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Strengths

Real Estate, offers realtor services to customers through its online system can access various real estate properties available around in the world. Its operations facilitate prospective property buyers, sellers and investors in their activities. In the current system, the user (customers) can select from a wide range of services including buying, renting, investing and selling. Furthermore, for the first time customers and sellers, the organization will act as a link to service agent based on their location and budget requirements. Finally, Real Estate system is based on an online infrastructure that keeps users up to date with real estate matter regardless of their locations (RealEstate, 2017).  

STRENGTHS

· Extensive and efficient customer services based on personalized portals.

· Mobility and flexibility of services.

· The company’s system is real time.

· There is a wide range of services.

· Real time user support.

WEAKNESSES

· Minimal operational standards.

· An international system is used thus has conflicting legal frameworks.

· Minimal to zero authentication functions

· Limited staff training.

· Minimal employee engagement functions.

OPPORTUNITIES

· Operates in a digital market which is an extensive environment.

· Virtualization of services.

· Cloud computing initiative to increase service delivery.

· Market advancements and evolution towards the digital platform.

· Social media advertisement

· Extensive resource collaboration

THREATS

· Cyber intrusions.

· Stiff competition by other companies.

· Market fluctuations.

· Data security and privacy.

· Variations in the international legal framework.

Both the opportunities and threats are external variables that the company cannot change. It should focus on its strengths and weaknesses to increase its customer base. For instance, it should engage all digital platforms to sensitize the users of its services. Furthermore, it should incorporate cloud resources across all existing ICT infrastructures in order to lower the cost of operation. Finally, it must engage its employees to enhance their service delivery outcomes (Pristina, 2011).

Short Term (Now)

  • First, increase its system's availability by employing social media in its marketing campaigns.
  • Furthermore, engage the customers on a one on one basis through the personalized portals.
  • Deliver personalized services based on the user’s preferences.
  • Employ promotion tactics such as bonuses and purchase incentives e.g. offer coupons for first-time subscribers that provide free real estate advice(SEE, 2014).

Mid Term (next 12 months)

  • The first step is to create a strategic plan to maximize on the company’s strengths.
  • Secondly, diversify the services by engaging in new business venture related to the existing services.
  • Employ ICT resources that maximize efficiency at minimal cost e.g. cloud resources and ERP (enterprise resource planning) for in-house activities.
  • Regularly update the user’s system to improve the user’s interaction with the online facilities.
  • Finally, engage with other service providers with similar objectives i.e. limit the vendor lock-in.

Long Term (next 3 to 5 years)

  • Establish a strategy team and through it develop a dynamic governance plan for the ICT services.
  • Implement long-term awareness programs for the employees to engage their understanding and to motivate their career choices.
  • Outline regular financial assessments (audits) in an attempt to reduce the overall turnover rates of the staff.
  • Finally, invest in cloud resources (infrastructure) to host resources for other prospective real estate companies.

A lot of information is generated today owing to the existence and expansion of the digital media and information technology in general. In addition to this, the seamless integration of communication networks has also increased the availability of information which has improved the users’ access and collaboration. Now, although these outcomes have enhanced the functionalities of technology, they have also led to massive data structures that have made the analysis processes a challenge. In all, the databases that exist today are overwhelmed with data an outcome that makes it difficult to distinguish between quality and non-quality information (Silwattananusarn & Tuamsuk, 2012).

While there is no single accepted definition of data mining, its operational concepts can provide an important account of its functionalities. In all, data mining is an essential element of knowledge discovery (KD) which is a general data concept that processes and produces key patterns of information based on the needs of the users. KD is an affiliated process of database systems which facilitates the identification of data according to the parameters of the users. Therefore, while they may seem similar knowledge discovery and data mining are two different database concepts. In fact, KD can be highlighted as the overall process of identifying data useful to a particular application. On the other hand, data mining is a subsidiary item of KD that provides useful patterns from large volumes of data while focusing on specific database algorithms (Han & Kamber, 2000).

