The Entropy of Institutional Knowledge

The Hidden Challenge

In the world of hardware engineering, there’s a silent and costly problem: the loss of institutional knowledge. Insights gained during research and development often vanish, slipping through the cracks of fragmented documentation, expert departures, and incomplete workflows. This loss isn’t theoretical; it’s akin to the inefficiency of jet engines where only a fraction of fuel energy propels the aircraft forward. Similarly, only a small portion of critical knowledge is captured and retained.

For hardware engineers, this inefficiency leads to prolonged development cycles, mission failures, and untapped opportunities. Imagine if this knowledge could be captured at its inception, stored as an enduring resource, and seamlessly shared across teams.

The Scale and Impact of Knowledge Loss

In-house observability solutions come with hidden costs that extend beyond their initial implementation. According to Sift’s 2024 Aerospace Report, 77% of respondents identified the loss of expertise and inefficiencies as significant consequences of maintaining custom systems. These challenges include:

• Knowledge Retention Issues: When team members leave or transition to new roles, critical knowledge about internally built tools often disappears, leaving gaps that hinder progress.
• Training Overhead: New hires face steep learning curves, requiring significant time and resources to get up to speed on custom in-house systems.
• Higher Costs: Time spent managing in-house tools and rediscovering lost insights diverts focus from advancing core projects or new innovations.
• Slower Time-to-Market: Teams spend excessive time fixing past errors instead of pushing forward, delaying product launches.
• Stifled Collaboration: Knowledge silos hinder cooperation and progress.

Like energy losses during conversions—where some input energy is inevitably dissipated as heat or other unusable forms—knowledge loss in R&D is a systemic issue. Without active measures to capture and retain expertise, critical insights fade over time, creating inefficiencies and missed opportunities that compound throughout an organization.

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Traditional tools used in hardware R&D are poorly equipped to handle the unique challenges of modern machines

The Shortcomings of Traditional IT Observability Tools

Conventional monitoring tools like Grafana are ill-equipped to meet the unique demands of modern machines. They provide surface-level metrics but fail to address deeper needs. Consider the limitations:

Static Monitoring and Dashboards

Equivalent to a check engine light, these tools lack diagnostic depth. Identifying the root cause of an engine failure, for example, requires analyzing thousands of data points across multiple channels—something traditional IT tools can’t handle.

• Data Overload: These tools generate endless streams of uncorrelated alerts that often result in “dashboard fatigue.” Engineers are forced to sift through mountains of raw data to identify meaningful patterns.
• Siloed Insights: Insights are often saved in one-off dashboards that grow exponentially in number, creating clutter and taxing database resources. This makes it nearly impossible to maintain a coherent narrative of what’s been learned.
• Poor Scalability for Live Operations: Dashboards polling live telemetry data place a heavy query load on databases, especially during mission-critical events. To avoid system crashes, teams are often told to avoid using them during high-stakes operations.

Fragmented Documentation and Knowledge Silos

Institutional knowledge often resides in fragmented systems—emails, spreadsheets, and ad hoc notes. This creates an environment where expertise is scattered and inconsistent.

• Lack of Context: Existing tools don’t capture the rich context engineers gain during their normal course of work. Documentation is often detached from the real-time workflows where knowledge is created, leading to stale, inaccurate records.
• Non-Searchable Repositories: Even when documentation exists, it’s rarely organized into a searchable, accessible repository. Engineers waste valuable time hunting for past insights or simply start from scratch.
• Loss of Expertise: When experienced engineers leave, the nuances of their understanding often go with them. These gaps compound over time, leaving organizations perpetually chasing their own tails.

Complex Data Types and Schema Challenges

Hardware systems generate diverse telemetry data types, like enums and analog signals, which traditional IT tools struggle to support. Dynamic schemas evolve with every iteration, requiring adaptable solutions.

• Diverse Data Types: Hardware telemetry involves complex data types like enums, bit fields, and analog signals, which IT tools can’t easily support. 
• Dynamic Schemas: Hardware subsystems change with every iteration, often requiring schema changes. Traditional databases struggle to adapt, demanding manual migrations that slow teams down.
• High Sampling Rates: Hardware systems can generate thousands of data points per second. IT tools are designed for downsampled, longitudinal data, which is insufficient for diagnosing critical issues like engine failures or sensor malfunctions.

Time Synchronization Challenges

In hardware systems, time is a critical variable. Subsystems operate on independent clocks, and some may even function in environments like orbit, where time dilation becomes a factor.

• Lack of Precision: IT tools don’t account for the need to precisely align time-series data across subsystems.
• Fragmented Timelines: Without synchronized data, it becomes impossible to correlate events across a machine’s various components, leaving engineers guessing at cause and effect.

Retention and Storage Limitations

Compliance and testing workflows in hardware development demand the ability to store and retrieve data for years—or even decades. Most IT tools aren’t designed for this level of retention.

