CIS8008 Business Intelligence
The organisation always demands for the skilled workforces in order to take appropriate decision in the business and it also need that decision to be taken fairly without including the biasness and false practices.
So in that case the concept of business intelligences considers as a valuable model for delivering the appropriate data at the right time and assists all the levels of the business to take suitable decision (Chen, et al., 2014).
At the same time the business intelligences (BI) also offers the user-friendly data to the users in terms to work in an efficient manner and assist in perform the certain task.
In case of architecture, organisation starts to use the business intelligences architecture in order to perform the various activity as IT department get more pressure related to preparing of reports, provide the information for decision making.
Thus these factors push the company to adopt the BI architecture that enables in making better decision. The systematic architecture will assist in improving BI of the business and allow implementing best strategy that is also cost effective.
The business intelligences emphasis the advanced technology that supports the today business operations in a reliable manner and in that case it delivers the innovative, intelligences analytical apps and solutions to industries that have process large amount of data.
It also offers the facilities related to data mining, big data, and cloud computing and social media tools etc. thus the BI supports organisation to perform the activities as per the technology.
The applications that business intelligences includes are the spreadsheets, reporting and querying software in which data extracting and sorting will be perform, online analytical processing, digital dashboards and data mining etc.
Moreover, the main objective of the BI is to collect the data and processes the data in order to take appropriate decision for the business at all levels. This will result in generating high profit and revenue.
Main components of business intelligences system architecture that are as follows:-
- Data modelling: Data modelling helps in organizing the data from different sources and allows minimising the cost of storage replication.
- Data warehouse: Warehouse have extracted the data from various operational systems in order to transform the data into relevant and accurate data and then loaded for the analysis. Thus the data warehouse helps in achieving summarise of data, maintaining the data of enterprise.
- Enterprise information management (EIM): EMI includes data modelling tools, data quality system and data profiling and management of master data of the company. This helps the company to properly analyse and mining of data (Larose, 2014).
- BI Hardware: Hardware is the biggest requirement for maintaining the high performances and manages BI system. Data warehouse appliances also use to combine the server, database and data storage into one system. In case of BI, Netezza and DATAIIegro are the well known appliances in the market.
Thus, all these components are the essential part for the business to properly implement the Business intelligences architecture.
Diagram of BI architecture
Data warehouse consider as an electronic devices that can store large amount of information of the business and it is stored in the secured, simple and allow to retrieving in an easy manner. The data warehouse system helps in exploring and mining of data in order to develop the pattern of information so that it help the business to make improvements (Kasabov & Capecci, 2015).
The system of data warehouse also makes it easier for the different departments to easily access of the data of various functions. Besides that data warehouse system provides various benefits to the business such as it assist the business to taking better decision and allow to quickly accessing the data about the changing market, customer taste & preferences etc.
It also offers the data quality and consistency to the user.
Better decision making: for the business houses, it is quite difficult to take decision on the basis of limited data so in that case companies generally use the data warehouse system. It is because data warehouse allow storing relevant facts and statistics and on that basis, right decision can be taken.
Quick and easy access to data: Data warehouse system allows the departments to quickly access of information from different sources without wasting of time and resources and this support the IT department to reduce the work load.
Data quality and consistency:- Data warehouse system gather the different data from various sources and then it convert into the single and widely used format. In the case when the data is standardised then it gives the accurate and correct data to the user and allow them to make effective decision.
The data warehouse is the important system for the web as it covert the different information into the single & systematic form and allows easily retrieving by the user for performing any activity and taking necessary decision. Moreover, it also helps the company to study about the changing trends of the market so that company can develop the strategy in an appropriate way.
There are two approaches such as Kimball and Inmon for data warehouse development. The Kimball is a bottom-up approach, whereas Inmon is a top –down approach. The Kimball approach is effective for optimizing the local resources for attaining quick results.
