What is Big Data? – Definition, History, Values and More

What is Big Data? According to Wikipedia: “A Big Data is an ensemble of complex information processed through various streams into a common datum. Big Data is usually used to refer to the combination of hardware and software that accumulate, via multiple algorithms, large-scale data sets from various sources”. However, it can be defined as the history of human action on earth. It is said that all the basic activities of human society are characterized by Big Data. It includes the use of technologies like the internet, telecommunications, mobile devices and many more to collect, store and analyze large amounts of data about practically anything and everything on earth.

“Big Data has become an increasing challenge for businesses in terms of both managing and processing it”. In order to deal with this emerging challenge, companies have started to collect unstructured and structured data in different formats such as audio and video, text and numeric streams. Experts believe that this increase in data will raise challenges for companies and will eventually lead to the need of structured and unstructured data storage and processing. Companies have started using different tools and frameworks to deal with big data.

“In the past decade, the development of artificial intelligence and data visualization tools for business intelligence has steadily increased. Both are emerging technologies with wide applications and significant impact for organizations’. Data visualization and artificial intelligence are combining the vision and expertise of designers, data scientists and developers. With the help of these experts, users get real-time insights into data sets that are stored in high performance, large scale servers.”

“Data mining is the process of gathering and organizing large volumes of data in order to support business decision making. Data mining techniques may involve any or all of the following approaches. These include manually extracted information, working with information from natural sources, or extracting information from large consolidated databases (e.g., from enterprise-wide web data). Data mining can be used to provide business insight via different approaches (such as cost-to-effectiveness estimates, quality assessments, and product portfolio analysis)”.

According to IBM’s Technology Review: “The volume of data available today is almost every bit as overwhelming as the volume of human knowledge: it will be an important factor shaping the way we live in the future”. Data is virtually every bit as diverse and complex as the information stored in our brains, so large volumes of complex and conflicting data is not something most people can handle. However, the volume of complex data available today is almost every bit as overwhelming as the volume of human knowledge available. It will be an important factor shaping the way we live in the future”.

IBM’s blog also states “To analyze data using big data sets requires different types of technical skills than traditional statistical methods”. Facebook’s developers stated in a blog post, “We needed to train our data scientists to understand how to deal with the kinds of problems that face technical firms – both those building the technology and those managing it”. This statement seems reasonable and accurate to me. Having worked as a Systems Administrator for a large retail company, I know that dealing with complex and conflicting data sets is difficult for even the most talented of IT professionals. So yes, it takes talent, skill, experience, and expertise, but it also takes patience.

Another example is Google’s Data Platform. This is largely unstructured, but users can get help with it. In addition, as organizations and businesses grow, so too does the need for more structured data. It makes sense to look at the data assembled by Facebook to understand the importance of structured data sets and how to organize them using big data. In fact, Facebook uses structure very prominently in its core competency: organizing the enormous amounts of information it acquires from its many different activities – not only gathering information, but also using it in ways that are almost too structured to be categorized.

In conclusion, we need to ask ourselves whether or not unstructured and large data sets are what we need in order to make better decisions. Personally, as a Systems Administrator for a large financial company, I believe that we need both unstructured and structured data sets to make better decisions. However, these choices should not be static. The world continues to evolve, and it will continue to do so as long as people are alive. As such, it is my hope that over time the IT industry will move towards providing better insights based on large unstructured and structured data sets to better inform their customer’s decisions.