Weaknesses

Regardless of the application, data mining usually holds the same objective of developing an effective and predictive model from large sources of data. These models improve the explanation and generalization of data by identifying crucial defining elements (CRISP-DM, 2017). Therefore, in its operations, data mining will take a new set of data from a knowledge base e.g. a database or a data warehouse and define crucial patterns that will represent it as a whole structure. In all, the following elements describe its functionalities:

  • Data definition
  • Data exploration
  • Data preparation
  • System modelling
  • Evaluation
  • Deployment

In the first step, the users must determine their objectives even before they handle the data in itself. Therefore, in this stage, the business, users or organizations will analyse the problem they face with regards to the available data. For instance, in an enterprise, the management may seek to increase their customer base by understanding the users’ requirements and preferences. By defining this objective, the data mining experts will have the isolation elements for the KD process (IBM, 2017).

Metadata, a segment of information that defines and characterizes another set of information. Now, this stage explores the metadata where it’s understood to give the necessary background information on the available data. Furthermore, it is at this stage that the data is collected, tagged and analyzed. Moreover, the data is also explored to define its problems e.g. quality, availability or even biased behaviour (Jackson, 2002). 

A ‘cleansing’ process is executed where the data is transformed into a certain model as data mining algorithms will only accept it in certain formats. In addition to this cleansing, the process also derives new data attributes to aid the analysis process e.g. a data average is given.

This stage is the cornerstone of data mining process because it is at this stage that the various functions and algorithms are used to develop the final data model. Now, this stage regularly consults the data preparation stage to ensure the objectives are fully met. Furthermore, it is also usually coupled with the evaluation stage to optimize the results of the model (IBM, 2017).

An assessment step that cross-examines the final model with the initial objectives. Now, if the objectives are not satisfied the process is reverted to the modelling phase. Therefore, the following questions are asked:

  • Has the model achieved the business objectives?
  • Have all the issues been addressed?

The final stage, where the final results are exported into database structures for presentation, this can include spreadsheets, graphs and pictorials among other visual displays. Now, remember, this process can be continuously repeated to optimize results, an outcome that facilitates the use of iterative procedures to perfect the results.

Opportunities

So, now that the general process of data mining is given the users must also evaluate the methods to use in achieving this procedure. What does this mean? In essence, the data mining process may follow a number of methods or techniques to achieve its results and the variation in these methods highlight the functionalities of the process as they define the general operations. In this section, the paper analyses the three most significant data mining functionalities; classification, clustering and association analysis (Chen, Deng, & Wan, 2015).

Similar to the true meaning of the name, the classification function assigns data objects or items to different classes for the purpose of developing predictive categories of objects having unknown class indicators. Therefore, every data object in a given database is assigned to a given category so as to identify its target class. An example of this function is a banking setup where loan applicants are classified as either low or high credit risk individuals (Shodhganga, 2012). Several algorithms implement this function in relation to given class objects, they are:

  1.    Decision tree induction– all the elements of the data are classified using a tree-like structure that has various nodes of operation. These nodes will be given as rectangles and ovals for the internal elements and the leaf nodes respectively. In all, the tree sequentially flows into different levels expressing the data’s attributes.
  2.    KNN (K-nearest neighbour) – the conventional nearest neighbour algorithm (NNA) is used where the classification aims to find the next nearest point in a given set of data.
  3.    Support vector machine – this algorithm uses the statistical learning principles to analyze and identify patterns in a set of data. Moreover, a binary data classifier is used to map the elements of the data in a multilevel dimensional space(Vozinika & Viana, 2004).

In this function, the data mining process does not consider the data classes or objects available, instead, the data is split into different groups of items having the similar patterns. Therefore, the different groups will hold different patterns but with the internal elements having common operational patterns. Although the method is confusing at first instance, it does makes a lot of sense when analyzed with an example. Consider the example of search engines which categories data based on their features (patterns) (STEFANOWSKI, 2009). Data is presented to the user as either videos, reviews or audio among many other groups. Algorithms:

  1.    Partitioning– this algorithm clusters data using repetitive procedures that relocate data points of subset information. Furthermore, the algorithm will also locate areas with a heavy population of data sets in order to cluster them based on their defining attributes.
  2.    Hierarchical algorithm– a sequential flow of events is exhibited where data items are combined into different subgroups. These subgroups then merge to form an even larger group, a functionality that continuously grows into different levels(Ayr¨am¨o & K¨arkk¨ainen, 2016).

