What Is Software Omniscience?
Omniscience refers to a system’s ability to achieve awareness of its state and behaviors. Complete understanding comes from the analyses of incoming information. It is a form of data-driven intelligence that can manage and optimize itself.
In the context of IoT, such a superpower helps with organization. And organization is needed, as the sheer number of connected devices in a single system creates complexity. Such expansive networks are hard to control, especially for the limited capacities of manual human labor.
But more importantly, Omniscient software can continuously learn. Incoming data about internal behaviors and external environments leads to data-based insights. A system that can assess its own equipment performance can offer suggestions for optimization, correction, and prediction. Each suggestion is an adaptation — new evolutions that push the system to its full potential. Efficiency, security, maintenance, and decision-making all improve as a result.
How To Implement Omniscience
The design of your data architectures determines how well you can leverage the superpower of software omniscience. Use the following steps to create an efficient and usable data intelligence system.
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Collect device and system data
Performance insights come from data collected on all your connected devices and equipment. Amass both structured and unstructured information from a wide range of sources, such as:
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Device context models: temperature, video streams, noise levels, etc
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User behavior: interactions, preferences, desired speed, etc
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Operation systems activity: customer relationship software (CRM), enterprise resource planning data (ERP), resource usage information, etc
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External databases: government open data repositories (Canada, USA data.gov, Europe), commercial external data, marketplaces (Snowflake, ZoomInfo), etc
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Organize
Data only becomes meaningful when organized. Process your collected information so you can derive usable insights from all connected entities, relationships, and attributes.
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Store: Hold all data in a central repository. It allows you to combine different perspectives for a holistic view of your connected equipment ecosystem.
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Transform: Format and validate data in a canonical form. Standardized data formats facilitate interoperability and analysis.
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Denormalize: Store the same data in multiple forms to enable different query types, depending on your use cases. This can help optimize real-time querying without compromising speed or accuracy.
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Augment: Add contextual information to your raw datasets. It enriches their value. Performance changes based on context, so your data is of better use if it reflects a range of environments.
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Manage Costs
Data collection incurs costs. Cloud providers (AWS, Microsoft Azure, etc) charge you based on storage, bandwidth, and computing resources. Such expenses can affect the efficiency of your systems. As a result, you need to address component costs within your data architecture.
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Storage: Storage prices depend on the volume, type, and duration of data stored. To optimize those costs, consider using data compression techniques. Tiered storage (hot, warm, and cold) based on data access frequency or data retention policies that delete or archive less relevant data can help maintain omniscience efficiency.
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Bandwidth: Bandwidth costs are influenced by the amount of data transferred in and out of your cloud infrastructure. You can optimize bandwidth costs with efficient data transfer methods such as caching, data streaming, and delta updates. Streamlining transfers (e.g. the strategic placement of data facilities) can also reduce latency and network traffic.
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Computing: Computation costs are determined by the complexity of your data queries and processing tasks. To manage computing costs, optimize the performance of your data processing pipelines. Query optimization, indexing, materialized views, and parallel processing are all possible techniques. Be sure to leverage your cloud providers auto-scaling capabilities to dynamically adjust computing resources based on demand (only pay for what you consume).
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Design built-in security
Lastly, protect your data architecture. As you centralize and consolidate your data, it becomes a more attractive target for potential attackers. Mitigate the risks related to social engineering, unauthorized access, ransomware, denial of service attacks, and data leaks. A breach can result in an untold amount of negative fallout that destroys any possible return on investment.
Some standard security measures you can implement into your data systems:
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Regular audits: enlist the help of security experts to ensure up-to-date security best practices are in place.
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Continuous monitoring: use systems that detect and alert you to possible threats
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Encryption: scramble all data (both in transit and at rest) for extra safety.
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Access controls: Limit which users can access different aspects of the system to limit the damage if a system is compromised.
The Omniscience Superpower in Action
When implemented, you will see the visible results of the omniscience superpower across several aspects of your organization:
Business intelligence
First, you will gain a vastly improved understanding of your business operations. Data visualization will depict the entirety of your value chains. With that info, you can evaluate equipment performance, usage patterns, and potential areas of improvement. Examples include data on failure signals, times to replenish consumables, how operators use your equipment, consumer outcomes, etc.
Predictive analytics
Second, you will be able to anticipate future trends. Machine learning algorithms, statistical models, and deep learning techniques uncover hidden patterns in historical datasets that let you predict and identify upcoming market actions. Address potential issues before they escalate into critical situations. Examples include the anticipation of operational downtimes or equipment failure.
Real-time decision making & automation:
Third, you gain the ability of real-time decision-making. With the massive volume of incoming information, an omniscient system can deliver actionable insights continuously. That leads to optimal outcomes, increased value of connected equipment, and improved user experience and satisfaction.
Performance monitoring
Lastly, omniscience allows for smooth internal operations. The system will use collected data to manage itself at the ideal levels. Examples include outcome tracking on specific equipment or providing alerts for failures or outages. When performance wavers, swift action (both machine and human) can limit any potential damage from unwanted events.
Conclusion
Omniscience allows connected equipment systems to reach their full potential. Why? Because data-driven intelligence creates insights that lead to action. A knowledgeable system that can manage (and improve) its performance is highly adaptable.
Such adaptation is your superpower. Organizations with omniscient systems streamline operations, make better decisions, and predict issues proactively. Leveraging data to earn adaptability provides a competitive edge, especially as we move forward into an increasingly connected era of IoT.
