Management of Data and Technology (Coursework Sample)

Management of Data and Technology
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Data Warehouse Architecture
In the current world, data has been crucial to any firm. Most of the firms, regardless of the field of operation they are in, rely heavily on their data. The data collected must be analyzed and stored to help the company managers and stakeholders in the critical decision-making process. One of the systems the firms use to diagnose, and query data is the Data Warehouse. Data Warehouse can therefore be regarded as an information system used to analyze and query all the data. It contains historical and current data, which simplifies reporting and analysis of data in an organization.
Furthermore, Data Warehouse architecture refers to the structural modification of an organization in data storage and collection. It makes it easy for data to be formatted, cleaned, and properly managed to make sense. Data stored in the data warehouse is easily digestible, making it easy for companies to make quick, complex decisions and business intelligence insights. The following paper will discuss the data warehouse architecture, data warehouse components, including the data, access tools and database, the different ways to transform data, and the trends involved.
Data Warehouse Components
Data Access Tools
Data access tools in a data warehouse allow users to interact with the data in the warehouse using different technologies. Data is used to gather insights, manage, prepare reports and make crucial analyses. The other data access tools currently in the tech world include query reporting tools, OLAP tools, and data mining tools. Reporting is done using graphs and chart visualization; this enables monitoring data in the warehouse by organizations. Data mining tools automate discovering new trends and patterns and establishing a relationship between them by mining large amounts of data. OLAP tools extract data from numerous relational data sets and provide a faster way to analyze multidimensional data (Santoso, 2017). These tools make it easier for the people and especially the data analysts, and fasten on operating a warehouse.
Database
The database is the heart of a data warehouse and the most vital component. It is in the database that the data is stored, managed, and analyzed through different queries. However, it is always necessary and advisable for the firms to choose the appropriate database before creating a data warehouse. The following databases are suitable for use; they include; data warehouse applications used for data management, analytics databases for making analysis, relational databases that are centrally arranged and cloud-based and hosted online. Due to the large amounts of data available, in-built memories have become a popular trend to save on costs of RAM and improve real-time performance (Visscher, 2018). Thus, every activity performed in a database is possible such as storage for the big data, analysis of the data to make reports that would help in decision-making and management of the data.
Forms of Data Transformation
Data Smoothing
Data Smoothing refers to a form of data transformation that involves algorithms to remove unnecessary data from the available dataset. Furthermore, data smoothing takes note of vital attributes in a dataset and can recognize even slight modifications. The modification recognition is significant because, through it, notable trends can be recognized. The whole process is possible after filtering the original dataset. Filtering can be used to determine trends in prices and economic analysis. It is a solution for most companies who encounter a large set of data daily. Data smoothing helps big companies using big data to comprehend the data quickly to determine hidden trends and patterns (Dhir, 20). It has been proven that most companies prefer smoothened data over unsmoothed one because smoothed data makes pattern prediction better rather than creating wrong signals.
Data Normalization
According to Konikov (2018), Data normalization is regarded as a form of data transformation where raw data is converted into a format that makes it easier to analyze and process data in a specific way. Raw data cannot be reliable in making a decision, and therefore it has to be analyzed and processed. During the data normalization process, the information is modified so that it appears to be within a given range. It applies faster data extraction and data mining. During the data normalization, data is transformed into a form that everyone can understand without difficulties.
Furthermore, the process helps in the reduction of ambiguity of data by making it clear to each individual using the data. Application data mining techniques make it more effective, efficient, and more accessible. The following are the three data normalization methods; Min-max, Z-score normalizations, and Decimal scaling. Min-max transforms unstructured data on a scale of 0 to 1 (Konikov, 2018). Furthermore, the data normalization method is crucial as it eliminates unwanted characteristics and removes data redundancy.
Critical trends in Data warehousing
Due to the increased volume of data daily, companies have opted to adopt cloud data warehouses due to their flexibility and scalability. This creates room for the constantly growing volumes of data and the needed processing capability. The best thing about the cloud data warehouse is that the cloud providers offer discounts on the resources provided. The discounts offered are essential because they enable clients to meet their goals and budget estimates. Nowadays, most cloud providers provide regular and automatic backups to promote fault tolerance. The automated backups are very efficient because no human intervention is required during the process; thus, data integrity is assured during the process. Many opt for a data warehouse as a service as it saves on hiring an in-house manager to perform managerial duties. These serve all the administrative tasks and save on maintenance and equipment acquisition (Garcelon, 2018). Data warehouses are vital these days as they enable the integration of big data into the firm’s system.
Big Data
Big data refers to a large volume of data that has to be processed and analyzed to make valuable decisions that can help organizations. The processed data is helpful to the company stakeholders and all the managerial level staff members to make critical decisions. The decisions made can either help in the development or the failure of the company. It is argued that a company's success is majorly dependent on the critical decisions that the company's stakeholders, such as the executive, make. Furthermore, big data can exist in different forms. Big data can be structured, unstructured, or semi-structured. Big data entails the following concepts; data visualization, sharing, analysis, storage, and mining.
Furthermore, big data can be characterized by variety, velocity, and volume. In extensive data analysis, variety points to the fact that the data is gathered from multiple sources. Velocity refers to the rate at which information is created frequently, especially daily. Volume implies the large volumes of data sets being produced from various sources. The different data sources range from many sources such as social media platforms and blogs, human interaction, and machines (Rai, 2020). In this scenario, we will look at the practical use of Big Data in our daily life and its demands on the organization in the management of data and technology.
Uses of Big Data
Big Data in Retail
Several years down the line, most companies gather information through sales, inventory, and surveys. Nowadays, with Big Data, companies have been able to maximize these data to boost their sales, improve productivity, and offer promotions to grow their businesses, among many more key performance indicators. Firms tend to use the Internet to track customer movements and activities; most used routes and the time these customers spend hovering over different products or events. Besides, retailers use social media platforms such as Facebook and Instagram to determine what customers perceive of their brand and areas to be improved (Rai, 2021). Through social media platforms, firms can retrieve helpful information by discussing the products with their peers and even criticizing them. It is said that people and especially the youths demonstrate their real feelings towards a particular product or service when discussing with their fellow peers. In the current period, the platform majorly used by the youths tends to be social media platform. It is, therefore, very crucial for the retail firms to pay much attention to what the people are saying concerning their products or services on the social media, because the comments can either gain the retail firms more potential customers or scare away prospective buyers. Retail firms take advantage of the social media platform to grow in the market by designing the products to meet customers’ desires and specifications (Rai, 2021).
Demands Inflicted by Big Data on Organisations.
Big Data has been there for a while, and most companies understand that they will gain valuable information out of it if they utilize the data maximumly. This can help make crucial decisions that may adversely affect the growth of a business. Achieving customer satisfaction and needs using big data analytics is among the demands. Organizations are required to use the available data to measure and develop products to make their business grow. Big Data demands that companies use predictive analysis future predictions. It can be us...