Managing Data

Online Transactional Databases is an essential system for supporting transaction-oriented applications over the internet. One of the burning issues in data management is the security of transactions over the internet. Individuals and organizations are afraid that their personal information might get into the wrong hands by transacting over the internet or they might even lose their finances. Online transaction processing systems have the capability of handling well credit card for clients. Some of the systems put in place to ensure security include data encryption which ensures sensitive information is not accessed by unauthorized persons (Talukder & Chaitanya, 2009). There is the use of a safe login screen where the login system where the login screen is made as secure as possible. The operating system of the OTP is updated regularly to ensure that the latest safeguards are updated and one is ahead of the hackers. The OTP systems are compliant with the law of the land especially the payment card security standards that ensure safety of transactions as well as legal compliance. 

Data mining involves the use of software to patterns, trends and relationships in Big Data that assist in forecasting for the future and assist in effective decision making. As the amount of data increases exponentially there is a need for information systems that can both convert this data into meaningful information as well as filter the only relevant data to a particular situation. An important task performed by data mining tools is clustering which involves generation of clusters or groups with similar kinds of items. For instance, the government can employ data mining to determine the rightful beneficiaries for Federal and State programs as this would ensure there is no waste or loss of finances. Organizations cannot survive without external data especially consumer data, competition data and generally the market trends. Data mining tools assist the organizations to collect and analyze the external data and build models based on historical data. For instance, data mining tools are used to analyze competition data with regards to which fronts in the industry are competitive and what are the success factors. The data mining tool also analyze the competition data to determine where the niche in the market exists. Furthermore, it is important for organizations to understand the financial capability of their external stakeholders such as suppliers. Historical data can be used to generate models that identify the financial capability or ability to access credit which influences their ability to supply raw materials without delay. 

William H. Inmon defines a data warehouse as “a subject-oriented, integrated, time-variant and nonvolatile collection of data in support of management’s decision making process” (Han, Kamber & Pei, 2011). Subject-oriented refers to organization of data based on major subjects such as customers and sales rather than focusing on general information. This is essential in ensuring that simple, concise and relevant information is used in the decision making process rather than using general information. Data warehouses are said to be integrated which means huge data and data that is scattered all across the organization is collected to a central location. The data warehouse has the ability to store data in numerous formats, convert the formats for usability purposes and integrate heterogeneous data sources. The integration is made possible through encoding structures and naming conventions for the different data sources. Data warehouses assure both quality of data as well as integrity by collecting data from numerous sources (both internal and external) and analyzing historical trends that support strategic decision making. 

 

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