By 2025, it is predicted that the value of data will increase by 10-fold. Virtually, every branch of industry or business will generate vast amount of data. Thus, the world will experience an aggressive growth and data could be a missed opportunity when not being utilized. And to make matter worse, the rate of collecting and storing data is faster than the ability to use them as a tangible decision-making. With the help of ever-growing technology, visionaries are creating visualization methods to help turning raw data with no value to an informative data. Big data has served a purpose for organizations to optimize their businesses. With an abundant amount of data that organization generate every day, the ability to turn the data into a decision, effectively and efficiently is crucial. Thus, the knowledge of analytics and visualization would come hand-in-hand to tackle the problem in big data. Hence, a new Continue reading
Data Management
What is Web Mining?
Web Mining is the technique which is used to extract and discover the information from web documents and services automatically. The interest of various research communities, tremendous growth of information resources on Web and recent interest in e-commerce has made this area of research very huge. Web mining can be usually decomposed into subtasks. Resource finding: fetching intended web documents. Information selection and pre-processing: selecting and preprocessing specific information from fetched web resources automatically. Generalization: automatically discovers general patterns at individual and across multiple website Analysis: validation and explanation of mined patterns. Web Mining can be mainly categorized into three areas of interest based on which part of Web needs to be mined: Web Content Mining, Web Structure Mining and Web Usage Mining. Web Contents Mining describes the discovery of useful information from the web contents, data and documents. In past the internet consisted of only different types of services Continue reading
Data Warehousing – Meaning, Benefits and Implications
What is Data Warehousing? The term data warehouse or data warehousing was first coined by Bill Inmon in the year 1990 which was defined as a “warehouse which is subject-oriented, integrated, time variant and non-volatile collection of data in support of management’s decision making process”. When referring to data warehousing as subject oriented, it simply means that the process is giving information about a particular subject rather than the details regarding the on-going operations of the company. Moreover, when data warehousing was referred to as integrated it means that the data or information which are gathered from a number of sources are then all gathered to synthesize a coherent whole. On the other hand, data warehousing being time variant simply means that the data available were identified on a particular period. Lastly, data warehousing as being non-volatile means that the data is stable and when a new data is added Continue reading
Data Analytics in Healthcare Industry
Health is the most integral part of life. In the healthcare industry a vast amount of data is generated from different segments of healthcare organizations such as hospitals, healthcare service providers, insurance providers etc. However, like other sectors ranging from retail to banking, who have already leveraged the potentials of big data, the healthcare industry has not yet fully explored the importance of big data in deriving valuable insights from such vast amount of data at hand. For example, Grocery stores determine the loyalty of their customers by identifying the sales patterns, giving discounts and special offers, having a mix of products which not only improve their profits but also, increases their customer satisfaction. Claims providers and payers, the pharmaceutical industry has now only utilized big data to tackle issues such as changes in the quality of healthcare services, reducing fraud and abuse of claims, improved care. Due to the Continue reading
The Role of Big Data in Marketing
Big data is large and complex data sets that are collected by companies and governments. The data that involve many types of information arriving in increasing volumes and with the incredibly fast rates. Big data signifies colossal volumes of data are being generated from assorted sources such as business processes, machines networks, and social media. Historically, it is a challenge to reserve the enormous volume of data, by the progression computing capacity that storage is not an issue anymore. Big data can be classified into three types of data which is structured data, unstructured data, and semi-structured data. The structured data being easily entered, processed, queried, stored and recover into a fixed format. The typical examples of structured data contain numbers and dates. The unstructured data cannot be fit or classified into a net box and the process and analysis are very hard and time-consuming. For instance, objects from blogs, Continue reading