Essay Example on Ensuring Quality Decisions: The Role of DSS

Introduction

Accessibility to relevant information is an essential requirement for the active decision-making process. The relevance of the data largely depends on the timelines and accuracies. The steps for successful decision processes include collecting, analyzing, and storing data. Decision Support Systems (DSS) are those structures which help the business operators in proper decision making whose main feature is the usability (Turban, Sharda & Delen, 2014). The significant component of DSS is its capability of repeating usage due to the inclusion of massive databases that assist in storing the information, which makes them evolve hastily within the fields of businesses. The most significant DSS development is the Web-based one. They help the business analyst by giving them decision-support information. This paper aims to research web analytics, web mining, and social analytics, which are the web-based techniques used to collect, analyze, and conceptualize information.

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Web Analytics

It is the process where the right information is extracted from the data that have been generated by a web page. There are about three types of data that are produced when the Web page is visited. The first type of data is that which is automatically created and stored in the access logs, referrer logs, agent logs as well as client-side cookies, which are in the servers. The second type of generated information is the user profiles, while the third is the metadata, like the attributes of the page, content, and the usage data (Turban, Sharda & Delen, 2014). The user behavior can be well understood by the use of the information from the Web servers when it is well analyzed. For instance, a company can be able to know various patterns from the clickstream analysis. The clickstream analysis is a helpful tool to see the time the site is accessed or visited. The information on the clickstream analysis can be used to determine the sections to place an online advertisement.

Web analytics technologies

Various tools and techniques for Web analytics are there in the market places today. Web analytic tools are becoming more popular because of their power of measuring, collecting, and analyzing the internet data (Bekavac & Garbin Pranicevic, 2015). This Web analytic technologies, have the potential of revolutionizing how businesses are operated over the Web. Web analytics can measure Web traffic as well as e-business and also assessing and improving the performance of an e-commerce website. Furthermore, Web analytics can trace the information regarding the number of visitors and viewers of a website page, which can be used in market researching.

Web analytics are of two major groups, which are off-site and on-site. Off-site Web analytic tools are the analysis and the measurement of the website regarding the products taking place outside the site. It is all about measuring the potential audience and prospects, visibility, and opinions that happen on the internet. The on-site Web analytic tools measure the behavior of the website visitors when they are on the page. It is all about measuring the performance of the website within the commercial context (Turban, Sharda & Delen, 2014).

The primary on-site Web analytic tool used today is google analytics. Others mainly used include Yahoo and Microsoft. These on-site Web analytic technologies use two methods to collect data. The first technique is the use of server file analysis, which records all the file requests a browser makes. The other technique is known as page tagging that uses JavaScript, which is included in the page for making requests to third party analytics.

Web Analytics Metrics

Web analytics practices give access to many a large amount of essential marketing data that can be used to advance a business (Turban, Sharda & Delen, 2014). The insights obtained from Web analytics can be effectively used in managing various marketing efforts within an organization. Web analytic programs utilize multiple metrics that are within four significant groupings. The first category is website usability, whose parameters include page views, time on site, downloads, click the map, and click paths. The other group is the traffic sources whose metrics include referral websites, search engines, direct searches, offline and online campaigns.

The third category is the visitor's profiles, whose metric includes keywords, content categories, day, and quay page profiles. The fourth category is the conversion statistics, which is defined per the specific marketing goals. Its metrics include new invitees, inveterate visitors, mains, sale alterations, and exit rates. In every category, the metrics are developed for the needs of the specific organization. A weekly dashboard that has exact numbers in the form of percentages can be created to indicate if the organization is succeeding or the possible marketing issues that require addressing.

A consistent evaluation of these metrics, together with the available marketing data, should be done to come up with a quantifiable marketing program. Web analytics technologies are sources of invariable insights that help to understand visitor behavior. Two more applications give a qualitative view of the behavior of online visitors. They are designed to give an overall report and feedback from the visitors and customers. The two applications are Customer Experience Management (CEM) and Voice of Customer (VOC). The CEM mainly concentrates on" what and why" questions by looking at the issues on the Web applications. On the other hand, the VOC usually focuses on "who and how" questions by tracking the direct feedback from the visitors on the site.

