Timeseer.AI Makes Its Mark at Hannover Messe 2023
This week 📅 Timeseer.AI attended Hannover Messe 2023 🌎in Germany. Our team, together with over 6500 exhibitors from 📊 75 countries and approximately 215,000 visitors, traveled to the Hannover Fairground to learn about and experience the solutions that Industry 4.0, 5G, and autonomous systems offer to businesses around the world.
Below 👇 are my four key takeaways:
#1. We’ve made BI dashboards sexy; now it’s time to make them reliable.
I walked around the fair and noticed that many industrial vendors still focus on descriptive analytics (BI dashboards), including popular industrial examples such as overall equipment effectiveness (OEE), energy monitoring, and asset condition-based monitoring (CBM). I talked to many software suppliers, and they agreed that their data consumers do not always trust the underlying data in their dashboards. This is extremely undesirable since it’s a no-brainer that the data used in dashboards must be accurate, reliable, and consistent.
In reality, raw data in dashboards is frequently subjected to aggregation, segmentation, summarization, grouping, and averaging. Consequently, it is much more susceptible to data defects or issues with data reliability. In an industrial context, we can identify various types of data quality issues such as gaps or duplicated time stamps; sensor data integrity issues like drift, wrong placement, degradation, and broken sensors; and IoT-related data issues such as network or bandwidth problems and battery issues.
Consumers lose trust in their dashboards not because of the dashboard itself but because of the lack of reliability of the underlying data. If companies want to create trust in their dashboards, they need to ensure trust in the data used. These two aspects go hand in hand as accurate and reliable data is essential to ensuring that the insights and decisions derived from the dashboards are meaningful and actionable. By implementing appropriate data quality controls and data governance processes, companies can improve the reliability of the data used in their dashboards and therefore build trust with their consumers.
#2. It’s time for predictive maintenance (PdM) to mature.
Despite the potential value and ROI, a significant number of IoT software companies continue to prioritize predictive maintenance as their primary use case. However, the reality is that scaling this use case beyond the initial asset to similar assets is not as straightforward as it may seem. The data is often insufficiently reliable, and the necessary context is often lacking to guarantee success. While adding a vibration sensor to your asset is a crucial first step, it is not adequate in and of itself. If every similar asset needs manual data modeling, it would destroy the business case.
#3. Data guardians are on the rise.
Currently, Industrial DataOps is a highly pertinent strategic topic, especially with the industry rapidly transitioning from data collection and storage to analytics and AI. However, it is now widely acknowledged that critical middleware issues must be addressed, and data infrastructures must be the focus to achieve mature AI. To realize this goal, DataOps can be broken down into components such as data integration, contextualization, data fusion, data quality, and data observability. At Hannover Messe 2023, companies such as HighByte, Element, Cognite, and Timeseer.AI advocated for these crucial data guardianship principles. It was heartening to see that we are all complementary entities working towards a common goal instead of being direct competitors. And I foresee a future where we can elevate each other.
#4. As a Belgian, I still prefer our beer of course, 🍺 but German bratwurst is better 🌭 .
In every German trade show, it’s a common sight to see people enjoying half-liter beers in thick glasses, which can give the event a festive Oktoberfest vibe. However, when it comes to the quality of beer, I still prefer Belgian beer 🍻 without being chauvinistic 😇. In my opinion, Duvel still reigns supreme as the best beer in the world. For that, I would like to express my gratitude to Flanders Investment and Trade for inviting us to their booth for a delightful after-hours aperitif.
Timeseer.AI to the Rescue 🚀
Timeseer.AI can help with the issues mentioned above and can be integrated into 3rd party analytics solutions such as dashboard software and self-service analytics tools. The value here is that data is verified and conditioned (cleaned) before being consumed in dashboards. Each dashboard in production that is based on reliable data can have our quality stamp ‘proofed and verified by Timeseer.AI’. Actually, one of our IoT clients (Versasense) uses Timeseer.AI to proof and verify the data in their CBM dashboards.
In the case of Versasense, Timeseer.AI has also had its impact on the AI/analytics side. And to understand, this is our value proposition on the ML and AI-ops side:
Data models are now trained on clean, reliable data periods resulting in a model quality improvement. In the Versasense case, the model improved by over 50%. Our partner also confirmed that they experienced a 60% reduction in time spent on manually cleaning or correcting the data.
Likewise, incorporating data validation has value at interference (when the model is running live). Unfortunately, data quality or sensor quality integrity issues are still the main cause of too many false alerts in predictive models. However, validating data at inference will significantly reduce the number of false alerts. In this case, Versasense noticed 70% fewer false alerts.
Concluding Thoughts
Data quality is often a small feature of analytics platforms, but what companies need is one single version of the truth. This is where Timeseer.AI comes into play because providing that data truth is our core — our entire platform is built for this purpose. And to achieve success, data quality belongs in the middleware. By integrating Timeseer.AI into your own application, you can realize significant value such as:
>> Increased trust in dashboards by verifying and validating the underlying data.
>> Improved model quality by basing trained models on Timeseer-proofed and validated data.
>> Fewer false alerts of predictive models due to data integrity checks at inference.
>> 50–60% reduction in time spent on cleaning data quality.
If you want to understand how Timeseer.AI fits into your architecture, please reach out to me at bert@timeseer.AI.
