The Adaptive Business Intelligence

Published Date: 02 Nov 2017

Financial globalization and expansion of foreign investment throughout the world for the past three decades has become an unfordible trend which integrates closely with the world economy. This fundamental issue has brought vast advancement in the development of cross-border market, extensive demand for supply and most importantly critical expansion in technology which ultimately became a massive force in creation of many new industries. Eventhough there has been financial crisis and recession issues till date, the claim for integration of technology in business to generate revenue and profit has never been downsized or terminated due to its incorporation importance.

One of the primary challenges in many industries has been gathering, interpreting, analysing and presentation of data due to the various format and reporting methods of different establishment and organization. Predictions and assumptions will only take a property so far whereby they will not be able to precisely lay out their revenue due to lack of data interpretation capabilities to compile, uncover and gather data from hidden patterns of vast database. As a result of incorporation of adaptive business technology in any modern organization the result will for sure be much more effective due to its accuracy collectively.

For example, an institution with huge number of associates might face difficulties to interpret and compile personal data from a huge database. With the incorporation of ABI technology such as data mining, employers will be able to acquire information regarding their capabilities, skills, knowledge and productivity that will be an advantage to the properties current and future advancement. Such action will also reduce operational cost and labour cost in the long run for the organisation. This report will focus on the capabilities of data mining and the future trends of this technology.

ABI TECHNOLOGY

In early days, the business’s relied on the managers for the making important decision. Many other important decisions like

How many workers are required?

Increase or decrease of work force?

Investment into research of a new product or service?

Entry into new market?

Scope of the new product in the market?

These are some of the complex decision that the business manager has to make. With the growth in market, the complexity of business problems has also increased. Hence the decision by the managers cannot be always produce desired outcome. There were many factors to be considered when taking such business decisions in order to obtain the desired outcome. A manager has to consider two questions before taking decision which are:

What is be expected in the near future?

What is the best decision right now?

These questions are frequently considered both in professional and personal life. If we are going to work from home, we consider the traffic on the roads while going. Hence we make some decision to leave early or take some alternative route to reach to the desired destination by use of prediction method. For a business to expand into new markets, the business manager has to predict the future of the business in the new market and what will be the best decision for the business now. This prediction method is the main basis for the adaptive business intelligence. In simple terms adaptive business intelligence is the system which is based on prediction, optimization and adapting to answer the two fundamental questions: What is be expected in the near future? What is the best decision now?

Business systems were tools that can be only used to generate detailed reports, but could not assist the business manager to make decision. Adaptive business intelligence assists the business manager in the process of making smart decisions. The business managers have now realized the vast difference in the systems that can only generate detailed reports and systems that assist them to make smart decision.

The Business Intelligence systems are application technologies and programs used majorly to gather, store, analyze and provide access to data when required. Adaptive Business Intelligence is defined as the discipline which uses predicting and optimization techniques to create a self learning decision making system. Adaptive Business Intelligence systems consists of elements from data mining, predictive modelling, forecasting, optimization, and adaptability, which assist the business managers to make better decisions.

Adaptive business intelligence is new approach to business intelligence, which has the capacity to recommend the best course of action based on the data from the past. Such adaptive business intelligence systems the business managers can make decision with increased efficiency, competitiveness and productivity.

DATA MINING

As underpinned above on the vast expansion of Adaptive business technology integrated in both medium and large organization has been successful to gather information from large databases in order to clearly represents, the optimal functions of the business by discovering valuable patterns financial wise. Apart from the advantages that the technology brings to a certain business, this technology will also benefit scientist and information technology engineers in their research and analyzed scientific conceptualization. Researchers and IT experts has extracted statistics with algorithms neural networking, machine learning and created various cutting edge methods focusing on large data mining difficulties.

The idea of constructing revolutionary computer systems to adapt to their working environments and integrate from previous learning experience has caught the eyes of researchers from various field right from mathematicians, physicist, medical practitioners, computer engineers and cognitive scientist. With various techniques, research and computer system there has come, a potential to modernize and transform many scientific, industrial and especially business fields. Numerous research groups has come together and shared their similar problems contemplating supervised, unsupervised and reinforcement learning problems to further develop quality research and innovative solutions which can be diversified in different industries.

