Major commercial buildings use the same amount of energy as over 500 houses. So trying to save energy in commercial buildings is key. But most building management systems (BMS) are trying to find a needle in the haystack, and wasting time doing so.
But new, cloud-based systems are finding the needle in the haystack much quicker than ever imagined. Many UK sites are starting to adopt new data analytics programs and systems, saving something like 1 1/2 million Pounds each year. So, those that have it are saving energy, saving the environment and saving cash.
There are high hopes for the future; thousands of sites will have this data capability. Thus, they will be able save the amount of energy of an entire power house.
Go Green and Save Green. Adopt a data analytics strategy in your company for massive savings.
Doesn’t it frustrate you when you buy an airline ticket to go home for the holidays, only to find out that your younger brother, King of procrastination bought his plane ticket a day before your flight departed (4 months after you), and he ended up paying less?
It is this very reason that Farecast was created in 2003. They understood that because of the internet, fixed pricing was a thing of the past; but now prices are so volatile that there’s almost no way to decide when it will be cheapest. Farecast solved this problem, establishing the motto “Know When to Buy.”
So how much data needs to be mined in order to predict when fares will be cheapest? According to the interview in this video, hundreds of billions of price observations that have been gathered over more than 8 years. This data is crunched to find patterns; both short and long term. Should I buy on Monday? Should I buy in the Spring?
These data predictions are about 70-80% accurate (this is more accurate than the weatherman!). This is the case because they gather more than just historical data. They gather unstructured data on rumors, gossip, blogs, PR announcements and more to come up with more accurate predictions.
This same principle can be taken into pretty much any industry, just as big data analytics can help businesses in just about any industry.
It seems that with the rise of the Big Data Revolution, data creation and data analysis is being utilized in almost every facet of our lives, our devices and our businesses.
Where is it likely to pop up next? The author of this article, Aleah Radovich predicts that it will soon be saving recruiters more than just time! Big Data can be utilized to find “…a match made in heaven.” Companies can identify the perfect candidates, and even candidates that may be more likely to accept an offer.
‘”People that watched ‘Skyfall’ were also interested in ‘The Bourne Identity.'”
We receive suggestions on what movies to watch, what we might like to listen to next and what we would like to buy. Five years ago, it was Amazon alone that used the recommendation system. Today, every company who is a somebody is using it.
So, what Amazon and Netflix do today, we may very well see human resources departments in every company doing tomorrow. The process has always relied upon referrals and recommendations, but now these recommendations are likely to come from our computer screens. We may statements like: Companies interested in Person X were also interested in Person Y.”
There are roughly 7.6 million animals admitted to shelters in the United States each year. The same number are adopted as are euthanized: 2.7 million. So many are euthanized due to limited resources; many animals are coming in, but there is simply not enough space and not enough money to care for them.
As a “mother” of a rescue dog, this issue is very near and dear to my heart. So, I decided to look into it. With my knowledge in Big Data, I thought that surly there is a way to address this concern.
What I found was that the ASPCA is now utilizing big data and a geographic information system (GIS) to approach this problem in a whole new way. They are using this data to determine 1) where these animals are coming from, 2) how they can best utilize their resources to care for more animals and 3) how to get more animals adopted into happy homes.
1. “Hotspots”
The ASPCA refers to areas where it is highly likely to find abandoned or unwanted pets, “hotspots.” They do their best to predict where these hotspots are, and allot more resources to these locations. This includes trucks, people, shelters, veterinary services etc. But what they have found since using the GIS system, is that many times in the past, their predictions were wrong.
Once they identify a hotspot, they know where to pour prevention resources into. Many times, owners just give up on a pet, because owning the animal is too difficult or expensive. They can provide owners with less expensive services like neutering or minor medical interventions, such as vaccines. This makes it more likely that the owner is able to care for the pet, and makes it less likely that animals will procreate uncontrollably.
2. Efficiencies
The previous section touched on this, in that many resources were previously wasted through inaccurate hotspot identification. Using data, they are better able to distribute their limited resources efficiently.
Ultimately shelters, rescues and the ASPCA are all businesses that must consider their profits and costs. Big Data can help these shelters in similar ways that we have discussed in our blog. Data could help them determine if they are spending too much on needles, or if they are paying to replace their tires when there is still a warranty. It can even help them best utilize their space. If they can reduce these costs, then they will have more money to spend on services that directly affect the dogs and cats.
3. Adoption
These days, when we are interested in adopting a dog or cat, our first stop is often times the web. We may search certain breeds we are interested in, or “how expensive is it to own a dog?” or, searching good dog breeds to take hiking. These searches can be used to identify concerns that potential adopters have, and what type of animal is more likely to be adopted in a certain area.
For example, Colorado is known as a very active state. Therefore, it may be more likely that a German Shepherd would be adopted in Colorado, than in New York City. Shelters can use this information to relocate animals to areas where they are more likely to be adopted.
Structured data is simply the opposite of structured data- data that is traditionally organized in columns and rows, and stored in fields of a database. This often includes text that is not structured in any particular form or order, such as an email.
Examples:
Examples of unstructured data include: Word documents, videos, email messages, audio files, presentations, webpages, photos and many other types of business documents.
Although these files may have an organizational structure, it does not mean that their data will fit cleanly into a database, like the data from an excel file.
The Importance of Unstructured Data:
It is estimated that 80-90% of an organization’s data is unstructured. Because data can be very valuable to an organization, by providing insight to decision makers, the unstructured component of data analytics should not be left out. Businesses should learn to manage all their data, not just their structured data.
Mining Unstructured Data:
Like a diamond, unstructured data is very valuable, but also very difficult to “mine.” Because of the difficulty, many software solutions have been developed in attempt to help organizations extract information from unstructured data. However, many find that such software, like Hadoop, is not worth all the Hype.
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