Top 3 Challenges in Preparing IoT Data for Industrial Success

Sean Kandel of Trifacta

The Internet of Things (IoT) has become integral to our daily lives, from wearables and smart watches to connected TVs and home appliances. In the business world, especially in B2B settings, connected devices—machines and sensors that track performance and maintenance—are transforming operations. For example, production lines may use sensors for predictive maintenance, while hospitals leverage IoT for remote patient monitoring, robotic surgery, and automated medication dispensing.

These expanding networks generate vast amounts of data, and handling this influx presents significant hurdles. To unlock the full potential of IoT and big data, industrial organizations must efficiently prepare heterogeneous, unstructured data. Below, we outline the top three obstacles to IoT data preparation and how modern solutions can address them.

1. Huge Volumes of Data

According to International Data Corporation (IDC), IoT devices are projected to produce 40,000 exabytes of data by 2020—over 13,000 times the global data volume in 2000. Managing such volumes exceeds the capacity of many traditional workflows, especially for manufacturers collecting billions of sensor records and internal application logs. Data preparation consumes up to 80% of a project’s time and resources; as data volumes grow, this cost escalates rapidly. Organizations must adopt scalable technologies that keep pace with the data deluge.

2. Complexity

IoT data is inherently complex, combining timestamps, geotags, and structured files like CSVs, often at high generation rates. Traditional tools—Excel, manual scripts—cannot handle this intricacy, leaving skilled analysts without the means to work with the data. Scaling internal resources is costly, and limited technical capacity can bottleneck innovation. Companies need intelligent platforms that can ingest, transform, and integrate multi‑format, high‑velocity data without excessive overhead.

3. Interoperability

Enterprise systems (CRM, ERP, marketing platforms) are not designed to seamlessly exchange or process sensor data. Integrating machine‑generated information with business applications such as Salesforce or Marketo remains cumbersome, preventing holistic analytics. A solution that enables data to “talk” across silos is essential for unlocking enterprise‑wide insights.

Data Preparation Platforms for IoT Initiatives

Organizations leading IoT projects are turning to modern data preparation platforms to overcome these challenges. With an intelligent platform, Trifacta customers have reported up to 90% reduction in data prep time and empowered non‑technical users to manage large, complex datasets. We have also partnered with Sumo Logic to provide a solution for preparing complex log data alongside business application data.

For instance, a leading European rail operator uses Trifacta to process sensor data from 8,000 locomotives across 32,000 miles of track, enabling predictive maintenance. Before the platform, data preparation was fragmented across teams and tools, delaying analysis. Now the company can prepare all complex sensor data end‑to‑end, dramatically speeding response times.

Another client, Kuecker Logistics Group (KLG), applies the Trifacta platform to vast sensor feeds from warehouses serving the world’s largest retailers. By automating data prep without hiring expensive developers, KLG has increased operational efficiency and can quickly pinpoint issues that affect the supply chain.

Conclusion

IoT data offers transformative potential, but only when backed by a robust preparation strategy. Organizations must equip teams with scalable platforms that manage volume, complexity, and interoperability. By adopting intelligent data preparation solutions, the once‑overwhelming universe of IoT data becomes a catalyst for innovation rather than a barrier.

The author is Sean Kandel, CTO and co‑founder, Trifacta.