The buzz on the word data may feel recent, but, on the contrary, historians state that a certain process of collecting data had been there in practice since 19,000 BC. They recorded certain information or counts by scratching onto an Ishango bone. Towards the third millennia BC, the recordings were done in the form of writings on various surfaces, and libraries were formed for mass storage of such recorded information or otherwise called data.

In the early 1600s the first use of the word “data” was recorded, and historians find it as a word derived from Latin meaning “a fact given or granted”. As the data collection progressed, they understood the need for analysis and in 1663, John Graunt initiated the analysis of death records kept by the London localities.

History highlights that the major improvement in data collection and analysis happened during the 1800s. One of the most significant occurrences was the 1880 US Census where the data collated was much more than what could be analyzed. This led to the introduction of various tools and machinery to enable the processing of this large amount of data. In the 1900s another challenge was of storage of data, this was again solved using magnetic data storage and eventually moving to digitalization. And in this century, it has taken more significance and a tremendous process has been achieved through automation, IoT, and so forth.

In this day and age, data is used in every walk of life. Today’s businesses make the most crucial decisions based on data analytics. Even the word data science had become extremely popular in the last decade. Nowadays universities are offering post-graduate programs in data science and analytics, this highlights the significance of data collection, analysis, and using the analysis for decision making.

What we infer from evaluating various white papers and surveys is, data analytics led to faster and more effective decision-making in organizations (2020 Global State of Enterprise Analytics report) and this has also helped in improving:

• Efficiency
• Financial gains
• Diversification of offerings
• Customer service
• Competition

If we evaluate the above high-level benefits of data analysis and link it with Facilities Management, it's a no-brainer that data-based decision-making is key to the success of any FM operation. Another realization is that most of us in this field of business are already practicing data collection and analytics in various methods. It's not a necessity to have the most modern and state-of-the-art tools to get the benefit, rather use of basic tools such as Excel, Power BI, google charts, etc. can also help arrive at building a strong analytics framework. The key here is to identify the key parameters required, the objective of the analysis, and how the data collection process is set to happen.

In an article published by Harward Business School, they have identified 4 key types of data analytics and they are furnished below:

1. Descriptive Analytics

This type of analysis will help you analyze “what happened”. In the FM world, the question of what happened is very common and finding answers to this can be achieved using this method. This method is practiced by pulling trends from the collated data over some time.

In case of cooling issues in the building, it is a standard practice for us to evaluate the trends in HVAC equipment operations throughout the various times in a day and months. This is normally achieved through the recordings the technical team has put together or through the BMS. The analytics of this data will help us understand what happened.

2. Diagnostic Analytics

The second type of analytics is to help you find “why did this happen”. This can be achieved by comparing related trends, evaluating correlations between variables, and identifying the relationships where possible.

Looking at a cooling issue again, we use the data to evaluate the loads required during various stages of the building occupancy vs climatic conditions, design vs actual equipment performance, overuse or underuse, etc.

3. Predictive Analytics 

This method is used to find “what might happen in the future”. Now we use the latest IoT devices to collect data and AI to analyze the future operations of the types of equipment and even their failure rates.

Predictive analysis is getting better and better as days progress and this is not limited to maintenance but also business improvements for the future.

4. Prescriptive Analysis

This method is the last of the four key types of analytics that helps us answer the question “What Should we do next”. This type of analytics is performed by taking into account all possible factors in a scenario and suggesting actionable summaries.

This is the most effective type of analytics, especially for data-driven decisions.

As we already have the data, now the decision is ours to make sure how to utilize the information we have and to what extent we as the FMs should be equipped for the future.

This article was written by Fahad Mohamed, Director – Dubai & Northern Emirates, Adeeb Group, as part of the Expert Talk series.