Essay Example on Big Data: Unlocking Hidden Insights for Better Results

Big data is better data by Kenneth Cukier is a talk that explores the importance of having huge as compared to small volumes of data. In his talk, the speaker explains that when there is big data, a lot of information can be obtained. Cukier gives an analogy of America's apple pie which is considered every person's favourite but further expounds that it becomes second best when every individual is given their own implying that they will have bigger pie's in size and therefore be able to sample them out in details and better. Cukier explains that in the same manner, bigger data not only enables people to sample out more data but also more of it.

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In references to Cukier's talk, the various application areas of big data are in the sectors of medical care, climate change, food security and energy. Other areas that big data can be applied include; banking sector, engineering and construction, transport and communication, among others. The business goals when applying big data is to utilize it in analytics to boost customer numbers and also in their retention. Through big data, businesses can be able to analyze customer trends and related patterns. By learning the trends, businesses can be able to provide services or products according to customer needs. Additionally, through big data, businesses can mitigate advertiser's problems and be able to give marketing impending. Businesses also apply big data in risk management, innovation and product establishment.

There are various types of data to be mined, and one of the data is flat files. Flat files refer to data that is in the form of binary or text structure, and this can be easily retrieved from data mining algorithms. These kinds of data do not have correlation among themselves, and they have to be embodied in data dictionaries, for instance, the comma-separated values file Another type of data that can be mined in the relational databases, and this can be said to be classes of data that have been structured in tables that consist of rows and columns in them. Such kind of files has a physical and logical schema. The physical schema defines the configuration of tables, while logical schema defines the relationship between tables. Its relational database is SQL. It can be applied in applications such as ROLAP model. Another source of data that can be mined is from a data warehouse, and this refers to a collection of integrated forms of data from various sources used for queries and decision making. There are three categories of data warehouse virtual, data mart and enterprise. Additionally, transactional databases are also another type of data that can be mined, and it is organized in the form of dates and stamps to act in the palace of transactions in databases. It has the capability of rolling back when data is not completed. It a kind of class that is highly flexible and users can utilize information without changing any form of sensitive data. It is majorly applicable in banks and object bases, among others. Besides, there are also the multimedia databases which entail videos, message and audios, among others. WWW is also another source of data that can be mined, and it encompasses information that can be identified by uniform resource locators.

The data mining knowledge is the tracking patterns, and this entails knowing the prototype in the data sets. It encompasses recognizing data being utilized at regular intervals or chains being utilized over time. Additionally, it also encompasses classification, and this entails data mining forms that force one to assemble various forms of data in distinguished categories. Associations are also a type of knowledge that is correlated with tracking patterns that contain independently connected variables. The data miners will look out for specific attributes that are closely connected. Also, outlier detection can be used to detect the anomalies that can be detected in diverse sets of data. Clustering is also another type of knowledge that entails putting similar sets of data based on their similarities. The knowledge that can also be utilized is regression, and it entails modelling and planning. It focuses on specific variables amidst others. Prediction is the most important and valuable because it is used to determine the types of data to be utilized in future.

The story is interesting because it opens up minds on various issues regarding data mining. It gives insights on various types of data set and the different applications that be used in analyzing them. Cukier explains that data mining is critical because it helps business thrive in different ways such as customer acquisition and retention, risk management developing marketing insights and regulating advertising issues.

Conclusion

In the Twentieth century, the Franklin De Roosevelt government launched the social security act which became the largest `data-gathering project in America, and IBM won the contract. IBM gathered the date of 23 million employees and stored information in punch cards because it was the most reliable technology then. Bletchley developed an electronic digital known as colossus machine it was designed for cryptanalysis tasks, but it gave a highly reliable speed. The major challenge was big data, but it was solved through key component data application known as velocity. In this generation, it viewed data in three sets known as velocity, volume and variety. It utilized knowledge discovery from data (KDD). The path that followed includes data, selection, target date, preprocessing, transformation, data mining, data interpretation and obtaining of knowledge.

The application area is government institutions and non-governmental organizations. The types of data to be mined include; the army and the engineering sectors. The knowledge to be mined includes; discoveries and science.