Benefits of Big Data
The growth of technologies is inevitable nowadays for solving issues that can’t be ignored for a long time due to lack of resources. Data storage is one of them that require hassle-free transaction for any organization around the globe. Understanding the differences between traditional data and big data is important to utilize for the continuous progression of data transmission.
Traditional data had some limitations for storing and retrieving data that leads users to choose big data to tackle any kind of issues that occurs during the data processing. The biggest advantage of big data is the availability of tools that are being involved in super-fast data processing and analyzing.
Traditional data is structured one that should be maintained with high-care from small to large-scale businesses. Centralized database architecture is being used to store and manage the data with fixed format or fields in the traditional data processing. Database applications such as Structured Query Language (SQL) are used for accessing and managing data in a traditional method.
Pros and Cons of Traditional Data
- Generated and implemented at the enterprise level.
- Volume ranges are limited from Gigabytes to Terabytes.
- Deals only with structured data.
- Generated only on an hour or day basis.
- Data source and management in a centralized form.
- Data integration is very simple and easy.
- Simple system configuration is enough for data processing.
- Size of data is small
- Database tools required only for performing major data operations.
- Normal functions are only required for data manipulation.
- Data model is based on the static and strict schema
- Stable and inter-relationship
- Easy to manage and manipulate as it is a manageable volume.
- Data sources are ERP, CRM, financial data, organizational data, and web transactions.
Traditional data have equal advantages and disadvantages. It sometimes kills the efficiency of data processing as it is difficult to maintain accuracy and confidentiality as traditional data has low data quality. But the data relationship between data can be figured easily with a simple flow diagram.
The limitations of traditional data are rectified using big data as it has tremendous features like flexibility, fast, ease, simplicity, maintainability, and quality.
Big data is not just the amount of data but it is about usage and manages of large and complex datasets that are difficult to handle manually. Big data can be any type like structured, semi-structured, and unstructured.
There are 5 important V’s in big data as Volume, Velocity, Veracity, Variety, and Value. Big data is implemented for extracting meaningful data from huge and complicated datasets from local and cloud storage.
Advantages of Big Data Processing
- Generated at an enterprise level or outside like cloud.
- Volume ranges from Petabytes, Zettabytes, or Exabytes.
- Dealing with structured, semi-structured, and unstructured.
- Generated data frequently and every second.
- Distributed data source and management.
- Data integration is difficult but with automation tools, it will be simplified.
- Requires high configured hardware and software systems.
- Larger data size than traditional data.
- Requires special kinds of tools to perform database operations.
- Special functions for data manipulation are required.
- Data models are dynamic and based on a flat schema.
- Data sources are social media, video, images, audio, device data, and sensor data.
Benefits of replacing traditional data with big data
Flexible : Big data is very easy and flexible to handle without any interruption. It provides better access to data and brings meaningful information as per the requirements of the organization.
Analyze : The data analyzing methods in big data are implemented for increasing the performance of data without affecting the data quality.
Easy and Fast: The whole data processing in big data is simple and takes very little time to produce a result very fast.
Simple to Store Data : Software applications are implemented to provide convenience to store data with a modern platform that brings fast data transfer options.
Quality Maintenance : Data Quality is very much important for an organization to get accurate insights. Big data never affect data quality like traditional data and it maintains the standard of data even it is huge and complicated.
Real-time results : Big data maximize the value of insights that are retrieved from real-time raw data. This is used for avoiding fraudulent data processing.
Machine Learning : The biggest benefit of big data is it can be implemented easily in the data analytics for the machine learning process as it became crucial for solving real-time problems with accuracy.
Applications of Big Data
Big data is now implemented in every industry for accessing useful insights from a large amount of global data. The following industries are utilizing big data for achieving special purposes of data processing.
Banking and Securities: Big data is improving the outcomes for banking and securities through early fraud warning, trade visibility, card fraud detection, enterprise credit risk reporting, audit trails, trade-related social analytics, anti-money laundering, pre-trade decision-support analytics, high-frequency trading, customer data transformation, IT policy compliance analytics, and IT operation analytics.
Communication, Media, and Entertainment: Big data is used for industry-specific data processing challenges by collecting, analyzing, and utilizing consumer insights. It is used to understand real-time data patterns, media content usage, and social media content.
Healthcare Providers: Big Data is implemented in the healthcare sector for monitoring, managing, and performing optimization and improvement of raw data through the understanding of customers effectively.
Education: Big Data incorporates with the education sector to learn new data management and analytics tools for utilizing different data sources efficiently.
Manufacturing and Natural Resources: Big Data involves natural resources for fulfilling the demand of oil requirements, agricultural products, gas, minerals, and metals to increase the velocity and volume.
Government: Big data brings significant challenges in the integration and interoperability of data across various departments and organizations that include energy exploration, fraud detection, financial market analysis, environmental protection, and health-related researches.
Retail and Wholesale: Big data is used in retail and wholesale sector for optimizing data from shopping patterns, and local event that helps in timely inventory analysis, and reduced frauds.
Transportation: Big data helps in transportation to get location-based social networks for traffic control, intelligent transport systems, route planning, and congestion management.
Energy and Utilities: Big data provide a smart meter reader that allows data collection frequently for facing challenges such as electric grid assets, capacity increments, and insights into energy consumption.
Traditional data is used for limited and local-based data processing whereas, big data used without any limitation of storage space and efficiency. The learning of big data brings numerous job opportunities to work for real-life applications such as healthcare, education, energy, utilities, retail sales management, banking, insurance, securities management, and public sectors. Upgrade your fundamental database skills to the big data processing to bridge the industry gap for data processing and analyzing.