The value of a business data at their disposal is immense. Big volumes of data necessitate database systems that transform them into meaningful statistics. Many organizations are using relational databases to leverage the collection and analysis of enormous volumes of data. As a CIO of Britam insurance company, I intend to design a data warehouse that collects and analyze data using web analytics and operational analysis.
The company obtains data from the web and integrates it with the operational data it obtains from its clients to arrive at a conclusion and forecast on the future. The data obtained in a mining process need to be analyzed, transformed and aggregated at different levels of abstraction. Thus, data content is one value added feature utilized by e-commerce sites to convey a relationship with the user. Data content is comprised of textual and graphic representation sourced from HTML/XML pages and scripts. The content data involves the semantic or structural meta-data that are embedded within pages sites, attributes descriptive keywords among others.
Data warehousing is a process of storing the entire company’s data in a big repository meant to provide seamless integration and guide in the process of decision making. In a data warehouse, operational data needed for business operations are continuously changed. Tables in this database are continually refreshed by removing old data. On the contrary, patches of old data are still available and their accumulation leads to voluminous amounts ranging in terabytes. This distinct feature has enabled many organizations to utilize the massive storage capabilities of warehouses to realize remarkable benefits in the way they handle their data.
Big Data gives Britam Insurance a competitive advantage over it competitors in the insurance industry. The ability to analyze the data provides enormous breakthroughs in terms of response to client needs, quality of services, customer satisfaction and cost efficiency. The availability of up-to date, quality-controlled data is changing decision making processes within the organization leading to gains in quality, warranty costs, customer satisfaction and profitability
In the design process, the design team comprising of programmers, database administrators and information security experts should critically evaluate each and every phase to come up with a standard warehouse that satisfies the organization's objectives. In order to design a database that can support the stated functions, the standard software development processes are followed. This may include;
- Defining the problem facing the insurance company in data storage and analysis
- Analyzing the need for a relational warehouse
- Data modeling processes
- Designing a prototype
- Development of the warehouse and documentation
- Carrying out tests an review operations
- Defining and applying data warehouse best practices
In the design process, the design team comprising of programmers, database administrators and information security experts should critically evaluate each and every phase to come up with a standard warehouse that satisfies the organization's objectives.
The data house is structured into functional groups or specialty areas such as employee details, departments or project characteristics.
A schema table is represented below to signify the flow of data in and out of the relational database.
Using an entity relationship data model, abstract schemas are designed and denoted with various entities. Likewise, in object-oriented data model, objects are denoted with different classes with a provision that segregate functional primary data from processes that are involved in creation modification of such data. The functional areas are primarily mapped out during the problem definition stage and should be independent of other processes. This is one crucial stage that should not be overlooked by the design team in order to produce a functional database.
A well designed warehouse will allow history rewriting and successful data rollback. In order to achieve this, the designers will have to introduce data in appropriate granularity levels to allow administrators to update the rights of historical data.
Data in the warehouse is non-volatile and as such would require mass loading. The data should be carefully transitioned from the current information systems to the new database in a careful manner through specialized procedures. Through the use of tools such as clearing tools, the data is scrubbed, audited, and safely migrated. Load utilities also came in to be used in the transfer of clean data to the new established warehouse. The safely transferred data should be guarded by use of appropriate credentials administered by the administrator.
Britam Insurance Company will implement a relational database. The process will involve the translation of ideas into sophisticated plan that will permit the development team to put together a relational data warehouse schema in lieu of the key functionality of the warehouse.
Data is moved from various databases to the new created warehouse where cleaning, verification and validation are conducted before it is finally stored in designated marts. The various sources of data represent sources such as clients, customers, suppliers, the management and other third party players. For instance the clients table link to the particular department data marts which subsequently link to marketing and decision support systems.
A simple data flow diagram to illustrate the movement of data in and out of the company is as shown.
The relational database in Britam bears the exact tools that ensure efficient organization and scalability of data at a given instance to accommodate dynamic operational data. Huge volume of digital information derived from the business. Likewise, the company has implemented a range of information systems to aid in the management and operation of its operations. All the data sourced from these systems are aggregated together and stored in the warehouse. The warehouse accommodates batches of old data which swell with time to quadrillions of bytes. The massive capability of the warehouse ensures that such data is stored and retrieved efficiently.
In conclusion a shift to a relational database is significant to the company in ensuring safe and efficient storage and retrieval of data. The database can be cloud-based to leverage cheap and virtually unlimited storage facilities for large volumes of data. This paper has sufficiently outlined best practices that can be employed by Britam Insurance Company in regard to warehousing needs.
Reference
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