Also known as association rule mining, this function analyses data to identify the rules of operation which are then used to highlight the attributes of the functional data. Furthermore, to improve its functionalities, the algorithm will focus on the most recurring attributes to form the best patterns that will yield the qualitative results for decision-making (Kumar, 2014). Its algorithms are:

  1.    Pattern growth– data is sequentially analyzed to develop the attributes of the data items that facilitate their collaboration. In itself, this algorithm is very complex but will work efficiently with large volumes of data.
  2.    Parallel algorithm– this algorithm will follow a certain logical flow of events which will facilitate it in identifying certain patterns that occur repeatedly together. These patterns will then yield the final data elements that categories the set of data(Tudor, 2008). 

Information technology and its affiliated systems are not limited to any industries or functionality, this outcome increases their overall application. Similarly, data mining is widely used in many industries where information serves as the main element for decision making. In essence, the resources given by the technology are the defining factors of management as organizations seek to expand and optimise their operations (Silwattananusarn & Tuamsuk, 2012). Nevertheless, let’s highlight some of the key applications of the technology.

Threats

Online stores have shown dominance in the past few years owing to their availability and accessibility. These stores are further supplemented by the financial services that have shifted to the digital medium including functionalities such as mobile banking. Now, E-commerce depends on the flow of information to disseminate services and resources to consumers through the internet. This information will include marketing ventures, financial transactions and user preferences among many other items. Data mining will facilitate these online businesses in their operations by enabling them to identify specific patterns that aid their success. For instance, an online retail store will identify the best-selling items after analyzing the data available on their servers. Furthermore, they are also able to understand their customer preferences after scrutinizing the data found on their social media platforms (Matillion, 2017).

A key public sector that uses a lot of information because of the number of users. Now, information systems support the modern healthcare industry where patients and staff records are electronically managed. Furthermore, the same resources (data) are accessed by a variety of users from different locations in order to organize their health procedures. Therefore, the healthcare systems are regularly overloaded with information as many users update their records. Moreover, a lot of heterogeneous data is made available to different organizations having items such as payments, names, prescriptions and practitioner notes among many others. Now, data mining facilitates the classification, analysis and clustering of this information, functions that improve the quality of the quantitative records. These functionalities also improve the outcomes of healthcare practices including reducing the resource wastage (Silwattananusarn & Tuamsuk, 2012).

Finally, consider the application of data mining in all the other industries in general where regardless of the functionalities the aims are usually same i.e. to improve the outcomes of service delivery while maintaining minimal operational costs. Starting with the telecommunication industry where ICT functionalities and services enhance communication. These functionalities generate a lot of information in an attempt to meet the users’ demands (Zentut, 2017). Again, data mining facilitates these operations by sieving and analyzing data to yield conclusive results that aid decisions. Similar functions are seen in other service industries such as banking, retail and insurance. Moreover, the application of data mining also extends to government operations where citizen’s records are analyzed for governance purposes.

Conclusion

Knowledge management is an important functionality today owing to the extensive availability of information. This availability as seen before may hold many benefits but also creates many challenges of analyzing data. Data mining is a subsidiary element of knowledge discovery (KD) that manages this data and alleviates the problems of information management. In essence, the users do not analyze the extensive records but instead interpreted it using specific patterns of the overall data segments. Now, these elements have been identified by this paper where an overall analysis of the data mining technology and concept is given. In the paper, the general functions and algorithms of data mining have been highlighted together with their applications. Moreover, through this analysis, the technology has been seen to have minimal user limitations as any industry can apply its functionalities to support its services. In conclusion, data mining can be identified as an all-inclusive technology that integrates with any framework, an outcome that highlights its current and future impact on technology.

References

Ayr¨am¨o, S., & K¨arkk¨ainen, T. (2016). Introduction to partitioning-based clustering methods with a robust example. Reports of the Department of Mathematical Information Technology Series C. Software and Computational Engineering, Retrieved 29 September, 2017, from: https://users.jyu.fi/~samiayr/pdf/introtoclustering_report.pdf.

