Big Data
Big data a term that refers to a process used where the traditional data mining and handling techniques cannot suffice the meaning, insight and interpretation of the underlying data in compiled from various sources. Data that are unstructured or simply quite large can’t be processed by the usual and common relational database engines. These types of data require different processing approaches which use huge parallelism on readily and available hardware. Big data are data sets that are complex and voluminous, traditional data processing application software that are inadequate. Big data challenges include capturing the data, data storage, analysis, search, transfer, querying, updating, information privacy and data source. There are five dimensions to big data known as Volume, Variety, Velocity and the recently added Veracity and Value.
- Veracity: The of captured data can vary greatly, daffecting the accurate analysis
- Variability: Inconsistency of the data set can hamper processes to handle and manage it.
- Velocity: In this context, the speed at which the data is generated and processed to meet the demands and challenges that lie in the path of growth and development.
- Variety: The type and nature of the data. This helps people who analyze it to effectively use the resulting insight.
- Volume: The quantity of generated and stored data. The size of the data determines the value and potential insight- and whether it can be considered big data or not.
Big Data Applications
Governance
The use and adoption of big data within governmental processes allows efficiencies in terms of cost, productivity, and innovation, but does not come without its flaws. Data analysis often requires multiple parts of government (central and local) to work in collaboration and create new and innovative processes to deliver the desired outcome.
Manufacturing
Big data provides an infrastructure for transparency in manufacturing industry, which is the ability to unravel uncertainties such as inconsistent component performance and availability. Predictive manufacturing as an applicable approach toward near-zero downtime and transparency requires vast amount of data and advanced prediction tools for a systematic process of data into useful information.
Healthcare
Big data analytics has helped healthcare improve by providing personalized medicine, prescriptive analytics, clinical risk intervention, predictive analytics, waste and care variability reduction, automated external and internal reporting of patient data, standardized medical terms and patient registries and fragmented point solutions.Some areas of improvement are more aspirational than actually implemented. The level of data generated within healthcare systems is not trivial.
Education
Because one-size-fits-all analytical solutions are not desirable, business schools should prepare marketing managers to have wide knowledge on all the different techniques used in these sub-domains to get a big picture and work effectively with analysts.
Media
In Media and Advertising approach big data as many actionable points of information about millions of individuals. The industry appears to be moving away from the traditional approach of using specific media environments such as newspapers, magazines, or television shows and instead taps into consumers with technologies that reach targeted people at optimal times in optimal locations.
Internet of Things (IoT)
Big data and the IoT work in conjunction. Data extracted from IoT devices provides a mapping of device interconnectivity. Such mappings have been used by the media industry, companies and governments to more accurately target their audience and increase media efficiency.
Why do businesses need Big data?
- Data can be used in crucial decision making process in business
- Social media data can be used to fine tune the business strategies
- Read, evaluate and provide apt responses for customer feedbacks
- Identify the risks to the product or service at an early stage in case of any
- Optimal efficiency in operation can be achieved