Timeseer.AI

“Timeseer.AI gave us confidence that we can provide accurate insights and that our data is of sufficient quality to build more complex data solutions.” 
— Jorne Delforge Director of Operations at iFLUX. Discover the full story here: https://lnkd.in/e99UwM2W

Timeseer.ai is invited by SWAN - The Smart Water Networks Forum to join SWAN's Digital Water Programme at Aquatech Amsterdam. 💡 From Data Streams to AI Dreams: Water Management 4.0 Join Adriaan Van Horenbeek (Timeseer.AI) at the Technology Case Study Showcase, starting at 3:30 PM CET, hosted by Wayne Byrne (Burnt Island Ventures). He will be joined by: - Thomas Bennett (UDlive) - Jorge Helmbrecht (Xylem Vue) - Slavco Velickov (Bentley Systems) - Chad Feather (Daupler)

Join us at the Timeseer.ai booth at World Water-Tech to explore how clean, reliable sensor data is shaping the future of water management. From tackling data integrity challenges to driving operational efficiency, we’re bringing AI-powered insights to the forefront of the water industry.

✨ Generative AI ✨ is not only about text or image data. Imputing missing segments in time series profiles is a valuable and intriguing use case. The example below is from the Timeseer.AI data quality and observability platform 🔥.

The data we record is rarely identical to the physical parameters we aim to measure. Understanding the origin and processing of data is crucial for accurate analysis and decision-making. Here are some key factors that shape the quality of recorded data: 1) Measurement artifacts like noise, quantization, and (potentially lossy) compression influence the data transmitted and stored. 2) The nature of the signal (e.g., setpoint vs. continuous parameter) and whether data is equidistant impact interpolation and aggregation methods. 3) Distortions such as sensor malfunctions, storage issues, or pipeline glitches can skew results. One common and impactful issue is stale data—where retrieved values no longer reflect the current state of the system. Stale data, along with other quality issues, can have a variety of adverse effects: - Incorrect billing - Unreliable digital twins - Failure to scale AI/ML models - Faulty reports and dashboards - Process or asset downtime - Safety concerns - ... At Timeseer.AI, our mission is to detect and mitigate these data quality issues in a robust, accurate, and scalable way. I’d love to hear your experiences with sensor data quality challenges! Drop a comment below or give this a thumbs-up if this resonates with you.

Come meet our own Adriaan Van Horenbeek at the World Water-Tech Innovation Summit 25-26 February in London! https://lnkd.in/gk4c4zuQ

Here are our top 10 IoT/OT Data Quality and Data Engineering Predictions for 2025 🌐💡

Again an excellent content piece by David Ariens. It is worth the read and he is always open to feedback! https://lnkd.in/gd4i6eQi 

Top 10 Insights Bert Baeck Gained on the Future of Data Quality Management at MCdata Impact 2024 🔍✨

Timeseer.AI and delaware BeLux partner to enhance sensor data quality in the Microsoft Azure and Databricks ecosystem   Timeseer.AI, a leader in Operational Technology (OT) and Internet of Things (IoT) data software, aims to address the quality challenges of OT and IoT data. This data is essential for all data-driven solutions across industries and organizations, including reporting, Artificial Intelligence (AI), Machine Learning (ML), and advanced analytics. Timeseer.AI have now partnered with delaware, a global consulting company delivering advanced ICT solutions and services, guiding clients through their business and digital transformations. With partnerships including SAP, Microsoft, Salesforce, OpenText, and Databricks, delaware leverages technology partners such as Timeseer.AI to create an omnichannel customer experience.   “We are thrilled to announce our strategic partnership with delaware. They are leaders in implementing digital technologies and accelerating digital transformation for their clients. By combining our strengths, we will enable our clients to move forward with high-quality data. Stay tuned for exciting developments as we embark on this transformative journey,” comments Niels Verheijen, CRO at Timeseer.AI.   Maarten Herthoge, Solution Architect and Manager at delaware, adds: “By leveraging Timeseer.AI, we eliminate uncertainty when handling sensor data and enhance overall data quality in our clients’ data platforms. This low-code to no-code approach is understandable for all stakeholders involved, freeing up resources on both our side and the client’s side, and allowing us to focus on delivering business value with a shorter time-to-market.”   High-quality sensor data is essential for manufacturing companies, ensuring precise monitoring and control of production processes. Reliable data facilitates real-time decision-making, minimizes downtime, and enhances efficiency. In today’s data-driven landscape, the accuracy and reliability of sensor data are fundamental for maintaining a competitive edge. By combining delaware’s expertise and experience in driving digital transformation with Timeseer.AI’s advanced technology, we enable our clients to leverage superior sensor data quality, accelerating their AI initiatives and driving impactful results.   “As an advisory board member for Timeseer.AI, we are committed to helping them evolve and strengthen their position in the sensor data space. We specifically appreciate their focus on driving business outcome for customers and making data accessible. Their partnership with our long-standing partner delaware acknowledges that, and we look forward to the benefits it will bring to our customers,” adds Johan Torfs, ISV Lead at Microsoft BeLux.   This partnership marks a significant step forward in enhancing data quality and driving digital transformation for clients. Bert Baeck Adriaan Van Horenbeek Margot Neyens Dries Storme Adriaan Van Horenbeek