89% of companies believe that if they do not develop and adopt a big data analytics strategy, that they could lose both market share and momentum.
Why? Because big data tells you things about the world, and about your business that you didn’t know before. And as more and more of your competitors adopt data strategies, they will learn more and more about your industry, putting you at a disadvantage. It is quickly becoming the “new thing” in business.
Luckily, it is quickly becoming easier and easier to adopt a big data strategy.
Step 1: What is Big Data?
The first step is to learn what Big Datareally is. Understand it first, in order to appreciate its value to your company.
Step 2: Get Comfortable With the Idea of Using Big Data
The definition may scare you, because it is a little mind-boggling and difficult to comprehend. However, it is not as big and scary as it seems. It is also not as costly as it seems. The Internet of Things (IoT) movement is making collection much easier, and the cloud is making storage much easier. Even analytics are becoming easier for companies of all sizes. If you don’t have enough money to spend on hiring a whole team of data analysts and data scientists, don’t sweat! There are firms out there that specialize in this that can analyze your data for a much lower cost than hiring a team.
Step 3: Ask Yourself These Questions to Develop a “Big Data Plan”
A good first question to ask yourself is, “what do we have?” Then identify what kind of data you think you are missing that you may need. This will help you identify what kind of data to collect. Many companies think that simply collecting a lot of data is a “data strategy.” However, data is only useful if it is relevant to your company. Furthermore, collecting a lot of data can make it more difficult for a data analyst to extract information and insight from that data. Having a lot of data makes the analyzing process more difficult, as they have to sift through all of the irrelevant information (and initially decide what information is irrelevant in the first place).
Next ask, “What assumptions do I have about my company?” This is a great question to ask, as it is a great starting point for companies that don’t have a big data analytics plan already in place. At this point, much of what you know (or rather, think you know) about your company are based on assumptions. You may have reports that tell you things, but this data can be manipulated and often comes from many, disparate sources. Because of this, you may not be getting accurate information.
Therefore, a good starting point is to test your hypothesis about your assumptions of your company. This also makes it easier for a data scientist to determine where to start…and you never know, along the way, he or she may find something else that will be useful.
As a data analytics and business process engineering firm, we find that these 5 things are the most common challenges that businesses face. They also greatly impede on the prosperity of the company. Watch this video to find out what they are. And check out more on http://cliintel.com
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.
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 is no secret that Europeans have been extremely reluctant to jump on the Big Data train. As Americans continue to indulge in Big Data and incorporate it into their businesses, we must wonder, what will happen to Europe? Will European companies fall behind without the technology to even the playing field?
Despite what industry you are in, you can use data to achieve a wide variety of goals. It doesn’t matter if you are a big, or small business. Simply put, data analysis converts raw data into the accurate and up-to-date information you need to know what’s going on in every facet of your company.
Cancer is a horrible illness that has been nearly impossible to figure out. Scientists have had a difficult time finding a cure for cancer because no two cases of cancer are the same. Everyone’s bodies are different and therefore, those who develop cancer do not have identical cancer cells.