One of the main differences between a relational database and a data warehouse is the format of data presentation and processing. A relational database is built up by a collection of tables, each table having a unique assigned name. Each table consists of a specific set of rows that represents the relationship between the different groups of values in each row. On the other hand, a data warehouse consists of data structures that are populated with data from the OLTP databases. The data structures of a data warehouse are also altered to fit a flatter schema. Basically, the warehouse structures are expressed as star schemas through views of fact and dimension tables. Another significant difference is that a relational database must be normalized to ensure that every table consists of unique sets of data. Normalization in relation databases ensures that there is only a single copy of any bit of information in the database. A data warehouse does not emphasize on normalization and it is possible to store more than one version of a given bit of information on a data warehouse. Data warehouses are also potentially much bigger compared to the relational database. Relational databases are modeled to hold active data while a data warehouse stores the additional information and old historical records of an organization (Elmasri, 2010).
The primary difference between database requirements for operational data and decision support data is the time span required. Operational data is required for the day to day operation of a business and it requires a short time frame. In contrast, decision support data requires a longer time frame since it is used to make complex decisions about the future of the business. Also, decision support data should be presented covering all the different levels of aggression and dimensions. In contrast, operational data should focus on representing individual transaction as they happen in real time.
Managers in large organizations such government’s institutions are in need of rapid access to information in order to make decisions concerning the institution. For example, the department that supplies water in the city requires collects vast amounts of financial records, customer records, input records and human resource records. A database system simplifies the ease through which managers in such an institution can identify possible improvements that will facilitate efficient water supply throughout the city. The database system could also prove to be useful in the health sector. Data can be brought together from different regions by the aid of the database systems and queries ran to help identify common diseases affecting locals. The ministry of health can then design countermeasures to prevent the spread of the disease. Databases also facilitate decision making in projects that involve groups. All the individuals in the group get access to the information even if they are working in different geographical location (Kimball, 2011).
Data warehouses and data mining play a vital role in decision making. Normally, data warehouses are mostly applied in the business world. The large business organization requires computing information from all their branches in order to make decisions. Also, the fact that past business experiences affect future operations of the business demands that historical records must be preserved by the business (Vercellis, 2011). Data warehouses are the most effective for this environment due to the large volumes of data involved. Modern day social media holds vast amounts of data from the millions of users across the globe. Decisions such as upgrading the social media system can only be based on information achieved through data mining. Other institutions that could utilize the services data warehouses and data mining are large government institutions such as the tax and revenue collection departments.
References
Vercellis, C. (2011). Business intelligence: data mining and optimization for decision making. John Wiley & Sons.
Elmasri, R., & Navathe, S. (2010). Fundamentals of database systems. Addison-Wesley Publishing Company.
Kimball, R., & Ross, M. (2011). The data warehouse toolkit: the complete guide to dimensional modeling. John Wiley & Sons.
