In this exciting new era of information exchange, big data has become a normal part of any enterprise. Organizations today depend on information to make decisions about strategy, operation and business. Today’s business environment is characterized by rapid change and competition. Traditional data analysis techniques are challenging even for the most technologically savvy organizations. Traditional data analysis techniques often lead to a limited number of reliable results. The challenge for managers is how to transform traditionally analyzed data into insights that support new business strategies and actions.
Big Data has become the standard by which managers judge the performance of their companies. What are the three different characteristics of big data that distinguish big data from conventional data analysis? The three distinct characteristics of big data are: volume, speed and verification. We shall explore these characteristics in order to enable managers to exploit the power of data to drive business value.
Volume – Big data has many volumes of data. It is not enough to say it has many volumes, because it is also necessary to measure the volumes of various sources. These measurements may be in the form of terabytes, petabytes, or petabytes. When managers analyze this volume of data, they must first discard non-productive sources. If the company continues to produce non-productive data, then it will be necessary to bring old data to the attention of those in charge of strategic management.
Speed – Managing data requires both speed and accuracy. Traditional data management systems were designed and built to handle one form of data. However, as the volume of data continues to grow, traditional software is being upgraded to manage different forms of big data. Big data isolation and velocity are two key technologies that allow for data to be quickly analyzed and used for business decision making purposes. There are three distinct characteristics of speed when managing data: reliability, quality and availability.
Reliability – No matter how well designed and managed in a traditional database management system is, there are always going to be problems with consistency, integrity and durability. Big data warehouses can replicate thousands or millions of records at the same time. This creates challenges for data reliability. In addition to unreliable primary keys, database management systems may experience logical failures, connectivity failures, and server downtime. In addition, database management systems must support both read and write (RDB) data access, which makes them vulnerable to data corruption. In addition, RDBMSs must support transactions between multiple independent tables and may have to support complex transactions involving multiple linked tables.
Quality – The ability for a data management system to provide quality outcomes is based on the ability for it to efficiently identify and resolve the relationships among the data sources. Specifically, each data source must be assigned a primary key and every linked table must have a secondary key. Next, an effective data hierarchy needs to be defined and implemented so that logical tables and views are logically separate from each other and can be accessed independently. In addition, the depth and breadth of a data hierarchy must be defined and enforced through the use of meta-tags, tables, indexes, and key columns. Lastly, an effective data isolation will ensure that the application can continue to operate efficiently even if some or all of the application’s data sources fail.
Availability – In most cases, the ability for a data hierarchy to provide application security is the most significant benefit provided by this technology. Big Data is inherently scalable and elastic, provides mission-critical data protection against external threats and consistent high availability even during unexpected events. Additionally, application performance will remain optimized through the use of event-triggers and monitoring. This ensures that users can remain responsive and on-site during critical periods.
Metadata Store – The ability to provide information to the rest of the world in the form of XML (Extensible Markup Language) is another distinguishing characteristic. Big Data technologies such as HD Fusion and Hortonwork Data Platform provide rich metadata facilities. These provide a foundation for both designing and maintaining a wide range of web services and data warehouse applications. Additionally, these technologies make it easy for organizations to access information at any time and from any location. Therefore, a data warehouse provides information on the state of the organization, its products, and services to the rest of the organization, and to customers and partners.