Smart Buildings

Big Data: From Qualitative Guesses to Quantitative Proof

“Big data is like teenage sex, everyone talks about it, nobody knows how to do it, everyone thinks everyone else is doing it, so everyone claims they do it” said Daniel Utges, Product Director at DEXMA during Memoori’s second webinar of 2017, sponsored by Project Haystack. A more professional definition may be that big data is a very large volume of information, coming from many different sources, normally in real-time, in both a structured and unstructured format, suggested Utges. This mass of information creates a number of challenges but also important benefits. It allows us to go from qualitative guesses to quantitative proof, and from reactive to proactive decisions. In our recent report The Internet of Things in Smart Commercial Buildings 2016 to 2021 we estimate that the market for Big Data and Cloud Based Software and Services in Smart Buildings will grow from $10 Billion in 2016 to around $32 Billion by 2021. Utges […]

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“Big data is like teenage sex, everyone talks about it, nobody knows how to do it, everyone thinks everyone else is doing it, so everyone claims they do it” said Daniel Utges, Product Director at DEXMA during Memoori’s second webinar of 2017, sponsored by Project Haystack.

A more professional definition may be that big data is a very large volume of information, coming from many different sources, normally in real-time, in both a structured and unstructured format, suggested Utges. This mass of information creates a number of challenges but also important benefits. It allows us to go from qualitative guesses to quantitative proof, and from reactive to proactive decisions.

In our recent report The Internet of Things in Smart Commercial Buildings 2016 to 2021 we estimate that the market for Big Data and Cloud Based Software and Services in Smart Buildings will grow from $10 Billion in 2016 to around $32 Billion by 2021.

Utges claims the key big data question from smart building customers is “how much money will I save?” referring to the return on investment (ROI) for customers choosing to adopt big data technologies. However, what Utges and the team at DEXMA are cleverly doing, is using big data itself to answer this question. In doing so they are able to quickly and accurately calculate the benefits of big data for any customer.

DEXMA's EnergyGrader product has a database consisting of 50,000 buildings, 26 different activities, creating more than 29 billion readings for 30 billion square meters – “a jungle of energy efficiency” says Utges. By selecting the most similar buildings from their database for any new customer, DEXMA is able to draw from relevant examples to provide accurate ROI forecasts and tailored energy efficiency strategies.

A new customer’s building may be in Barcelona, for example, but the most relevant buildings to draw comparison with may be from anywhere in the world, he adds. Their system will correlate against the function of the building, the type of façade or a number of other factors not limited by geographic location.

Is big data about collecting all possible information, how much of that data is actually useful and isn’t there an energy cost for storing that mass of data? - Utges was asked during our webinar.

“As a new customer you generally want to collect data on everything, then over time the useful data can be refined. An industrial project, for example, may focus on thermal energy measurements, so the entropy or temperature difference between the inlet and outlet maybe the key information, another customer may prioritize a different set of information, it all depends on the retrofit being provided,” Utges explained.

Beyond energy efficiency and the associated cost savings, a facility managers that DEXMA works with may also have certain ‘happiness KPIs’ that also create value from big data. “Chairs must be comfortable, the indoor environment match the occupants preferences and so on,” Utges said. For these purposes DEXMA generally work with control systems, and therefore ‘small data’ rather than big.

Big data also plays heavily into the development of artificial intelligence. Utges believes that the future is not one of humans monitoring and assessing big data but one where we simply pose questions to the system. An energy manager may ask a chat bot “which buildings should I focus on this week?” and it would say “do this and this in these buildings.” Going further, an AI system may just set up appointments and schedule retrofits on it’s own.

Eventually AI will replace many of the tasks that energy and facility managers undertake, but rather than making these positions obsolete, Utges suggests that this will free up human minds for what they do best. “An AI system could really speed up you’re your day-to-day tasks, allowing you to avoid all of your administration issues for example, and just focus on decision making,” he said.

Looking forward Utges is very excited about energy efficiency technology, “10 years ago energy efficiency wasn’t as sexy as it is today and electricity prices weren’t as high as they are today, he said. He believes big data is central to developing much needed energy efficiency of the future, “we’d really like to know what happens every second of everyday, everywhere,” big data is the key to making this a reality.

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