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Common Building Data Infrastructure Challenges and How to Overcome Them

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Patrick Gilhooly

Data infrastructure can be thought of as a network of roads along which data travels. Poor infrastructure is like trying to drive on roads with missing street signs, toll booths, traffic lights that don’t work, dilapidated bridges, and random roadblocks. These obstacles can make traffic grind to a halt, or even get lost on the way to its destination.

Data requires a well-designed and properly maintained infrastructure that allows for seamless data flow. Data infrastructure challenges often result from simple mismanagement or poor planning that leads to delays and sometimes even failures of the network. 

In a smart building, quick retrieval and utilization of building data from smart sensors and other devices depend on the infrastructure through which data flows and in which the data is stored. Making the most of smart technology requires removing any obstacles in your building data infrastructure and supporting optimal data handling.

Data Infrastructure Challenges in Smart Buildings

A smart building is made up of sophisticated systems of hardware, software, wired and wireless networks, and cloud-based servers. A strong data infrastructure will boost efficiency and productivity while also promoting interoperability and collaboration. However, data infrastructure challenges can greatly diminish the capabilities of your building.

Common data infrastructure challenges include:

  • Data transformation problems
  • Slow connectivity
  • Poor scalability
  • Insufficient open-source data capabilities

Other issues may evolve from the above challenges, causing delays or even preventing building data from adding value. To minimize risk, systems integrators should use proper definitions and tagging standards when mapping data infrastructure. 

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Data Transformation Problems

Properly formatting data allows users to analyze and share it easily. Software applications often automatically restructure data in ways that work best for them, rarely considering how this affects interactions between hardware and software. In other cases, human error compromises data during manual transformation. Automating data transformation in a way that supports the overall goals of your smart building ecosystem creates better data quality and ensures your data infrastructure can handle the big data generated by your building.

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Slow Network Connectivity

Slow network connectivity is one of the more frustrating data infrastructure challenges. Key factors to consider include:

  • The computing capabilities of connected devices affect how quickly they connect to networks
  • Connected devices such as smart sensors or controllers use data infrastructure to communicate wirelessly with the router and each other, which may cause interference and result in slower Internet speeds for all connected devices 
  • Greater competition for bandwidth slows network speeds
  • Incompatible routers negate the ability of a network to transmit data quickly

There are several ways to improve connectivity. Simply closing unnecessary apps, disabling automatic updates, disconnecting devices not actively used, and altering the Wi-Fi router’s channel can speed up connections. Installing a Wi-Fi repeater will also increase coverage and make wireless connections more stable, resulting in faster and more reliable connections. However, one of the best and most reliable ways to address slow network connectivity is by installing a fiber optic backbone network.

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Poor Scalability

Poorly-designed infrastructure won’t be able to deal with the volume of data generated by building equipment, smart devices, and sensors. These data infrastructure challenges make data flows unpredictable and sometimes problematic, creating difficulties for system integrators when scaling up or down systems. 

Smart building workflows rely on multiple stages of big data analysis, which puts high demands on networks, servers, and storage systems. While developing network capacity, better processing for raw data, and expanding storage will help, they don’t replace good data infrastructure. In fact, having the right data infrastructure is the single best way to enhance performance and flexibility.

Consider it from a networking perspective. Analyzing big data and creating virtual servers alters how data flows through the network. These data flows may cause servers to become congested, which then impacts network performance. To resolve this, network integrators often just add more links, which also increases the number of switch ports needed and creates an additional administrative burden.  

To support scalability, data infrastructure design should include: 

  • Data optimization
  • New storage platform 
  • Specifications for routing and switching elements
  • Virtualization 
  • Wireless access

This model offers considerable flexibility and scalability by moving data to a hybrid, private, or public cloud. This ensures both capacity and performance are scaled and allows for better utilization of resources. It also integrates these elements while optimizing workflow. Cloud solutions work particularly well when it comes to scalability, with public cloud service providers handling the more technical phases of scaling. However, you must make still decisions about the extent of scaling and scale network resources accordingly. 

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Insufficient Open-source Data Capabilities

Modern smart buildings rely on a healthy open-source ecosystem to provide interoperability between products from different developers and manufacturers. With open-source data infrastructure, challenges come up most frequently with maintenance. A key issue with open-source tools is their lack of structure when adopted and lack of ongoing support. This means updates and patches often aren’t done expediently, leaving systems vulnerable

Open-source software—and hardware that utilizes open-source software—performs best when regularly updated and maintained. This requires dedicated IT professionals within the organization or a digital services vendor to fix bugs and add new features. While previously open-source tools were only made for other developers, now many of these products are available off-the-shelf. Yet they still require continuous maintenance to make sure they perform at their peak.  

Using the OAP to Resolve Data Infrastructure Challenges

The Ontology Alignment Project (OAP) was born out of the need to streamline integration of new devices and systems in smart buildings. The OAP helps bridge a gap within the building automation sector on how data is defined and tagged. These open-source standards can be easily applied, enabling better use of fault detection and diagnostics, analytics, and user interfaces. 

The variability found within the industry regarding defining and tagging building data creates barriers when used for real-world applications. For intelligent built environments to thrive, ontologies need to align with a uniform data model and relationships need to conform easily so that the infrastructure can evolve quickly. As an open-source solution, the OAP standardizes definitions and tags to streamline communications, eliminate common data infrastructure challenges, and help you use smart technologies to their full potential.

Buildings IOT offers state-of-the-art services and products necessary to resolve data infrastructure challenges in built environments. Contact our team of experts to learn more about what we can do for you.

 

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