Big Data refers to large volumes of data, complex and fast, that cannot be processed using traditional methods. Since the early 2000s, more companies began to store, access, and analyze data for improved business intelligence. The concept of Big Data handling is widely popular across industries and sectors. Typically, processing Big Data requires a robust, technologically driven architecture that can store, access, analyze, and implement data-driven decisions.
In this post, we will find out why Big Data without the right processing is too much data to handle. For that, we must delve into the 5 V’s of Big Data. But first, let us explore Big Data processing as a fundamental aspect of how business intelligence works.
Big Data Processing Cycle
Data processing is the manipulation of data by a computer system. This involves converting raw data into a readable format by the machine. The processing involves collecting data through the core memory to connected devices and transforming or formatting this data for the relevant output. Any use of computer programs to perform operations happens through data processing.
Suppose an enterprise wants to know its financial year performance. In that case, they must analyze large financial data sets to create a report that outlines the key indicators of the overall financial year performance. Similarly, if you search something on Google, the engine crawls across millions of pages to match the ideal resources with your search input. These are all forms of data processing in everyday life. There are numerous ways of processing data with accuracy and quality.
Typically Big Data is processed in 4 stages of the data processing cycle.
- Data collection – The process of finding or gathering relevant information from a large set of data for processing.
- Data input – Sending relevant collected data to a machine for processing.
- Data processing – Carrying out operations on data by a computer, retrieving, transforming, or classifying information.
- Data output – The finalized data available for access, visualization, and interpretation generated by the computer.
Modern technology offers the ability to search, find, retrieve, transform and classify data for analysis through intelligent computer applications. These applications save time, effort, and resources in compiling information that an enterprise or individual can then use. As the world becomes more interconnected, technology-driven solutions are essential, thus creating Big Data and Big Data processing.
To understand why processing is essential, let’s understand the 5 V’s that makeup Big Data.
Big Data metrics: The 5 V’s
Big Data’s importance in the modern business and technology ecosystem is boiled down to five V’s, i.e., volume, velocity, variety, veracity, and variability. By exploring these 5 metrics of Big Data, we can understand the importance of processing large volumes of data using the ideal technologies.
Big Data is enormous and thus refers to large amounts of data in volume. To understand the value of data, the size of data is a key metric. When dealing with Big Data, it is important to consider the volume of data required to store, access, and analyze. For example, knowing how many people actively share photos on Facebook requires a large database of information to be studied quickly for accurate results. Hence, an effective method of processing is required for any Big Data.
In Big Data, velocity refers to data flow from sources like machines, social media, mobile phones, networks, etc. The continuous flow of data is integral to determine the potential of data, how fast it’s generated and processed to meet the demands. Data sampling is a way of processing data when dealing with data velocity.
For example, Google records more than 3.5 billion daily searches on the search engine, while Facebook users increase by approximately 22% yearly. These statistics are a result of studying data velocity in large volumes.
Variety in Big Data refers to the type of data, whether structured, semi-structured, or unstructured. Variety also refers to heterogeneous sources of data. Essentially, data coming in from new sources, whether internal or external, refers to variety. Structured data is organized data that has a defined length and format.
Semi-structured data may be semi-organized, generally a type of data that does not conform to the formal structure of data. Log files are examples of semi-structured data. Unstructured data is unorganized data that generally doesn’t easily fit into the relational database of an enterprise or its data architecture.
Text, images, and videos are examples of unstructured data that cannot fit into rows and columns. Thus, to process a variety of data, an enterprise must integrate AI-powered machine learning algorithms that can analyze various types of data to provide effective analytics and reports.
Veracity refers to the uncertainty in data and the inconsistencies with various large sets of data. Sometimes, the data can get messy, where the accuracy and quality of data become questionable. Big Data processing is essential to analyze various data dimensions of different types and from different sources.
This allows a data architecture to retain its veracity by quickly processing large sets of data. For example, bulk data sets could create more confusion than a smaller amount of data that will convey incomplete information. Hence, processing Big Data is vital to the organizational output and analytical business intelligence of an enterprise.
Various robust enterprise frameworks like enterprise applications are integrated into the data architecture to visualize and analyze data. The processing of data simplifies Big Data into visuals and programs that a company can interpret and implement.
Value is the final V to understand the processing of Big Data. A large amount of data with no value is useless unless the Big Data is transformed into a valuable asset. Data in itself does not carry any value unless converted into something valuable to extract crucial information. Thus, the value of data is the most important metric of data.
With the rise of cloud computing, data architecture has evolved, and so has processing power. Big Data processing offers intelligence, and a data architecture offers access to this intelligence. Why else do you think more companies are relying on data for growth? Because data is the new oil and everybody wants the right data at the right time.
Konnect Insights based on Big Data principles use AI, ML, and NLP to ensure that all the data you track for your brand is easily comprehensible and representable in a visually appealing manner on their Beautiful Dashboards and BI tools in one unified platform.Tags: Analytic Tool, Big data, Big data metrics, Data, Data metrics