Strategic Value of High-Resolution Data - Praxis
Strategic Value of High-Resolution Data

Strategic Value of High-Resolution Data

Today’s imperative – of driving innovation and strategic decision-making through data – calls for a nuanced understanding of the implications of high-resolution data on software development, paramount for leading organisations into the future.


Data comes in many forms and varieties, continually expanding in complexity and application. High-resolution data, characterised by its detailed and precise nature, plays a significant role in modern software development. This type of data includes rich information streams related to images, movements, sounds, and other unstructured elements that organisations are now structuring and analysing to drive value.

In an era where data-intensive applications are becoming the norm, businesses must harness high-resolution data to stay competitive. This data enables organisations to operate at higher levels of precision, offering insights that were previously unattainable. The importance of high-resolution data can be seen across multiple industries, but it is particularly impactful in the Internet of Things (IoT) and AI domains.

The IoT ecosystem is actually a prime example of where high-resolution data thrives. Modern cars, for instance, are equipped with hundreds of IoT sensors that generate vast amounts of data. This data can be used to monitor vehicle performance, predict maintenance needs, and enhance user experiences through advanced AI applications.

Evan Kaplan, CEO of InfluxData, a company specialising in providing time-stamped data to clients like Cisco, IBM and PayPal, in a recent interview with Forbes highlighted the critical role of high-resolution data in AI: “AI is only as strong as the data that powers it. Real-world AI applications, which focus on practical, everyday scenarios, rely heavily on high-resolution data to drive predictive analytics, forecasting, and anomaly detection.”

Precision and Scale: Managing High-Resolution Data

The challenge with high-resolution data lies in its sheer volume and velocity. Platforms designed to handle this data at scale are essential for fuelling AI models that require real-time or time-series data. These platforms can manage data down to nano-second precision, enabling fully autonomous systems that continuously enhance their intelligence through data streams from various sensors.

Kaplan explains, “Every Internet-connected device generates a continuous flow of time-series data. AI uses this data to analyse historical patterns, model behaviours, and make predictions. This is an example of real-world AI that creates intelligence at scale via automated data collection, enabling systems to forecast outcomes and respond effectively.”

Understanding the strategic implications of high-resolution data is crucial. This knowledge can be applied in numerous ways to drive business value:

  • Data-Driven Decision-Making: Leveraging high-resolution data allows for more informed decision-making processes, leading to better business outcomes.
  • Innovation and Product Development: High-resolution data can inspire new product features and innovations, providing a competitive edge in the market.
  • Operational Efficiency: By utilising high-resolution data, organisations can optimise operations, predict maintenance needs, and reduce downtime.
  • Customer Experience: Enhanced data allows for personalised customer experiences, improving satisfaction and loyalty.

Overcoming Challenges

One of the significant challenges with high-resolution data is managing its high cardinality, which refers to the number of unique values a dataset can contain. In environments like a McLaren Formula One car, sensors capture 50 distinct data points every millisecond, leading to exponential growth in high-cardinality data.

To address this, organisations are turning to columnar databases that facilitate near-real-time querying and economise disk space. These databases are optimised for handling high-cardinality data efficiently, ensuring that businesses can derive actionable insights without being overwhelmed by data volume. The continuous evolution of data and AI models however necessitates regular updates and monitoring to maintain effectiveness.

Kaplan notes, “The substantial data output from sensors can be expensive to retain, prompting organisations to devise strategies for managing older data. Transforming data and summarising second-by-second analysis instead of millisecond intervals can help mitigate storage costs and retain only the most valuable information.”

As we look towards the future, the role of high-resolution data will only become more prominent. The proliferation of sensors and connected devices is generating increasingly granular data, creating unique management challenges but also offering unprecedented opportunities. Businesses that can effectively harness and analyse high-resolution data will be well-positioned to lead in their industries.


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