Introduction to Data Mining
In the past decade, advancements have been made on processing-power and haste thus enabling people to advance past labour-intensive, monotonous as well as onerous exercises to faster, laidback and programmed exploration of data. More multifaceted data cliques have turned out to have more potential in uncovering relevant insights (Delen, 2014). Insures, retailers, telecommunication providers, manufactures and banks are now able to ascertain relations among variables such as value optimization and upgrades. These institutions are also able to determine in what way does the risk, budget, rivalry, and societal broadcasting platforms benefit the enterprise operations, incomes, business models and client relations through data mining.
According to Gupta (2014), data mining also known as knowledge extraction, knowledge discovery in databases (KDD), information harvesting or data/pattern analysis is the practice of tunneling into statistics to ascertain some concealed acquaintances as well as forecast upcoming possible drifts. It is an interdisciplinary subfield of statistics and processor knowledge with the utmost goal of extracting information through the use of intelligence methods. Data mining transforms data sets’ information into comprehensive structures for further use. The technique also forms the analysis step in the KDD process.
Other than forming the authentic examination stage, data mining also involves databank and its management. These are facets that comprise all disciplines related to managing data as a valuable resource ranging from data governance, data modeling and design and data security among others. Besides, data mining entails the pre-dispensation of data. This is the stage that concerns laundering, stabilization, conversion, attribute abstraction and assortment of statistics. Further, data mining engrosses models, that is, statistical simulations concerning the generation of sample data. The models, in this case, represents the data generating process considerably in idealized form. Moreover, data mining encompasses making extrapolations, conception, deliberations of intricacy, online updating as well as post-dispensation of revealed structures.
Data mining is a concept that is comprised of distinct sets of disciplines. The foundations of data mining comprise three intertwined science fields namely the statistics, machine learning/artificial intelligence and database systems. They are as discussed below.
This is a discipline that involves the collection, organization, analyzation, interpretation, and presentation of data. As Rangra and Bansal (2014) stipulate, statistics are used to study relationships existing between different sets of data or synthetic data that is derived from an idealized model. Statistics as a constituent of data mining deals with every aspect of data. For instance, it involves initiating a plan of how data is to be collected either through designing experiments or surveys.
This is the systematic analysis of numerical replicas and procedures used by processor schemes to execute certain undertakings (data mining) without using explicit instructions. Machines have artificial intelligence that resembles that of human beings although at this point it is being displayed by the machines/software. Trigo et al, (2014) highlight that machine learning purely relies on inference and patterns to make predictions instead of the instructions being provided by a handler. It is thus viewed as a subcategory of simulated acumen. The knowledge procedures for machines are developed on a scientific replica that is founded on the trial facts to make decisions and forecasts without explicitly performing the task. The algorithms for machine learning are used across different fields such as processor visualization and electronic mail sorting. They are also used in cases that are infeasible or difficult to establish a convectional algorithm to effectively perform a task. Machine learning has a close relationship with computational data which emphasizes creating forecasts using processors. Data mining as a ground of examination within machine learning centers on the explanatory assessment of data amidst unsubstantiated knowledge. Machine learning is also known as predictive analytics especially when being applied across business concerns.
These are systems developed to organize how data is to be collected and later stored (electronically) in a computer system for future access. Formal modelling and designing techniques are used where databases are more complex. The database systems work with the database management system (DBMS), a program that provides an interaction between final operators, appliances and the databank itself to encapsulate and scrutinize data (Gupta, 2014). Besides, this program incorporates the fundamental conveniences that are offered to manage the catalogue. The totality of the DBMS, the database and the allied appliances form the database system. Database systems are classified in correspondence with the databank simulations that they sustain.
