The age of smart buildings is creating a data boom for facilities managers. Now, there’s more data available than many companies know what to do with, and the single biggest hurdle in understanding that data is getting a handle on it. Workplace data management is quickly becoming paramount, and it presents a new challenge for companies. How, exactly, do they coordinate and manage data at-scale?

Identify and define data sources

The first and most essential step in managing data is identifying it. This means looking at all the IoT devices active within the workplace, as well as other smart building components that generate data. Next comes employee input data—things like support tickets, room reservations, and log data. Don’t forget about integrated software, either. This task in and of itself can be cumbersome, but it’s necessary to identify data before you can use it.

Consider tracing vital business operations backward. Where do you get the data to do X, Y, and Z? Identify the primary sources responsible for creating data and define their purpose. This will make the next step in data management simpler.

De-silo and aggregate data

Organizational data is, by definition, siloed. But it shouldn’t be. To make data shareable across the organization and promote better utilization of it, companies need to adopt data lakes and warehouses.

A data lake is a simple repository where unsorted, raw data is collected. Warehouses are where it goes to be sorted and stored, until it’s accessed by applications and people who need it. These systems are part of the broader business cloud—cloud connectivity is key in allowing data to flow freely from its input sources, no matter where they are in the organization.

Clean, organize and store data

Data without context is useless. What is a workplace data management strategy without markers and identifiers that validate the data? From warehousing, data needs to be cleaned, organized, and accessible:

  • Cleaning data means turning it into a source of truth: removing duplicate or antiquated entries, formatting it uniformly, bringing together like-kind data, etc.
  • Organizing data involves giving it labels, qualifiers, quantifiers, and structure that’s consistent with how people and systems will access it.
  • Making data accessible means putting it into a warehouse repository that’s accessible and stable, with automations and integrations built-in to keep data fresh.

This stage requires the most technical expertise—data experts who understand Extract, Transform, and Load (ETL) data operations or who can configure software like an iPaaS to improve data fluidity.

Develop architecture and deploy data

By this stage of a digital data management process, the architecture largely exists. Each stage of the process has started to take shape based on the infrastructure needed to handle data:

  • Raw data feeds into data lakes
  • Data is cleaned and sorted into data warehouses
  • iPaaS and integrations make warehoused data widely available

For larger companies with more robust data resources or applications, there are other parts and pieces of infrastructure that may or may not become necessary:

  • Data catalogs that pre-format data for quick accessibility
  • Data automations that handle data without human intervention
  • Machine learning that orchestrates and reports insights

The result in most cases is a strong infrastructure that brings data to the forefront of different applications used for decision-making—Computer-Aided Facility Management (CAFM), Enterprise Asset Management (EAM), and Integrated Facilities Management Systems (IWMS). Most people won’t see the hamster spinning on the wheel to bring them data—they’ll just get the insights they need.

Protect and secure data

No conversation about digital data management would be complete without heavy emphasis on cybersecurity. The more transactions data has between collection and application, the more opportunities there are for malicious action. Companies need to protect their data from all angles:

  • At the point of collection (ex. IoT cybersecurity)
  • At the point of transmission (ex. SSL connections)
  • At the point of storage (ex. Network security)
  • At the point of access (ex. Employee credentials)
  • In the peripheral (ex. Integrations and connections)

Cybersecurity starts with cognitive efforts to protect data. Use software that defends against cyberattacks. Educate employees about cybersecurity best practices. Create auditing processes for your data management system. Good habits and mindfulness, coupled with some common sense, ensure data remains secure.

Every company is a data company

Every company generates data. As they learn more about how to use workplace data effectively, they also need to recognize the importance of data management. That means building out a strong data management infrastructure and protecting it from all angles. The easier and safer it is for data to travel across your organization, from one important touchpoint to the next, the more valuable that data becomes in decision-making at every level.

Keep reading: The Top Challenges for Creating Smart Buildings

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Jonathan writes about asset management, maintenance software, and SaaS solutions in his role as a digital content creator at Eptura. He covers trends across industries, including fleet, manufacturing, healthcare, and hospitality, with a focus on delivering thought leadership with actionable insights. Earlier in his career, he wrote textbooks, edited NPC dialogue for video games, and taught English as a foreign language. He hold a master's degree in journalism.