• Short-Term Data Use: IT observability tools are optimized for ephemeral data, discarding information after it’s deemed no longer relevant.
• High Storage Costs: For hardware companies, retaining years of telemetry data in traditional databases is prohibitively expensive. 

Inaccessible Interfaces for Hardware Engineers

Many IT observability and monitoring tools require a background in coding in Python or query languages like SQL, making them inaccessible to many hardware engineers and technicians.

• High Barrier to Entry: Non-software engineers, who may be the most knowledgeable about a machine’s performance envelope, are unable to interact with these tools effectively.
• Workarounds Create Frustration: To accommodate these limitations, engineers rely on workarounds that are inefficient and brittle, such as exporting telemetry data into spreadsheets for manual analysis.

Monitoring tools were designed for a different era and a different purpose—they aren’t equipped to handle the dynamic, data-intensive demands of modern hardware R&D. They deliver static snapshots instead of dynamic insights, creating bottlenecks in the discovery and retention of institutional knowledge. (More on this topic: The Observabiity Odyssey)

Monitoring tools were designed for a different era and a different purpose—they aren’t equipped to handle the dynamic, data-intensive demands of modern hardware R&D.

Empowering Engineers

Sift takes a fundamentally different approach by empowering hardware engineers—the people with the deepest understanding of their machines’ performance envelopes—to directly instrument their systems and codify their expertise into opinionated workflows. This ensures critical knowledge is captured, shared, and effectively applied across teams. Here’s how Sift makes this possible:

• Bridging the Gap Between IT Observability and Hardware Needs: Sift is purpose-built to handle the unique challenges of hardware systems, including high-frequency telemetry, dynamic schemas, and time-aligned data. Unlike IT tools designed for software data, Sift meets the demands of hardware development and operations head-on.
• No-Code Instrumentation: Sift’s no-code approach allows engineers to define stateful rules and other operational parameters, embedding their expertise into workflows without the need for programming skills. This democratizes access to powerful observability tools, putting control in the hands of those who know their systems best.
• Real-Time, Multi-Channel Analysis: With Sift, engineers can correlate telemetry across subsystems in real time, uncovering anomalies and embedding actionable insights directly within their workflows. This capability accelerates problem resolution and ensures critical events don’t go unnoticed.
• Capture Knowledge In-Line with Data: Sift’s opinionated workflows ensure institutional knowledge is embedded directly alongside telemetry data. Similar to in-line code comments, this approach keeps insights accurate, contextually relevant, and readily accessible—eliminating the inefficiencies of disconnected documentation.
• Foster a Collaborative Knowledge Base: Sift breaks down silos by enabling teams to annotate data, share insights, and codify rules. The result is a continually growing knowledge base that compounds in value with every iteration, improving collaboration and ensuring expertise is available to all.

The Impact of Stateful Rules on Knowledge Retention

Stateful rules are central to Sift’s approach for preserving and operationalizing institutional knowledge. They align time-series data, correlate events, and embed contextual insights directly into workflows. These rules adapt to the changing behaviors of complex aerospace systems, redefining how teams interact with their data. Learn more about stateful rules here. 

Reshaping the R&D Landscape

The challenges of knowledge silos and fragmented workflows are often invisible until they cause delays, errors, or inefficiencies. Sift tackles these bottlenecks head-on, reshaping how hardware engineering teams approach R&D. By embedding institutional knowledge directly into workflows, Sift equips teams to operate with greater precision and confidence.

The broader implications can be significant. Sift’s tools help teams build faster and with fewer errors by surfacing actionable insights in real time, minimizing the risks associated with knowledge loss. By creating a culture of shared intelligence, Sift ensures that institutional knowledge becomes a compounding resource instead of a fleeting asset.

Sift’s integration is seamless, meeting engineers where they are without disrupting their existing workflows. It’s not just a new tool—it’s a step forward in how teams retain, share, and apply their expertise, ensuring that hardware organizations remain agile in the face of growing complexity.

By creating a culture of shared intelligence, Sift ensures that institutional knowledge becomes a compounding resource instead of a fleeting asset.

Harnessing Knowledge to Advance R&D

Institutional knowledge loss is a critical yet often overlooked inefficiency. Left unaddressed, it stifles innovation, slows progress, and adds unnecessary costs to hardware development. Sift turns this inefficiency into an opportunity—capturing expertise at its source and making it accessible and actionable for the entire organization.

By enabling engineers to codify their insights and embed them directly into workflows, Sift unlocks untapped potential within R&D teams. The result is a culture of continuous improvement, where knowledge is preserved, shared, and enhanced over time.

What could your team achieve if 90% of your institutional knowledge were preserved and actionable? With Sift, it’s more than a hypothetical—it’s the foundation for a smarter, more resilient approach to building the future of hardware. Schedule some time with our engineering team here.