Today marks an important milestone for Industrial IoT analytics market. Harbor Research released its latest report on the state of the market and why data transformation is key to getting value from IoT analytics projects. You can download the full report here.
If I were to focus on five key takeaways from this report, they will be:
1. Over 70% of time and cost in an Industrial IoT analytics project is spent on data transformation. Data Management and transformation is the unspoken bridge between connectivity and analytics such as machine learning projects in IIoT market. Without proper data transformation, basic visualization and advanced analytics is meaningless. This is all the more true as connected machines are becoming more “intelligent machines” generating all kinds of complex machine data in different formats and protocols.
2. Not all machine data is created equal. There is simple sensor data. Then there are machine logs such as syslogs. Finally the world is headed towards multi-structured machine data that includes sensor info, syslogs, configurations data, statistical data on performance etc, and procedure data such as for medical machines (hidden in event logs). Assimilating all these disparate data sources, normalizing values, indexing facets, building object model, etc – all in one single development step is a nightmare for OEMs and operators who struggle with the notion of big data and machine data analytics. That is where Glassbeam shines really well with our core IP on SPL, SCALAR, Rules engine, and apps like Explorer etc.
3. Complex machine data will be driven with evolution of machine intelligence. As technologies mature, particularly embedded computing and software tools, machines will continue to evolve to much higher levels of intelligence. As machines become more and more complex, so too will the challenge of extracting intelligence from the machine’s data. Because more advanced intelligent machines produce a variety of more complex “machine logs” in a relatively predictable manner, it is an ideal “staging” area for designing, building and deploying a new generation of advanced data management, transformation and analytics tools.
4. Glassbeam addresses complex machine log data in ways other companies cannot. Reason is that DNA of Glassbeam is very different from companies like GE Predix, Uptake, C3IoT, Splunk, etc. Many of these companies have great IoT platforms that can ingest and index syslogs for search and analytics. They cannot handle complex logs that have different sections, formats etc, out of the box. Tackling sensor data is also fairly easy for these platforms. However as described above, machines are becoming more intelligent and so will the OEMs and Operators who will ask for deep diagnostics analytics on complex machine data. Glassbeam addresses this market segment really well.
5. Over 3 Billion machines will produce complex logs by 2022! This is per Harbor Research Smart Systems Market Model. Number of installed machines producing complex logs is projected to grow from 1.5B in 2017 to 3.2B in 2022 at 24% CAGR. Healthcare is the highest growth category from 1.3M to 5.6M machines at 34% CAGR. Machines producing complex machine log data will account for a $164.8B Value-Added Applications opportunity in 2022. Which ever way you look at these numbers, there is no denying the fact that the market is exploding and Glassbeam is right in the middle of the perfect storm of machine connectivity, data transformation, analytics, and machine learning.
In summary, Glassbeam is at the forefront of IIoT analytics market. There is huge promise in the value of conducting data management and analytics for multi-structured machine log data. However, many customers underestimate the importance of data management and transformation, and do not understand that the full value of this complex log data can only be realized through advanced data solutions. For all potential solution adopters, Glassbeam plays a key role in enabling an end-to-end data platform, spanning from data acquisition to data visualization, and is built to handle complex machine log data. Glassbeam is purpose-built to address these challenges, allowing end-users to realize new levels of value from data, while also achieving significant cost and time savings.