How Big Data Can Help Your Organization?
Big data has huge potential to benefit organizations from all walks of life around the world. Big data is more than just large amounts of data. In particular, the combination of different data sets will provide organizations with real ideas that they can use to make decisions and improve their financial situation. Before understanding how big data can help your organization, let's take a look at what it really means:
In general, it is recognized that big data can be explained in terms of three Vs: speed, diversity, and quantity. However, I would like to add some V to better explain the impact and implications of a well thought out strategy through big data.
Also Read: Data Generator a collection of useful API's.
Speed is the speed at which data is created, stored, analyzed, and viewed. In the past, batch processing was a common practice, usually receiving database updates every night or even weekly. Computers and servers take a lot of time to process data and update databases. In the era of big data, data is created in real time or near real time. With the availability of connected to the Internet, wireless or wired devices, machines and devices can transfer their data when created.
The speed with which data is currently being created is almost unimaginable: we upload 100 hours of video to YouTube every minute. In addition, more than 200 million emails were sent every minute, about 20 million photos were viewed, 30,000 photos were uploaded to Flickr, nearly 300,000 tweets were sent, and nearly 2.5 million requests were made on Google.
The challenge for organizations is to respond to the tremendous speed with which data is created and used in real time.
In the past, all the data created was structured data, perfectly integrated into columns and rows, but those days have passed. Today, 90% of the data generated by organizations is unstructured. There are many different formats for current data: structured data, semi-structured data, unstructured data and even complex structured data. A wide variety of data requires different methods and different techniques for storing all raw data.
There are many types of data, and each type of data requires different types of analysis or different tools. Social networks (such as Facebook posts or tweets) can give you different ideas, such as analyzing how your brand feels, and sensory data can give you information about what the product looks like using the wrong ones.
90% of all data ever created was created in the last 2 years. The amount of data in the world will now double every two years. By 2020, our data volume will be 50 times that of 2011. The huge amount of data is huge, and in the evolving digital world, the huge contributor is the Internet of Things with sensors. Devices across all devices worldwide they create data every second.
If we look at aircraft, they generate about 2.5 billion megabytes of data each year from sensors installed in the engine. In addition, the agricultural industry generates large amounts of data through sensors mounted on tractors. For example, John Deere uses sensor data to monitor machine optimization, monitor a growing farm fleet, and help farmers make better decisions. Shell uses ultra-sensitive sensors to find more oil in wells. If these sensors are installed in 10,000 wells, they will collect about 10 exabytes of data each year. Again, if we compare it to a square kilometer matrix telescope that will produce 1 exabyte of data every day, it is absolutely nothing.
In the past, creating so much data can cause serious problems. Today, with lower storage costs, better storage options (such as Hadoop), and algorithms that make all this data meaningful, this is no longer an issue.
This data is incorrect if there is a large amount of data entered at high speed on different volumes. Bad data can cause many problems for organizations and consumers. Therefore, the organization must ensure that the data is correct and the analysis performed on the data is correct. Especially in automated decisions where no more people are involved, you need to make sure your data and analysis are correct.
If you want your organization to focus on information, you should be able to trust data and analytics. Surprisingly, one-third of business executives don't trust the information they use to make decisions. Therefore, if you want to develop a big data strategy, you need to focus on the accuracy of the data and the accuracy of the analysis.
Big data changes a lot. Forrester's lead analyst Brian Hopkins defines variability as "variation in meaning in the dictionary." It was the Watson supercomputer that won the danger. Supercomputers must "analyze the answer in one way and find out what the right question is." This is very difficult because words have different meanings and all depend on the context. To get the right answer, Watson must understand the context.
Variability is often confused with diversity. Let's say you have a bakery that sells 10 different kinds of bread. various kinds. Now imagine that you go to this bakery for three consecutive days and buy the same type of bread every day, but the taste and smell are different every day. This is variability.
Therefore, variability is very relevant to the sense of analysis. Variability means that the meaning changes (rapidly). In almost the same tweet, the meaning of a word may be completely different. To perform proper sentiment analysis, the algorithm must be able to understand the context and decipher the exact meaning of the words in that context. This is still very difficult.
This is the difficult part of big data. Make all this large amount of data easy to understand and read. With the right visualizations, you can use raw data. Visualization does not of course mean ordinary graphics or pie charts. They represent complex graphs that can contain many data variables while remaining understandable and readable.
Visualization is probably not the most difficult part of the technology; this is undoubtedly the most difficult part. Telling a complex story on a chart is very difficult, but it is also critical. Fortunately, more and more emerging big data companies are beginning to focus on this aspect, and eventually visualization will make a difference.
All this available data will create tremendous value for organizations, companies and consumers. Big data means big companies, and every industry will benefit from big data. McKinsey pointed out the potential annual medical value of big data in the United States. That's $ 300 billion, more than double Spain's total annual medical expenditures. They also mentioned that big data could bring 250 billion euros of potential value to European public sector management each year. In addition, in their thoughtful 2011 report, they claimed that by 2020, the potential annual consumer surplus of using personal location data worldwide could reach $ 600 billion. This is a huge value.
Of course, the data itself has no value. The value lies in the analysis of the data and how it becomes information and ultimately knowledge. The value lies in how the organization will use this data and transform it into an information-driven enterprise whose decisions are based on knowledge derived from data analysis.