CIS499 Week 11 PowerPoint

CIS499

Strayer University

17 November 2018

Introduction

Mamset Technologies Inc. conducts data collection and analysis using web analytics and operational systems. There are various operating systems from where the data collected, and over time the number has created challenges for the company as the data managed has increased. The current solution lacks sufficient capacity to allow Mamset to deliver value to its clients, leading to the need for a better data solution. Operational systems usually operate independently, and as they increase, they become unwieldy and can lead to cases of data redundancy, necessitating the deployment of data warehouses that consolidate them all into a single solution that is easier to manage.

Need for Data Warehousing

Data warehouses are central repositories for storage of data, with different data sources feeding into the warehouse. These various sources will be operational systems, each covering their clear data and as such will result in the data sent to the data warehouse being of varying formats. As such, a data warehouse will simplify the data processing of different data sets by having it done in a central location instead of disparate operational systems..

Mamset manages data for a number of clients, with the data collected from the customer’s operational systems including marketing data, financial information, sales data and many more. The data warehouse is therefore on data from external sources, aggregating them to provide a database that is searchable, and reportable. Data consistency is a big part in the development of a data warehouse, making it possible to establish a uniform process for all kinds of data. One of the primary reasons for the implementation of a data warehouse is to provide insights from the data it processes. These insights are put into decision making, supporting management activities and strategic planning. We must consider the importance of that aspect of management; it is essential that the insights from the data are not jumbled and confusing to comprehend. Senior management needs to make decisions on all parts of the organization, covering different departments each producing data different from the other (Krishnan, 2013). If each department were expected to produce their data insights for the management, it would be a vastly different situation, one characterized by different approaches to data processing and formats for reporting the results. A data warehouse eliminates this by providing a solution for centralized processing, where results are consistent even if they are from different departments.

Decision making as pointed out is a vital part of a company’s senior management. As a result, if fed with the right kind of information. A significant role in providing information for strategic planning is in the analysis of historical data that is the information from an organization’s past activities. Even in a situation where the past performance is not expected to have a bearing on future performance or events, an understanding of historical performance should provide vital insights applied in decision making (Krishnan, 2013).

Data mining involves the processing and analysis of large chunks of data to produce patterns and discover relationship from seemingly unrelated data sets. Data warehouses collect data from different areas of a business, and in their raw and unstructured state, they cannot provide the necessary insights expected. Data mining activities, on the other hand, will sort out through the massive amounts of data and will identify patterns from vastly differing data and determine how various data sets relate to each other, with the goal of producing insights to provide predictions, identify problems and even provide valuable solutions in some cases. Data mining tools and techniques make use of various parameters that are applied to the unstructured data to produce varying results (Vaisman, & Zimányi, 2014).

Beyond the greater analytic capabilities of data warehousing, another of the most critical factors of implementing the solution is the data visualization tools that come with the solution. After data processed, it needs to be viewed by the users who need it, with the manner of presenting the data is an essential part of the process. While text-based data presentation was used in the past, the industry has evolved whereby text-only data visualization is no longer a viable option. More and more tools brought into the market that can quickly identify patterns, trends, and correlations in data that would otherwise have gone without detection if only text-based solutions used. Data visualization tools used in data warehouses go beyond the text and simple charts and bar graphs. Users have access to a dashboard that can provide infographics, heat maps, geographic maps, sparklines, as well and much more detailed versions of graphs and charts (Ferdinandi, 2013). For analytic purposes, data visualization provides a much more effective solution of approaching data, providing opportunities to interact with the data and manipulate it in real time to determine various combinations and correlations.

Speed is a central requirement for most technology solutions. The capability to carry out a task in a short amount of time will put one solution over another. An information technology solution can perform vastly faster than a human user, covering capacity that would otherwise require a large number of human users to conduct successfully. Business decision making and other demands for data do not have set times, therefore requiring that the solutions put in place to process data can produce the needed results whenever queried. Shorter processing times mean that decisions can be made much faster and areas that need the data can receive them promptly. A data warehouse has these capabilities, with higher processing capabilities than normal operational systems, and capable of handling much more massive amounts of data (Ferdinandi, 2013). They will also process data devoid of bias present in human users, producing results that can be trusted.

Data security goes beyond safeguarding the data an organization holds, with potential breaches in security resulting in compliance violations and reputational damage for the organization involved. The main security advantage offered by a data warehouse solution is the limitations placed on access to it. Many points of entry and exits within a database complicate security as it becomes difficult to monitor all of them and enforce access control requirements. A data warehouse apart from concentrate data processing will also centralize data security, allowing for a system of controls on access to data. Encryption can be applied to the entire data warehouse, limiting access to only the users with the decrypting key (Krishnan, 2013).

Database Schema

The database schema is an illustration of a database’s logical view, focusing on either the entire database or specific areas and components within it.it provides definitions on data organization and the relationships among entities, attributes and so forth. A schema for a section of the Mamset database is as below.

Figure 1: Database Schema

The above schema represents employee record for one of Mamset’s clients. It presents information on employee and how they relate to other aspects of the organization. The relationships include those with their department, titles and salaries among others. (Ferdinandi, 2013).

Entity Relationship Diagram

Figure 3:

The above entity relationship diagram, presents tables of department employee, salaries and projects. For “works at” object, there is a one-to-many relationship and on the “works on” object is a many-to-many relationship (Beynon-Davies, 2013).

Data Flow Diagram

The Data Flow Diagram (DFD) below illustrates the setting up of functionalities for the system.

Figure 4

Flow of Data for use in the Data Warehouse

Figure 5: