Over the years, since the introduction of Big Data, the amount of data created has grown exponentially. Referring to the graph above, it is clear how exponential the data growth has been; with an increase of approximately 6000% in data growth over ten years (2005-2015). However, it was from 2005 to 2010 that the biggest data surge was observed: an increase of 843%. Over time the percentage increase decreases but that doesn't mean the data doesn't continue to grow exponentially. From 2015 to 2020, the data we generate will increase by 405%. Say no to plagiarism. Get a tailor-made essay on "Why Violent Video Games Shouldn't Be Banned"? Get an original essay Several identified causes for the growth of data are as follows: There is a significant increase in human engagement with social networks and the Internet of Things (IoT). There is an explosion of machine-generated data. Due to the enormous amount of data we have generated, we enter the phenomenon of Big Data. What is Big Data? Big Data has been described differently in many articles. Oracle described it as holistic information management that includes and integrates many new types of data and data management alongside traditional data. McKinsey, on the other hand, defines them as data sets whose size is beyond the ability of typical database software tools to capture, store, manage and analyze. Although these two renowned companies offer a varied definition, they are actually not very far from each other. To summarize, Big Data is fundamentally data sets that are mostly unstructured and are so large and/or complex in both volume and variety that traditional data processing application software may not be sufficient to handle them. Key Features of Big Data It is often defined by the 3 Vs, namely volume, velocity and variety. Volume refers to data at rest. It's the amount of data we have: from online channels, to social media platforms, to online transactions, etc. We typically look at terabytes to petabytes of data. In Big Data, megabytes and gigabytes are considered small. Speed ​​refers to data in motion. It is the speed at which data is generated, collected and analyzed as it is generated. The current Big Data technology we have today offers the ability to analyze data instantly. The variety lies in the different types of data we use. It also describes one of the biggest challenges of data as it can be unstructured - uncategorised - and can include many different formats such as XML, MP4 etc. Equally Important Features to Consider In addition to the 3Vs, there are other 3Vs that are worth considering when dealing with Big Data. Veracity is about ensuring that data is accurate and reliable. This is where processes would be needed to prevent bad data from accumulating in your systems. It's important to set metrics on the type of data collected and what sources it comes from. Variability is when your data is constantly changing. It may be similar to the variety but differs in some aspects. To explain it in layman's terms, imagine going to a coffee shop that offers five different blends of coffee and getting the same blend every day but a different flavor every day. In this case, different blends refer to variety, and when you get a specific blend every day but it tastes different, that's variability. It is important to note this "V" as it can have a huge impact on the homogenization of the data. The last "V" to take note of is the value. It refers to the value of the extracted data. It's the end game because after dealing with other features, you need to make sure you get avalue from the data that is analyzed. It is critical to fully understand the costs and benefits of data collection and analysis. No matter how large a volume of data you have, if it's not high-value data, it's essentially useless. The most important feature IS questionable. But for a business or organization, variety is more important. This happens because it is not the ability to process and manage large volumes of data that determines the positive results of Big Data. Rather, it is about the ability to integrate more data sources than ever before: from old to new, from small to large, from unstructured to structured, and so on. Market Drivers Big Data continues to thrive and prosper due to the following factors: Exponential data growth The adoption of mobile devices is relentless. The spread of mobile Internet sees consumers increasingly connected, using social media networks as a communication platform and source of information. This then leads to the creation of a huge amount of unstructured data that needs to be analyzed. Increased Investment in Big Data Technologies Competitive pressure on organizations has increased to the point that most traditional strategies offer only marginal benefits. It has been demonstrated and recognized that Big Data has the potential to provide new forms of competitive advantage to organizations. Therefore, it has become crucial for them to make better decisions. This therefore translates into future demand for better Big Data technologies, so investments to enhance them are also growing. Innovations and Developments in Unstructured Data Software Big Data technologies must be implemented to aid research and development efforts because they must support the growth of digital content and enable more efficient analytics results. Strong open source initiatives The Hadoop framework, along with software components such as the R language and NoSQL tools such as Cassandra and Apache HBase, is at the center of many big data discussions. Due to its popularity and feasibility, other vendors launch their own versions. An example is Oracle's NoSQL database. Evolution of the IoT In relation to the first point, there are billions of devices connected to the web and they will continue to increase over the years. Gartner predicts that there will be more than one hundred billion devices connected to the Internet in the next few years. These send huge amounts of data that need to be stored and analyzed. Companies deploying sensor networks will need to adopt relevant Big Data technologies to process large amounts of data sent by these networks. The influence of Big Data in brief At the enterprise level, it enables new and innovative business models, as well as providing insights, thus leading to a competitive advantage over competitors. On a technical level, as data continues to grow and is available in so many formats, traditional methods alone may not be sufficient. Big Data eases this burden and offers a new solution for efficient data analysis. Financially, data system costs continue to grow and it is much more advantageous to opt for commodity hardware and open source software that you might find in big data technologies. Why is Big Data important? Benchmark With such a massive amount of information, data can be modeled or tested however an organization sees fit. Improving Businesses In business, sharing knowledge is an indispensable skill. Big Data enables more effective business outcomes because collecting large amounts of data and spotting a trend allows it to move more quickly, smoothly and efficiently. It also allows them to achieve goals.