Big Data and Information Quality Guarantee
Big data is used to illustrate increase of data availability that could be structured or unstructured. Data sets combination in big data is characterized by big volume, variability, and velocity that make it hard for it to be captured, managed, and processed by technology within the time they are needed for effective use. Big data composition format is as a result of modern technology generation. Examples of technologies used to gather big data are web blogs, radiofrequency Id, and sensors devices (Segaran & Hammerbacher, 2009). For big data to be utilized accurately and effectively, it must be used together with a structured relational database from a given application like Enterprise Resource Planning (ERP) or Customer Relationship Management (CRM)
In cases where big data is adequately captured, processed, and evaluated, organizations are in position to have a deeper comprehension of their firms, customers, goods and services, and an upper hand against their competitors. This makes the quantity information have a quality value. The most important issue about big data is making use of the quantity information acquired to have a quality value in the organization.
Implications of Big Data for the Quality Management
Big data is a crucial aspect for all organization since it is an information asset. Big data represents technological advancement within organizations, which makes firms to know the world better, especially in making management decisions. The decisions made in the management of firms are as a result of the structured and unstructured data.
Big data management ensures that quality information is obtainable for good business intelligence analysis applications (Laney, 2012). With big data availability, organizations and other entities are able to set up strategies that allow them achieve desired results. The major aspect of big data and management is the analysis of information, which assists managers in making good management decisions in product development, market development, operational effectiveness, and market predictions.
Big Data Advantages over Traditional Sampling
Big data as a contrast to long-established methods of sampling has numerous advantages. First, the information given in the big data offers business insights that are far reaching than the traditional ways. This offers crucial information, especially regarding human behavior, responses, communication, and trends, which are direct from the consumers. This information is significant in the development of an organization.
Additionally, big data offers an opportunity to utilize available data that was not being used. For instance, customer relationship management systems can come up with strong visualization of customer’s reactions, wishes, and responses data, thereby increasing organization performance. Big data ensures an organization creates strong trends to solve research related issues and predict the future models within an organization.
Challenges and Opportunities in Quality management Systems of Big Data
The main opportunity of big data is the creation of value. This value can be generated from analyzing big data through ways like discovering different needs, improving performance, market segmentation of customers, creating transparency, and replacing human decisions with automated systems algorithms. These will be beneficial to the organization.
Big data challenges vary and are classified into three. The first challenge is data itself, which involves aspects like volume, variety, velocity, and veracity that an organization faces. Data processing too is another challenge for big data. Establishing effective model for data analysis and ability to take action, especially in the fail fast aspect, is critical in big data. Management challenges involve data privacy, security, governance, and ethics. Ensuring the safety of data to responsible access keeping in mind the laws involved could be tricky given the fact that sensitive and private information is at risk.
Laney, D. (2012). “The Importance of ‘Big Data’: A Definition”. Gartner. Retrieved 21 June 2012.
Segaran, T., Hammerbacher, J. (2009). Beautiful Data: The Stories Behind Elegant Data Solutions. O’Reilly Media.