Businesses must discover innovative ways to gather data, manage data, perform data analytics, and make use of the information they collect as the amount of data they collect grows exponentially.
With the large amount of data businesses collect growing exponentially, they must find new ways to collect data, manage data, data analytics, and take advantage of this information. Data Scientist have been working since long time on this problem. People share information voluntary and companies need to use this unstructured data through research questions and business intelligence. The more information businesses have about their customers’ preferences, behaviors and pattern recognition, the better products and services they can offer at the right time.
Terms, Big data and macro data are used for large data sets whose size is beyond the ability of typical database software tools to capture, store, manage, and data analytics- the complete Data Science.
Companies collect data in any form from various data sources and through different data collection methods- whether emails, video clips, or social media — but storage problems arise when this data becomes very large. A Gartner analyst says that companies need at least 1 petabyte (one million gigabytes) of disk storage capacity even to consider efficiently handling Big Data.
The 5 V(s) of Data and associated problems
Computer Science has advanced beyond anyone’s imagination. In the world of data and data points, the speed and Volume at which data is collected have caused many to classify it. These classifications can help understand big data and the challenges that come with acquiring and handling it and its business value.
Data can be classified by Volume, Variety, Veracity, Value, and Velocity. This classification is also known as the 5 V(s) of Big Data. The 5Vs provide a taxonomy for classifying data into manageable categories. It simplifies the process of understanding big data and its business value.
Characteristics of Big data- The five V’s are Volume, velocity, variety, veracity, and value.
The Volume of data: The Volume of data has expanded exponentially over the last few years. This data can be structured, semi-structured, and unstructured, and it focuses on the sheer amount of data rather than its content. The main challenges of managing huge data include storage limitations, processing power requirements, and bandwidth capabilities.
The velocity of data: Time is an important factor when assessing big data as new information emerges continuously throughout the day. Data is generated rapidly, and big data velocity determines the pace at which it is collected from the real world. With everyone coming into the Information Age and producing data at high speeds, the velocity of data is increasing exponentially. The problem with this is that we can’t handle the amount of data coming in.
This led to a need for data processing. Electronic Data processing is the streamlining of data. The more complicated solution is data investment, which involves making sense of the vast amount of data we have received lately to make accurate decisions.
Variety of Data: Along with Volume and velocity, the diversity of big data or different data types such as images, videos, etc., presents a unique challenge for organizations to manage their wide range of content effectively. It was reported that more than 90% of the world’s data was created in the last two years alone. This creates a challenge because there are too many data sources and it’s not properly organized and managed.
The veracity of Data: Big Data contains large quantities of ambiguous and dirty (unverified) data that needs to be cleaned and organized before serving its purpose. Semi-Structured data, for example, is often incomplete or inaccurate, which makes data cleansing a challenging process.
There are problems associated with collecting inaccurate or duplicate data. When data are entered, they are often transcribed incorrectly or incompletely. Doing so may lead to misleading results and false conclusions.
Data quality and Data quality management is an issue because unstructured or semi structured data cannot contribute knowledge to a study or research project. If one can never trust all the variables in a set of data, then it is important that one focus on only those variables which seem most valid and reliable.
Value of Data: While the other V(s) represent external factors affecting big data, the value represents the internal factors associated with business strategy and execution. To extract maximum value from Big Data, companies and data scientists need to have a clear goal for what they want to achieve through their analysis. Once this is established, they can determine which information needs to be collected and how it will be used.
To summarize: The 5Vs of Big Data are Volume(size), Velocity (Frequency), Variety (Types), Veracity (Accuracy), Value (Business).
Take Away
Data is on everyone’s mind. Any of us, who works in data science, cannot escape the insistent pressure to deliver, both because of accelerated market demands and growing customer expectations. Therefore, we must harness the power of our various tools and learn to use various Database management systems, of our widespread processing capability with intelligent data integration skills. Indeed, we must leverage whatever tool or technique that assists in reducing complexity, and in so doing, deliver extraordinary value to ourselves and our customers.
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