Chen, F., Deng, P., & Wan, J. (2015). Data Mining for the Internet of Things: Literature Review and Challenges. International Journal of Distributed Sensor Networks, Retrieved 29 September, 2017, from: https://journals.sagepub.com/doi/full/10.1155/2015/431047.

CRISP-DM. (2017). Data Mining Process. Retrieved 29 September, 2017, from: https://www.researchgate.net/file.PostFileLoader.html?id=590c8c7896b7e41e035f7e9c&assetKey=AS%3A490663138598919%401493994616568.

Han, J., & Kamber, M. (2000). Data Mining: Concepts and Techniques. Simon Fraser University, Retrieved 29 September, 2017, from: https://myweb.sabanciuniv.edu/rdehkharghani/files/2016/02/The-Morgan-Kaufmann-Series-in-Data-Management-Systems-Jiawei-Han-Micheline-Kamber-Jian-Pei-Data-Mining.-Concepts-and-Techniques-3rd-Edition-Morgan-Kaufmann-2011.p.

IBM. (2017). The data mining process. Data mining , Retrieved 29 September, 2017, from: https://www.ibm.com/support/knowledgecenter/en/SSEPGG_9.5.0/com.ibm.im.easy.doc/c_dm_process.html.

Jackson, J. (2002). DATA MINING: A CONCEPTUAL OVERVIEW. Communications of the Association for Information Systems, Retrieved 29 September, 2017, from: https://faculty.wiu.edu/C-Amaravadi/is524/res/dm_c_ov.pdf.

Kumar, T. (2014). Data Mining Association Analysis: Basic Concepts and Algorithms. Lecture Notes for Chapter 6, Retrieved 29 September, 2017, from: https://www-users.cs.umn.edu/~kumar/dmbook/dmslides/chap6_basic_association_analysis.pdf.

Matillion. (2017). 5 real life applications of Data Mining and Business Intelligence. Retrieved 29 September, 2017, from: https://www.matillion.com/insights/5-real-life-applications-of-data-mining-and-business-intelligence/.

Pristina. (2011). Analysis of ICT Industry in Kosovo. . Kosovo Economic Development through Quality and Networking., Retrieved 29 September, 2017, from: https://www.esicenter.bg/content/EN/library/ICT%20sector%20analysis_Kosovo_v1.pdf.

RealEstate. (2017). Official website. Retrieved 29 September, 2017, from: https://www.realestate.com.au/buy.

SEE. (2014). SWOT Analysis on ICT Theme e?Health. . . Regional ICT foresight exercise for SEE countries, Retrieved 29 September, 2017, from: https://forsee.eu/documents/Montenegro_OC_FORSEE_SWOT_Analysis_eHealth_v11.14_long_162.pdf.

Shodhganga. (2012). CHAPTER 3: DATA MINING: AN OVERVIEW. Retrieved 29 September, 2017, from: https://shodhganga.inflibnet.ac.in/bitstream/10603/11075/7/07_chapter3.pdf.

Silwattananusarn, T., & Tuamsuk, K. (2012). Data Mining and Its Applications for Knowledge Management : A Literature Review from 2007 to 2012. International Journal of Data Mining & Knowledge Management Process (IJDKP), Retrieved 29 September, 2017, from: https://arxiv.org › cs.

STEFANOWSKI, J. (2009). Data Mining - Clustering. SE Master Course, Retrieved 29 September, 2017, from: https://www.cs.put.poznan.pl/jstefanowski/sed/DM-7clusteringnew.pdf.

Tudor, I. (2008). Association Rule Mining as a Data Mining Technique. Retrieved 29 September, 2017, from: https://bulletin-mif.unde.ro/docs/20081/7%20ITudor.pdf.

Vozinika, F., & Viana, F. (2004). Data mining classification. Retrieved 29 September, 2017, from: https://courses.cs.washington.edu/courses/csep521/07wi/prj/leonardo_fabricio.pdf.

Zentut. (2017). Data Mining Applications. Data mining, Retrieved 29 September, 2017, from: https://www.zentut.com/data-mining/data-mining-applications/. 

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