Industrial Machine Data to the Databroker Marketplace
Databroker is proud to announce the addition of Industrial Data Acquisition Solutions (INDAAQ) to our global community of data providers.
The Hannover, Germany based team of sensor and data experts have already published two data offers on the marketplace. These are industrial machine process sensor and image datasets that provide insights into precisely the rate at which industrial drill bits degrade overtime. The data can be leveraged by manufacturing firms looking to transition towards Industry 4.0 practices, as well as by IT firms looking to support the same transition through the refinement of machine learning (ML) and artificial intelligence (AI) models.
“We’re excited to be able offer these high-quality datasets on the Databroker marketplace, and we’re looking forward to adding more in the near future. For us, Databroker represents a secure and reliable channel for monetizing the data we generate. Perhaps more importantly, making our datasets available on Databroker supports our goal of driving industrial innovation.”
-Hemanth Mandapati, Co-founder INDAAQ
Taking a step back, let’s examine in more depth the context in which this data can be applied. Remember, the so-called fourth industrial revolution (Industry 4.0) is the move towards a data-centric approach to manufacturing where data obtained from sensors and IoT devices are leveraged to refine processes, improve efficiencies, and add value. One key method for doing so is to engage in predictive maintenance.
Predictive maintenance is the practice of identifying problems before they occur so that workaround solutions can be integrated without incurring catastrophic unscheduled shutdowns. For manufacturers, downtime due to machine failure and unplanned maintenance hits the bottom line hard. For example, this 2016 report from Aberdeen Group found that the cost of a one-hour unscheduled outage averages $260,000. Even at this relatively early stage in the leveraging of data towards improved predictive maintenance methods, research from McKinsey suggests the practice increases production line availability by 5 to 15% and reduces maintenance costs by 18 to 25%. A 2018 PWC report suggests predictive maintenance practices extend the lifetime of aging assets by 20 percent. Such efficiency gains can be the difference between life and death in the hyper-competitive global manufacturing environment, so it’s not surprising that the global predictive maintenance market is expected to reach $6.3 billion by 2022, according to a 2019 Market Research Future report.
To accurately predict failures, we need data. Real-time data on the current performance of machines is of course useful, but to train the ML/AI models to make accurate predictions, we also need large volumes of historical data. Unfortunately, many firms simply don’t have access to that data. For example, a 2018 survey of 200 US-based manufacturing firms found that 36% lack data analysis and/or collection abilities. It’s little surprise, then, that 48% of the surveyed manufacturers have buy-in at the executive leadership level for data-driven initiatives like predictive maintenance, but only 15% have reached the production stage for such initiatives. This is where datasets such as those expertly harvested by INDAAQ come in.
Moving back to the datasets so far published by INDAAQ, they use high-speed-cameras to capture milling/drilling imperfections data and apply AI to define the imperfections thresholds. Previously, the only way to develop and define such thresholds was by manually sampling production batches. Now that process can be automated and bad samples can be rejected automatically.
Companies performing milling and drilling operations using computer numerical control (CNC) machines can use the data to automate maintenance, quality checks, and tool replacements.
There’s also an opportunity for IT firms to leverage the data to improve their ML/AI models. Training, testing, and validating these systems require large amounts of data. In many cases, the expertise for gathering the needed data falls outside the general expertise norms of an IT engineer or data scientist. In such cases, these firms can use INDAAQ’s expertly generated datasets to train their created systems. This has potential to quicken the system-training process by 20% according to INDAAQ, ultimately empowering IT firms to offer custom solutions to their customers, and at a lower cost.
“The addition of INDAAQ’s data to the marketplace is a perfect example of what Databroker is built for. For INDAAQ, the marketplace provides exposure to their datasets, offering potential for additional revenue streams. Buyers, meanwhile, get convenient and secure access to hard-to-find data that can drive huge value add. At the end of the day, this data will be used for streamlining industrial processes, meaning it’s important for both economic growth and adhering to sustainability goals. It’s a win-win-win.”
-Matthew Van Niekerk, Co-found and CEO Databroker
Datasets listed on the Databroker marketplace are, in many cases, just a sliver of what can be made available. Book an appointment with our Data Match advisor, who will work with INDAAQ to ensure you get exactly the datasets you need.
About the Databroker.global Marketplace
Databroker is the first global marketplace for data that allows the exchange and monetization of any type of data (IoT sensor data, files, APIs, data streams, and more). Data buyers and sellers can connect via the Databroker platform and exchange data securely on a peer-to-peer basis.
Alternatively, data-rich companies can, if they wish, leverage our white-labeled Platform-as-a-Service (PaaS) solution to operate their own data exchange platforms.
Databroker manages all financial transactions, acts as escrow, and applies blockchain smart contracts to enable the sharing of data safely between both parties.
About Industrial Data Acquisition Solutions (INDAAQ)
Incubated at Leibniz Hannover University in Germany, INDAAQ consists of a team of sensor and data experts with a vision of improving manufacturing systems by providing unique and customer specific data.