An array of homogenous information that can be edited, retrieved, and stored at any given period of time is called a relational database. There are numerous variations of databases, but the most prevalent type is OLTP. There are also databases grounded on CSV files and some organizations may even use Excel worksheets (this methodology is no scalable). The key characteristic of a relational database is that it is limited to one application only (Hughes, 2013). These databases are performant in terms of real-time transactions.
Therefore, this type of databases is aimed at a robust processing of data rows one by one. A data warehouse, at the same time, stores the data about transactions coming from different sources. This data can be used for analytics. Data warehouses are typically OLAP and not OLTP so that it could be easier to analyze the obtained data and manage transactions (Howson, 2013). The data models that can be used are either dimensional or enterprise. In comparison to relational databases, data warehouses allow an unlimited number of applications assigned to a single data warehouse. In a corporate environment, data warehouses may be much more useful than relational databases but complex queries are better handled by OLTP-based applications.
There are certain differences between decision support data and operational data that pivotally impact the process of interaction with these two types of data. The majority of these variations relates to the volume of transactions and their types (Howson, 2013). Decision support data is commonly represented by read-only transactions while operational data gives the possibility to update transactions when necessary. The former should also be updated from time to time so as to display the operational data correctly (Hughes, 2013). In this case, the volume of transactions is much lower in decision support data whereas operational data is categorically beneficial. Operational data is a representation of a single transaction (assembling the information from more than one table) while the decision support data make the best use of the transaction data contained in the operational data (Rausch, Sheta, & Ayesh, 2013).
When it comes to decision-making, businesses can use their databases to do a number of critical things intended to maintain competitiveness of the business and the overall level of performance. The first example is keeping track of all transactions that occur within the organizational environment (Howson, 2013). This is needed to observe the process of data exchange in real time and manage the data on the fly. The second example is the data breakdown that is completed after the data exchange process is over. This step can be helpful in terms of providing the executives with the information that relates to the strategic vision of the company and the ways to improve performance (Rausch et al., 2013). The last example is the application of the obtained data within the business environment so as to ensure that the decisions that are made by both managers and employees are based on relevant and accurate data.
The first example where data warehouses and data mining can be used to support data processing is mobile phone industry. These service providers use data mining to stop their customers from converting to another similar company. This allows the companies to create targeted offers intended to spark interest in the customers that are on the verge of opting out. Another example is e-commerce. Here, data processing ensures that the customers are exposed to various cross-sales meaning that the website is going to offer a product similar to the one that you have just checked out or a product that is usually bought together with the last item. The third example is the collection of comprehensive data about customers in supermarket chains. In this case, data mining allows the companies to obtain information concerning the preferences of their customers and build predictive patterns.
Howson, C. (2013). Successful business intelligence: Unlock the value of BI & big data. Emeryville, CA: McGraw-Hill.
Hughes, R. (2013). Agile data warehousing project management: Business intelligence systems using Scrum. Burlington, MA: Morgan Kaufmann.
Rausch, P., Sheta, A. F., & Ayesh, A. (2013). Business intelligence and performance management: Theory, systems and industrial applications. London, UK: Springer.