A Data Science Central Community
The word wide web is turning into a colossal heap of data that is being stored at hundreds and thousands of datacenters across the world. According to a recent research made by Data Science Central, the size of data on the internet is expected to double in every two years. Such amount of data is not only hard to be stored but it is also posing a challenge for organizations and businesses to use this data for making useful decisions.
Ever since the idea of big data started making headlines in the cyber fraternity, organization have been trying to understand and make good use of this phenomenon. This is why businesses are pouring millions of dollars into the research while have already come up with tools to make it easier for organizations to make decisions based on the results from data sets.
Wikipedia defines big data as the term for datasets that are so large or complex that traditional data processing applications are inadequate to deal with them. Challenges include analysis, capture, data curation, search, sharing, storage, transfer, visualization, querying, updating and information privacy.
There is no hard and fast rule to call any information big data, however, any kind of information that needs new tools and techniques to be processed could be big data.
The processing of such data is to be done by groundbreaking technologies compared to the old hardware that the industry has been using for decades.
There is currently an on-going debate among the stakeholders on how useful it could be investing in this industry. The good sign is that a number of organizations have already been able to turn the idea of big data into lucrative businesses. Although there is a lot more to be done both in terms of research and practical development, this new arena in the digital landscape cannot be ignored.
Universities and independent think tanks have termed big data as the next big thing. It has been the buzzword among technical groups for quite some time now. There is steady development being made in data storage, computation, and visualization which gives a lot of hope for the future of this industry.
Another reason why the industry is attracting huge investments from corporations is that the field is quite new and by investing time, money and manpower in the right place at the right time, companies can take lead and set an example for others to follow.
One of the most important question about big data is how it can be analyzed given its huge size and complexity that an ordinary analytical software cannot manage? The most common way to analyze big data is by using a method called MapReduce. The process includes processing data sets in a parallel model. The process itself includes two part, Map function and reduce function.
The Map function does all the filtering and sorting of data. It then categorizes each of the processed datasets in a structured form so that it can later be analyzed easily.
The next part of the process is the Reduce function. The Reduce function creates a summary of all the data that was categorized in the previous step.
Although there is too little research work that is available to the public, there are only countable organizations that have come up with useful ways to harness big data in making real-life decisions. There are tools that are in the process of development and are only available for researchers.
Here are some important tools and resources that a big data enthusiast can use to bootstrap the process of learning big data:
The mentioned tools in above list offer limited tasks and are meant for medium scale operations. There are a number of advanced tools like the Tableau Desktop and Server, Talend Open Studio etc., that have very specific purposes and one can use them for advanced visualization, queries, and data processing tasks.
There is no doubt that big data has a lot to offer with the promise to get results. At the same time, it is very important to set goals. When you start digging for useful information from the millions of terabytes of data, you are potentially exposed to the risk of over-spending your resources. To make sure you are always in the right direction throughout the whole process, it is equally important to carefully craft an execution plan that minimizes the risks of failure.
The basic step towards achieving successful results from big data campaigns is to choose the right tools. If you choose a wrong tool for any operation, you could put yourself in a state of confusion. This is why you need to bring on board people who could plan and optimize the various steps involved so that you could not only save a lot of time but monetary resources as well.
A good example of going forward in the right direction is to have a resource manager who understands the complexities of data science. A good resource manager will not only make the right selection of tools but will also bootstrap the process to save you time and money.
The next person who is going to help you in your ambitions is a big data scientist. This person would have all the insights of the industry and can point out many things that otherwise could go unnoticed. Data scientists also have the capability to predict the results of steps being taken. This way you can further minimize the risks with the help of right people in the right place.
The process of making real life decisions based on big data does not only include the selection of right hardware and compatible platform but it also includes putting at work the right people as well. Organizations can indeed make decisions that could have a huge impact on creating intelligent solutions. Just a couple of years ago, processing such amount of data was only a dream. Owing to the amazing advancements being made in the field of computing and data sciences; these dreams are becoming true.
On top of that, there are a number of tools so inexpensive and resources with ease of access; no matter how big or small your company is, it can benefit from big data and pave a path towards a future with no limit of success.
About the author: Rick Riddle is a content manager at Evoessay.com and an up-and-coming blogger from New York. His current interests include big data mining, machine learning, computer vision, pattern recognition and digital marketing. Follow @rickrddl on twitter