Although the principals of data mining has been developed and utilized since the early 1960's for to collect and analyze government database in hospitals, education institutions and military operations, the current understanding and expansion of the system is far more useful for huge corporations and business organizations with new disciplines such as data management, recognitions of various patterns, machine learning and artificial intelligence. The rapid growth of social media networking and articulate information sharing on the internet has not made interpretation of data anymore user friendly. This is because information can be shared in bulk, symbol and faster than ever which makes it challenging keep track and interpret without an appropriate computer system to collect, analyze, interpret and store data accordingly. In current business organizations, the advancement in digital data acquisition, interpretation and storage system / technology has proofed to the expansion of large databases which resulted of its occurrence in all area of human attempts right from credit card transaction records, mobile call statements, daily grocery transactions, pharmaceutical records, to government and private sector statistic reports.

The most important issue in a business environment is that the result of data acquired with computerized data mining system is that the proprietor of the software and individuals that will utilize the data should understand the statistics to make decision and take appropriate action accordingly. The models and patterns in data mining applications are the result of relationships and summaries which derives from clusters, graphs, recurring patterns in time series, linear equations and tree structures, this are basically referred to as observational data. It is vital to understand that data mining is not a system of collecting data to store or interpret because the data used in the system are information gathered previously for other purposes of a business organization which are then compiled for the purpose of data mining. This for example can a reports or records use to estimate revenue of certain department in a establishment or to maintain bill to bill payment to a particular supplier which then used in data mining process and application, referred to as "secondary" data analysis.

Data mining usually focused on large sets of data which develops new problems in the process which rises questions as common of how to access or review the data, how to analyze the data in a certain period of time, what does the data fundamentally represent and weather the result is simply conceptual or reflecting reality. Data mining can also be utilized for future predictions using census from a certain group of people with a common goal or general community to determine success result from potential and future consumers. This type of data mining will not provide exact classical statistics but merely opportunities that the organization might be expecting from either the success or failure of the census.

Data mining algorithms might not necessarily be focusing on individual users, previous understanding or knowledge which fundamentally measured due to novelty of the system. Data mining are usually set in wider explanatory discoveries in database, also largely known as Knowledge discovery in databases (KDD), a term used in the research field of artificial intelligence. The process generally involves certain steps:

Selection of target data

Data pre-processing and data transformation (optional)

Extracting pattern, models and relationship with data mining algorithms

Review, assess, interpret and document results and structures

KEY TECHNIQUES OF DATA MINING

Classification: Classification is the most commonly used method in data mining for solving real world problems. This method of data mining learns from the pattern of past data which are then classified into groups which have a similar characteristic, set of information, variables in order group the new instances into the respective classes.

The common two steps of classification method involves development of model /training and testing the model/deploying. The model development stage uses the collected input data with the actual class labels. After the model has been developed and trained the holdout data sample are used to assess the accuracy.

The collection of preprocessed data is spilt where the major part (around 2/3) of the data is used for training the model. While rest of the preprocessed data, is used for the testing model for assessing accuracy.

Classification Techniques / Algorithms: There are a number of techniques or algorithms used for classification modelling. The following are the commonly used algorithms used:

Statistical analysis: Statistical techniques were the primary algorithm for classification until the machine learning techniques emerged. This algorithm includes discriminant analysis and logistic regression, both of which assumptions that the nature of the relationship between input and output data is linear, distribution of data is normal, there is no correlation between the data and the data is not dependent on each other.

Bayesian Classifiers: This approach builds classification models based on past occurrences that have the capacity to place a new instance into a most probable group or classes.

Decision Tree: Decision tree include various input variables that may impact the different or classification pattern. These variables that are used as input are called attributes. A decision tree consists of branches and nodes. The outcome of the test which is used to classify a pattern based on one attribute is represented by the node. The final class choice for the pattern is represented by the leaf node.

