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End-Users and Project Haystack


End-Users and Project Haystack

Buildings and facilities can achieve superior performance by extracting insights from data. When it comes to data, it is one thing to have access to data, it’s another to make it actionable.

Device data is stored and communicated in many different formats. It has inconsistent, non-standard naming conventions, and provides very limited descriptors to enable us to understand its meaning. Without meaning, time consuming manual efforts are required before the data can be used effectively. The result is that the data from today’s devices, while technically “available”, is hard to use, thus limiting the ability for building operators to fully benefit from the value contained in the data.

 

Project Haystack methodologies describe the meaning of data using a simple, extensible data-tagging approach and standard models for common equipment systems and allow software applications to automatically consume, interpret, analyze and present data from IoT devices, smart equipment and systems. It is used in millions and millions of square feet of commercial buildings and facilities of all types and sizes, and provides a higher quality of data; reduced costs, less time to implement and reduced risk.

We are often asked, what are the benefits and value driven by Project Haystack methodology for end users? Well, there are several:

  • Enforce Best Practice Process: Insures that the data contained within systems have been understood and categorized in a standard way. For example, you can’t implement tagging of system data without having the necessary “as built” information on “what is what.” It forces that part of the process to be done.
  • Standardize Definitions: Enables users to make sense of data by defining and categorizing it, and establishing standard definitions and descriptors, so that your data can be consumed by all information systems and people in your organization.
  • Kick-Start Your Data Strategy: Sets you on the right path toward an overall data and analytics strategy because you should always start with tagging and semantic modeling.
  • Flag Data with Special Value: Permits you to choose certain types of data to classify and identify as commercially valuable and useful data.
  • Make Data Self-Describing: Provides consistency of definitions that helps reconcile the difference in terminology, as well as clarity of relationships to help resolve ambiguity and inconsistencies. Also provides clarity of data lineage as it contains information about the origins of a particular data set.
  • Avoid Vendor Lock-in: Ensures that the data within systems is available to external applications using standard, known descriptor techniques. This makes their data more portable for use with other applications and minimizes the risk of vendor lock in.
  • Maintain Ties with Legacy Naming: Allows existing point naming conventions (or point naming “disasters”) to remain in place but provides a uniform way to interpret the meaning of those points, whether it be by humans simply reading the tags on the points in a text document or tabular presentation, or by software that can automatically interpret the tags to determine the meaning of the points.
  • Prep Data Faster: Normalize data and define it in terms of what it is and what attributes it possesses. Easily query and derive reports.
  • Keep ‘All Systems’ Humming: Helps end users design well-functioning database & provides a foundation on which to build business processes. Helps navigate challenges & opportunities and drives better decisions.
  • Tap Into a Great Knowledge Base: Gain entry into a community of highly skilled and experienced professionals when it comes to the operational aspects and infrastructure of buildings and facilities.
  • Work Smarter: Enables you to see relationships and redundancies, resolve discrepancies, and integrate disparate systems so they can work together.

 

 

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