The challenges of working with large scale spatiotemporal data

Machine data is growing faster than we can manage

As the world evolves, we are surrounded by an ever-increasing number of devices that track movement. Vehicles, GPS trackers, smartphones, sensors and IoT all generate a constant stream of spatiotemporal data that changes across locations over time. This data accumulates fast and while some may be actively collected and analyzed, a substantial portion is under-utilized. For organizations to unlock the true potential of their data, they need to address several challenges:

Variety of data: Spatiotemporal data is often collected by different devices and stored in different locations and inconsistent formats.  The interoperability and integration between these data silos makes it difficult to aggregate and process effectively.

Volume of data: Traditional solutions were not designed to cope with the sheer volume, variety and velocity of spatiotemporal data. As data accumulates, ingestion, processing and storage become slow and expensive. Users may accept the latency, restrict their reporting or attempt to find workarounds, neither of which efficiently contribute to the goal of extracting timely, accurate insights for decision-making.

Streaming data: Real-time insights from streaming live data has the potential to transform operational effectiveness and customer satisfaction but enterprise architecture and data workflows do not always address the needs of users that want to work with data streams:

• The speed of ingestion may cause bottlenecks
• Users may not be able to index several small records at speed
• Unpredictable distribution of data and query loads can cause storage hotspots
• It can be time-consuming to search across multiple columns and attributes at scale
• Storage may not cache efficiently

Data Quality: Not all collected data meets the desired quality standards for analysis or decision-making. Inaccuracies, missing values, or inconsistencies in the data can limit its usability. See our separate blog: Challenges working with Skewness and Noise

Data Privacy: Spatiotemporal data often contains sensitive information, such as personal locations or activities. Local laws and concerns about privacy and security can limit the sharing and utilization of the data.

Lack of Awareness: In some cases, organizations or individuals may not be aware of the potential value of the spatiotemporal data they possess or can access. They may not have the necessary tools, skills, or knowledge to extract meaningful insights.

‍Productivity: When systems are not performant under certain conditions, such as dealing with high velocity data flows or large scale workloads, user performance is impacted too. A simple business question such as 'How can we shorten the delivery window for customers or make more regular updates to status?' can turn into a protracted project that is uneconomical to run in real-time.

Using purpose-designed technology can overcome some of these challenges. See our separate blog: Why spatiotemporal data needs new technology

In Conclusion

Organizations and individuals looking to extract the true potential of their data and deliver more value to the business need to keep an open mind to the possibilities while simultaneously addressing the obstacles to success, be that resources, skills, systems, data or investments.    

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