Neural Networks: Neural networks are an advanced data mining algorithm for producing satisfactory results. The neural network has been inspired by the biology (brain) for processing the information. Neural networks have the capacity to learn from the data, without any rigid assumptions, has the ability to generalize. Neural network can be trained for a categorical prediction.

Clustering Analysis: This analysis is an important data mining method used for classifying items, concepts or events into common group. Each group is called a cluster. Cluster analysis is commonly used in medicine, social network analysis, astronomy, archaeology, biology and for development of Management Information System (MIS) Cluster analysis is an analysis tool to solve classification problems. Sorting cases into clusters or groups is the objective so that the degree of association is strong among the same cluster members and weak among the members of other clusters.

Association: Association technique is most familiar and straight forward data mining technique. There is correlation between two or more items of the same type of patterns to identify.

APPLICATIONS OF DATA MINING

The following are some few applications of data mining:

Banking: Data mining helps the banking sector with the following:

Automating the process of loan application by predicting the defaulters.

Detecting fraud online transactions and credit card.

Maximizing the customer value by selling services and products that are most likely to be bought by the customer.

Retail and logistic: Uses of data mining in retail and logistic industries:

Predicting accurate sales at specific locations in order to obtain the correct inventory levels.

Identifying the sales relationship between the products to improve the store layout and promote the sales.

Forcasting consumptions of different types of product to optimize and to maximize sales.

Discovering patterns in the movements of products by analyzing sensory and RFID data in a supply chain.

Computer Hardware and software: Data mining is can be used:

Predicting the disk drive failure before it occurs.

Filtering and identifying the unwanted web contents and email.

Detecting and preventing computer network security bridges.

Identifying unsecure software.

Government and defence: Defence applications of data mining

Forcasting the cost of moving military personnel and equipment.

Developing more successful strategies for military.

Predict consumption of resource for better budgeting and planning.

Medicine: Data mining is an invaluable method for research in the field of medicine:

Identify new patterns for improving the sustainability of cancer patients.

Predicting the success rate of organ transplant which helps in developing better matching donor organ policies.

Identifying the functions of genes in human chromosomes.

Discovering the relation between symptoms and illness to help the medical professionals can make correct decisions at the right time.

FUTURE TRENDS IN DATA MINING

Due to the immense success of various application of data mining, the field of data mining has been establishing as the major discipline of computer science and has a potential for future developments. Some of the typical future trends of data mining are as follows:

Standardization of data mining languages: There are various data mining tools available which have different syntaxes. In the future, data mining applications will have standard syntax and flexible user interactions.

Web mining: The development of World Wide Web and its usage grows, it will continue to generate ever more content, structure, and usage data and the value of Web mining will keep increasing. Research needs to be done in developing the right set of Web metrics, and their measurement procedures, extracting process models from usage data, understanding how different parts of the process model impact various Web metrics of interest, how the process models change in response to various changes that are made-changing stimuli to the user, developing Web mining techniques to improve various other aspects of Web services, techniques to recognize known frauds and intrusion detection.

Scientific Computing: In recent years data mining has attracted the research in various scientific computing applications, due to its efficient analysis of data, discovering meaningful new correlations, patterns and trends with the help of various tools and techniques. More research has to be done in mining of scientific data in particular approaches for mining astronomical, biological, chemical, and fluid dynamical data analysis. The ubiquitous use of embedded systems in sensing and actuation environments plays major impending developments in scientific computing will require a new class of techniques capable of dynamic data analysis in faulty, distributed framework. The research in data mining requires more attention in ecological and environmental information analysis to utilize our natural environment and resources. Significant data mining research has to be done in molecular biology problems.

Business Trends: Business data mining needs more enhancements in the design of data mining techniques to gain significant advantages in today’s competitive global market place (E-Business). The Data mining techniques hold great promises for developing new sets of tools that can be used to provide more privacy for a common man, increasing customer satisfaction, providing best, safe and useful products at reasonable and economical prices, in today’s E-Business environment.

CONCLUSION