Why do Commercial Real Estate Decision-Makers Need Data Analytics?
“Data” is increasingly important to all organizations in the way that energy, raw materials and talent are. It has become an essential resource – table stakes, really – for today’s operations. And while “big data” is a buzzword that sometimes deters decision-makers from paying further attention to the topic, there are compelling reasons to look past the hype and focus on substance instead; that is, to put technology to work to extract business value from data.
Simply put, data analytics enables organizations to be more successful. Research by McKinsey & Company shows that data-centered organizations are 23 times more likely to acquire customers, six times more likely to retain customers and 19 times more likely to be profitable.
What is Big Data?
Gartner’s definition of big data, which dates back to 2001, is still considered the reference definition: “Big data is high-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation”. The proliferation of “Internet of Things” (IoT) devices in recent years has accelerated the growth and diversity of big data.
Volume, velocity and variety are often referred to as the ‘three Vs’ of big data. Here is how each specifically relates to the real estate function:
- Volume. Big data entails processing high volumes of data. In the real estate function, this data can come from a wide range of sources such as occupancy sensors, smart meters and sensor-enabled HVAC equipment, to name just a few. The volume can vary between tens of gigabytes of data for one source and hundreds of terabytes for another, depending on the size of the organization.
- Velocity. The rate at which data is generated exceeds the ability of most systems, except those dedicated to handling large volumes, to receive and process them. Velocity also refers to the ability of a business to process and make use of the results.
- Variety. Big data includes a wide array of data sources and types, some of which are less structured than more traditional numeric data. Most big data used in the real estate function is structured, but unstructured and semi-structured data types such as audio, email, and video also exist and require special handling.
A connected lighting system that acquires occupancy and daylight data, using fixture-integrated passive infrared and photo sensors, is a reliable source of big data in commercial real estate. With ubiquitous sensing throughout a space, a smart lighting system is the perfect conduit for collecting data on what is happening in the building at any given time. Sensors collect all sorts of data in a space including changes in daylight level and occupancy.
What Is Data Analytics?
Data analytics is core to enabling data-driven decision making. It is a process that involves collecting data to populate key performance indicators (KPIs); then analyzing patterns in these KPIs and utilizing them to draw conclusions and make decisions that drive business performance. In essence, data analytics powers data-driven decision making by leveraging verified, analyzed data to reach decisions rather than using intuition or guesswork.
To be able to extract genuine value from data, it must be accurate as well as relevant. Collecting, extracting, formatting and analyzing insights for enhanced data-driven decision making in business was once very burdensome and took a long time.
Today, the availability and democratization of generic and specialized analytics software empowers users without extensive technical expertise to extract insights from data.
IT now invests in building and maintaining such systems so that less time and resources are required for producing reports, trends, visualizations and insights. Extracting insights from big data as a best practice is critical with reporting, dashboards, advanced visualization, end-user “self-service” and data warehousing identified as the top five technologies and initiatives strategic to business intelligence. According to a BARC research report, using big data is associated with a profit increase of 8-10% and a 10% reduction in overall costs. And Forbes estimates that the growing demand for global market data analytics and business intelligence services may boost revenues north of $200 billion in 2020.
Why Does Data Analytics Matter?
The value of data-driven decisions comes from developing a consistent methodology for continuous improvement. Companies that use analytics can grow, evolve and adapt; they do not necessarily need to solve all problems from day one. Collaborative decision making based on real data, and democratized data availability and tools, is inherently more robust and verifiable. Companies can therefore learn from their mistakes and enhance their processes iteratively.
Learn how smart lighting data and data analytics was used to optimize space in Mt. Royal University by downloading the guide below.
This blog post is the first in a 2-part series about Data Analytics in Commercial Real Estate (CRE). In a future blog post, we will provide a checklist for implementing or improving a data analytics program in CRE. If you are a subscriber to the Digital Systems Blog, you will automatically receive notification of this post in your email box.