Traditional Business Reporting
Traditional business reporting refers to the old techniques of collecting and aggregating financial and other types of information in an establishment. As Nagano and Costa Moraes (2013) points out, organizations have been using this strategy in the past decades before the initiation of data mining as a way of obtaining information necessary for making business decisions. In traditional business reporting, enterprises are required to provide a report on operational and fiscal statistics of that particular establishment to the public. It is also a regular act of providing data to the verdict-makers within a business to sustain their efforts. Traditional business reporting thus serves as an ultimate measure of the greater undertaking regarding enhanced knowledge management and corporate acumen. Some organizations are still using traditional business reporting despite it being time-consuming. Traditional business reporting encompasses capturing, storing and processing large quantities of business data to predict a trend or patterns for various business variables for instance sales and revenue patterns. The implementation of traditional business reporting embodies the extraction, transformation, and loading (ETL) techniques being coordinated by a record repository thereafter exposure apparatuses are used for efficiency and effectiveness. The tracking tools although simple to use, share and track data back over time, they offer limited real-time options for tracking as well as fail to provide an exhaustive analysis.
Business reports can be distributed in various forms ranging from print outs, via emails or can also be accessed through the intranet of a corporation. As a result of improvements in information technology, unified reports have been intensely produced joining different views on an organization in one place (Ramin and Reiman, 2013). Traditional business reporting thus engrosses grilling statistics snouts that have discrete analytical simulations to produce a statement that can be read by a human being.
Problems with Traditional Business Reporting
The goal of business intelligence is providing real-time information to management and further function as a support mechanism for making decisions. Traditional business reporting has fallen short of this goal due to some reasons. The first reason is that the technique was designed for very large scale deployments involving dozens of systems. Currently, most databases reside on individual systems being used by small and medium-sized businesses or departments of larger organizations. Another reason is that while ETL and OLAP (Online Analytical Processing) cubes are still essential for the enterprise-wide analysis of a large number of systems although most users are nowadays in need of simple reports from an individual system. The explosion in the number of databases globally thus makes it almost impossible to integrate them all. Besides, the ETL technologies are not real-time and are very difficult to keep up to date without database experts, who at times have great priority goals (Nagano and Costa Moraes, 2013). This makes many data warehouses to take so long to launch and most of the time they end up being obsolete on completion. More importantly, the update of the ETL and OLAP cubes are much involving thus reports rarely keep up with the businesses’ pace.
Benefits of Data Mining Over Traditional Business Reporting
Data mining helps organizations to derive knowledge-based information, unlike business reporting, which provides general information, thus requiring further processing. Data mining is also an efficient and cost-effective solution compared to any other application for statistical data. It further facilitates an automated prediction of behaviours and trends as well as an automated discovery of hidden patterns, unlike in traditional reporting where patterns are identified manually. Data mining is also a speedy process thus users can analyze large volumes of data in less time, unlike traditional reporting which time-consuming. Unlike traditional reporting which is somehow rigid, data mining can be instigated into novel practices as well as into prevailing programs.
Data mining is an aspect that has transformed what that was old (data) into being new again and useful. The data mining knowledge keeps developing to adhere with the swiftness of the boundless aptitude of extensive statistics as well as the affordable power of computing. The idea of data mining has become essential as a result of intensification on the volumes of unstructured data being produced in the digital universe. This paper is a comparison between data mining and traditional business reporting. In comparison, data mining has outweighed traditional business reporting on the benefits as well as in areas of applications. The paper also provides an in-depth discussion on the foundations of data mining, traditional business reporting and the problems associated with it. Finally, the paper elucidates on the eminent benefits of data mining.
Delen, D. (2014). Real-world data mining: applied business analytics and decision making. FT Press.
Gupta, G. K. (2014). Introduction to data mining with case studies. PHI Learning Pvt. Ltd.
Nagano, M. S., & da Costa Moraes, M. B. (2013). Accounting information systems: An intelligent agent approach. African Journal of Business Management, 7(4), 273.
Ramin, K. P., & Reiman, C. (2013). IFRS and XBRL: how to improve business reporting through technology and object tracking (Vol. 1). Hoboken, NJ: Wiley.
Rangra, K., & Bansal, K. L. (2014). Comparative study of data mining tools. International journal of advanced research in computer science and software engineering, 4(6).
Trigo, A., Belfo, F., & Estébanez, R. P. (2014). Accounting information systems: The challenge of real-time reporting. Procedia Technology, 16, 118-127.