Web Mining

Web mining is the process of data extraction that focuses on the identification and extraction of specific information from the Web. The specific techniques of Web mining are Web structure mining, Web content mining, and Web usage mining. Web structure mining refers to the process where valuable data is extracted from the embedded links within the Web changes. It majorly helps in identifying various authoritative pages as well as hubs that are the basis of algorithms in contemporary pages, which are very crucial to the main search engines like google (Bharanipriya & Prasad, 2011).

The web structure mining has a structured linkage data view, and the main data is in link structure. They are represented in the form of graphs and use proprietary algorithms as the method of discovering the underlined Web link structure. They are also global and mainly focused on generating a structural summary regarding the website. Web content mining is the process where valuable data is extracted from the web documents in a readable format. Web crawlers are majorly used to read the website content automatically. It is used in the enhancement of the search engine results. It has a structured, semi-structured, and unstructured data view. The main data type is the text document or hypertext document, where the main data is also in the ontology form. It is represented in the form of edge labeled graphs by the use of Machine Learning Association Rules.

Web content mining uses a proprietary algorithm and statistical method as a way of data mining. Its main task is to report on the finding of any important information from the website. It has a local data scope, and its goal is mainly to discover more market knowledge. Web usage mining focus on evaluating website usage, for instance, the behavior of user navigation. Its main source of information is log file. This log file gives the server-side data and the rate of clicks. Its data view is of interactive type, while browser and server logs are the main data. Web usage mining is represented as inform of relational tables and graphs. Its advanced analysis concentrates on very sophisticated statistical machine methods, whose aim is to understand the user navigation history. Web usage mining scope is global

Web Mining Impact on Decision Support Systems

The techniques used in web mining are of great significance in the process of extracting a huge amount of data in the data analysis as well as in proper usage of the mining methods. All the three forms of web mining discussed above use the DSS. The automated data mining technique known as knowledge discovery facilitates the analysis. This technique tracks and analyses a lot of data to discover the major patterns and rules together with any information regarding the data that is not processed. Data mining has a crucial role within an organization as it helps in determining the customer browsing behaviors as well as anticipating the stock movements, which enables to create the sale policies and brings about a competitive advantage. The techniques also help in improving the DSS by the usage of statistical algorithms, logic, and data viewing. As a result, visualization, classification, and association rules are achieved through these methods.

Social Analytics

Social analytics' work is to monitor, analyze, measure as well as interpreting all the digital connections and people's relationships, topics, ideas, and also the content. It involves textual content mining in social media together with analysis of the social networks. It aims to gain knowledge of customer behavior as well as the likes and dislikes of the products and services of a firm. There are two groups of social analysis: social network analytics and social media analytics (Aggarwal, 2011).

Social network refers to a social structure that includes people or organizations that are connected with some specific relationships. It brings about a rounded way to analyze the edifice and changing aspects of social units. The social network analytics helps to study the structures by identifying local as well as global patterns. It also helps in locating the influential units in evaluating the dynamics of these networks. The typical social network analytics include communication networks, community, criminal, and innovation networks.

The metrics of measuring social networks analytics are in three categories that involve connecting, distributing, and segmenting social networks. The connection metrics are homophily, multiplexity, mutuality, network closure, and proximity. The distribution metrics are the bridge, centrality, density, distance, structural holes, and tie strengths. The segmentation metrics also include groups and social rounds, huddling coefficient, and interconnection. Social media are those technologies that allow social interactions among people. Through it, there is creation, sharing as well as an exchange of information and opinions among the people. The social media analytics is all about development, evaluation of the informative tools which aim to collect, monitor, analyze, summarize, and visualize the social media information. Social media analytics is also the systematic technique of consuming massive information from Web-based social media outlets to improve the competitiveness of an organization.

In many organizations, it is a form of marketing and communication strategy. There is an exponential development of the social media channels that range from Facebook, Twitter, You Tubes, and blogs together with analytic tools that uses these data sources in offering a chance to a firm in reaching millions of customers all over the world. There are several analysis tools used by the company in deciding the goals of using social media. These tools are in three categories: the first is descriptive analytics that utilizes basic statistics in identifying the characteristics of the activities together with trends like the number of followers possessed, number of reviews, or the type of channel mostly used. The second tool is social network analysis, which refers to the connecti...