Data is growing exponentially- we are collecting AND analyzing more data than ever. As this occurs, it is also becoming more and more helpful- not just for the medical field (where we often hear about big data analysis being used), but for businesses as well. Here are 4 ways that big data is having a big impact on business:
It Provides Businesses With More Detail
The more data a company has, the more information can be extrapolated (such as customer data). Because knowing your customersis the key to a thriving business, a greater level of detail and insight is the difference between a good company and a great company.
Because you can analyze data according to specific criteria, it enables businesses to be more targeted. This can allow a business to provide more tailored products and services to their customers. Because this is the age of customization, this provides a more positive experience for your customers.
Big data can improve decision making in two ways: the analysis provides decision makers with more information and detail, and it also provides decision makers and management teams with accurate information. Data analysis is based on hard facts, rather than gut feelings or guesses. It can help minimize risks by uncovering statistics that may not have been known prior to to analysis.
This of course, is our favorite way that big data is helping business. Simply put, it saves your business money by highlighting problems before they become major issues. As a result, making quick decisions, based on facts will provide a greater return on investment, and more quickly.
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
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.
There is a glass with water in it sitting on a table- is the glass half empty or is it half full?
The way you answer the question above may determine how you think about IBM’s new solution for the high demand for data scientists. Data scientists are people who can analyze and control analytical programs that work to sort through large masses of data.
As the amount of data the world produces increases, the need for people who can sort through that data and make meaning of it goes up as well. IBM believes that they can aid this problem with their natural language analytics program which is predicted to be able to do the job of a data scientist.
This provokes the question, is this a good thing? Is this a means to an end or is this the beginning of a war between humans and robots for jobs?