Business Studies Paper on Business Intelligence

Business Studies Paper on Business Intelligence

Data Management Architecture     

Data management refers to the process of developing and deploying guidelines, procedures, and architectures to be used by businesses in managing information lifecycle. Organizations have varied options when choosing the approach to use in data management. One of the approaches is master data management (MDN), which is a detailed method of managing data that enables businesses to connect all the important information to a single data file. Another approach to data management is the big data management, which refers to the collection, organization, and administration of huge amounts of data both categorized and uncategorized.

Key Concepts of Data Management Architecture

Data Warehouse

            A data warehouse is considered a unique data design that enables quick retrieval of data and easy computation of complex queries over large volumes of data. For instance, a data warehouse designed to handle production information will allow easy input of data. Data warehouses enable companies to perform their business operations efficiently and extract useful information from large volumes of data (Patel, 2012). Companies use information in data warehouses as the basis for making decisions. The ease of accessibility of information with data warehouses enables the generation of information like operational reports promptly (Galhardas, 2016).

Data Lake

            A data lake is a centralized storage system that keeps business data to be used in executing different workloads. Data lakes were created to enable the capturing of new types of data that businesses wanted to exploit. A data lake has the following benefits to a business: collecting and storing large volumes of basic data at a low cost; storing different types of data in the same source; enabling manipulation of the data; and defining the data design at the time of use (Cito Research, 2014). Other benefits of data lakes to businesses include enabling a new kind of data processing and analyzing individual subjects based on specific cases. The pioneering data lakes were designed to deal with web data in companies like Yahoo and Google (O’brien, 2015). All the information stored in a data lake is categorized as aggregate, derived or raw. Raw information refers to data that can be utilized in start-up projects. Derived data is any data that is obtained after calculations, cleansing, and integration of information to enable reuse. Lastly, aggregate data is made up of a collection of results and information derived from consumers (Valez & Agrawal, 2016).

Data Mart

            A data mart is a mini data warehouse that contains a limited scope of information compared to a data warehouse. Data marts may hold summarized information about a specific department that is tailored to cater for the needs of a specific department (Ponelis, n.d.). In big companies, data marts form the foundation for data warehouses if a sequential approach is used. Several data marts make up the data warehouse in a company. In addition, organizations can use data marts as cost-effective alternatives to data warehouses (Firestone, 2002).

The Uses of Data Warehouses, Data Lakes and Data Marts in an Organization

The main function of a data warehouse in an organization is consolidating information from different sources to avoid conflict. Data warehouses achieve this by gathering information from different sources and then making the information available in a unified and harmonized format (Levy, 2016). To integrate data in warehouses, organizations follow three steps. The first step is to extract the data whereby the data is uploaded regularly from different data sources to an initial server before being directed to the data warehouse. The second step is to transform the data, whereby different datasets are harmonized by changing the format and resolving any conflicts. The third step involves loading whereby the prepared data is fed to an analytical program that performs the necessary calculations, identifies trends, creates reports, and performs additional business intelligence functions. A data warehouse is also referred to as a schema because the data contained in it is structured (Levy, 2016). Companies use data warehouses for validation purposes. For example, validating the trends of purchases between the residents of one city with another.

The main function of data lakes is to enable organizations to store massive information at a reduced cost. Moreover, data lakes allow flexibility and easy access to information. Data lakes work with information from multiple sources that include data from the social media, Hadoop files, and relational databases (Krause, 2015). Data lakes also keep a record of the original data. Data lakes only utilize schema when extracting information from the lake in order to answer the business intelligence query that has been made, unlike data warehouses that use schemas in writing the data (Levy, 2016). For instance, businesses can use data lakes to analyzed unstructured data on aspects such as telemetry and equipment readings.

The main use of data marts in organizations is providing analytical tools for a limited scope of data, for instance, analyzing data just for a single department or domain in a company. Data marts play a critical role in data management by preventing one department from messing up data needed by another department (Levy, 2016). Data marts also make the process of analyzing data simple by focusing on small amounts of specific data as opposed to dealing with all organizational data contained in a warehouse (Evans et al., 2012). For example, data marts can be used to analyze data aggregate on aspects such as the number of customer and the transactions in the preceding six months.   

 

Works Cited

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            Warehousing: New Definitions and New Conceptions. [Online]

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O’brien, J., 2015. The Definitive Guide to the Data Lake, s.l.: Unisphere Research.

Patel, S., 2012. What is Data Warehouse?, New Jersey: Fairleigh Dickinson University.

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Valez, F. & Agrawal, S., 2016. Data Lakes: Discovering, Governing and Transforming Raw

            Data into Strategic Data Assets, s.l.: Persistent.com.