Introduction Databases and data warehouses are increasingly being applied into business activities, as they reduce time spent as well as increase efficiency and speed at which decisions can be arrived at. Intelligent decisions can be made through the use of statistical analysis into data collected. Huge volumes of data are being created by businesses every minute from different branches that could be geographically spread. Not only is it important to collect this information, it is also imperative that the data be looked into to ascertain patterns that can create an edge for the business.

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Online Transactions Database (OLTP Databases)
A relational database that is developed for online transactions operates using OLTP (Online transaction processing). OLTP systems are distinguished by numbers of short online connections that deliver source data to data warehouses; it is usually limited to a single application. OLTP is a set of programs designed to support transaction oriented applications. Such as financial queries, and retails sales, i.e. where response is required in the shortest time possible. Such a relational database is designed to be concurrent, i.e. can avoid a particular point of failure, as they are also decentralized. Such a database is also designed to maintain data reliability, in a multi-access set-up. This online transactional database exposes ongoing real-time business processes. This database does not have to be relatively big, as it can operate well if historical data can be recorded. Data in this relational database has to be backed up constantly due to its crucial role in the business process. Loss or breach data in this database can likely lead great financial losses. It is also important that the database is backed up and secured round the clock, in terms of access and maintenance of data integrity (Coronel, Morris, & Rob, 2013).

Decision Support database (OLAP Databases)
A data warehouse optimized for processing and summing up large amounts of data, operates on OLAP (online analytical processing). This is a database that runs many business intelligence applications. OLAP executes multidimensional investigation and models the data to be used to support business decisions. These decisions range from forecasting, budgeting and preparation of financial reports. This database, discloses multidimensional view of different business operations. The space requirements needed on an OLAP database are big due to aggregate structures and historical data thus requires more indexes than an online transaction processing database. Data in this database is not backed up as frequently as in the previous data base mainly because OLAP subsists on other OLTP databases.

Applications of Decision Support Databases
With billions of transactions happening all the time in organizations all across the World, It is not enough to just collect this information and store it. Organizations are mining this information and gathering intelligence from it, to make accurate and reliable business decisions that will steer the organization to a successful future.
Clinical Decisions: Data warehouses are assimilating and analyzing all information collected from medical organizations with many inpatient and outpatient facilities. This data can be used to support decisions not only at the organizational level, but down to the individual specific patient. Apart from the individual patient records, data warehouses are also able to aggregate and analyze provider, financial and facility data to form a basis through which the relevant stakeholders can make a well informed decision that will reduce costs while improve outcomes and efficiencies. Health analytics are used in billing and collection of revenue in healthcare facilities. This information can be used for business forecasting.

Web searches: Document driven DSS enable users to conduct conclusive web searches and enhance the surfing experience of the user. When a user goes online to search for a product or service, the search engine assists them by bringing the most relevant results first. This reduces the time they would have spent going over web pages.
Chat and Instant Messaging Functions: Communication driven DSS are able to drive communication software, to assist users conduct meetings and achieve better collaboration (Hillard, 2014).

Applications of Data warehouses in large Organizational Environments and trend analysis
Banking environment: Data warehouses are used in deriving predictive models through customer transaction, to come up with a trend to help banks come up with new products. Products such as new credit card products, among others to meet the ever increasing customer demands, can be analyzed through looking into repeating patterns and advanced statistical analytics. The trick is how to understand all the data available for decision makers to answer strategic critical questions. Statistical correlations can be used to predict the probability of future outcomes, for each individual customer. Customer data in regards to interactions, attitudes, descriptions and behaviors can be analyzed. Because data warehouses can provide reliable data since data is collected from numerous data points, the final predictive models can be accurate to provide enough information for the formation of new products (Rainer, 2012).

Customer Retention: Acquiring new customers and retaining them is the key to any successful business. This is becoming increasingly harder with the number of competitor in the market. This is where data warehouses can be used especially to reward customers for being loyal to the company. With proper application of predictive analytics, a business can lead a better customer retention program.

Fraud Detection: Fraudulent transactions can be flagged instantly through use of technology. Data warehouses are key, in this application, as organizations can reduce cases of fraud through identity theft for example in insurance claims, as the data can be flagged through information mined from databases (Rainer, 2012).

    References
  • Coronel, C., Morris, S., & Rob, P. (2013). Database Systems. Mason, Ohio: Cengage Learning.
  • Hillard, R. (2014). Information-Driven Business. Wiley.
  • Rainer, R. (2012). Introduction to Information Systems: Enabling and Transforming Business, 4th Edition. Wiley.