On the other hand, Inmon approach is effective for a stable business because it requires significant amount of capital as well as time for designing the activities. There is no change in the designing of the process with the change in the existing business condition; rather new method is incorporated in the current practices.
In addition, Kimball is effective for marketing and CRM process in banks because there is specialization in the activities and the data is subdivided into many categories (Kimball & Ross, 2013). At the same time, Inmon approach is effective for insurance and manufacturing process because all the activities are inter related and helps in collected the overall picture.
In the ETL process, data is extracted from the operational systems and loaded into the data warehouse for systematic deliver to the clients in order to perform the activity and take some decision.
ETL process helps organizations to make meaningful and data driven decisions on the basis of interpretation the data. In the ETL, the data is extracted from the OLTP database and transformed it into the data warehouse scenario and loaded into the data warehouse for helping the employees to get the information at the right time for the performing of the task.
The ETL has prove to be important for the data warehouse system as ETL extracted the data from the different sources and then it organize the data in a specific and systematic format and these standardised data send into the data warehouse system (Yuan et al., 2013).
This process allows the users to get the accurate and relevant information for making decision and performing the particular task and job. Thus, this ETL plays an crucial role for the warehouse data system as without the ETL, then data could not be filter or sort then it creates the confusion among the management and it also does not effectively assist in taking decision.
Comparison between OLTP and OLAP
OLTP: It stands to on-line transaction processing and it is characterized by large number of on-line transactions. The main focus of the OLTP system is to make the quick and fast query processing and it is also maintain the data integrity in multi-access environments.
The OLTP database is widely used database as it provides the detail and current data for the transactions and decision making.
OLAP: It stands for on-line analytical processing that is characteristics by low volume of transactions as compare to OLTP. In this process, queries are very complex and it also includes aggressions (Cao et al., 2015).
This system is effectively, measure the response time and it is also widely used by data mining techniques. In this system, generally historical data are stored by the companies.
In addition to this, both the terminologies play a significant role in warehouse system. OLTP is crucial application that manages those transactions that has high volume of data. It is based on the client-server architecture and it also supports transaction across the network globally and this helps the warehouse to response quickly to the client regarding delivery of data to them at the right time.
In case of OLAP, it supports the complex calculation of low volumes data and by convert the complex data into the simpler form then it transfers the data to the warehouse system. This allows the data warehouse system to deliver the sort and systematic information to the users. Thus, both OLTP and OLAP support the warehouse system to deliver the simpler and relevant data to the clients.
There are three categories such as descriptive, predictive and prescriptive that is used for analyzing the big data.
Descriptive Analysis – This analysis uses data aggregation as well as data mining methods for developing in-depth information about the past scenario. For example, big companies use descriptive analysis for evaluating the reports and collecting information about the historical data in association with finance, operations, sales etc.
Predictive Analysis – This analysis follows statistical models as well as forecasting techniques for developing the information about the future practices (Gandomi & Haider, 2015). For example, it is used by the organization to forecast the customer behaviour as well as purchasing pattern for identifying the marketing trends. It helps in forecasting the demand for the product in the market.
Prescriptive Analysis – This analysis applies simulation algorithms to make effective utilization of available resources in order to maximize the final outcome. It helps in knowing the strategies what should be done for attaining quick goals (Cook & Nagy, 2014). For example, companies apply prescriptive analysis for optimizing the production and inventory in the supply chain management for ensuring the correct delivery of product at right time for increasing customer base.
Information visualization is the centre piece in BI as well as in analytics as it supports to offer big data with potential for great opportunity. Information visualization supports to represent the data in a statistical manner.
In this context, Renoust, et al. (2015) stated that the human brain processes information quickly through charts, graphs or other visual forms so visualization of large amounts of complex data becomes easy to pore over spreadsheets or reports.
Data visualization supports to make the system quick and easy way while conveying the concepts in a universal manner. Moreover, it enables to make different scenarios by making minor adjustments. Information visualization enables the firm to identify the areas where there is a need of attention as well as improvement.
In like manner, it supports to clarify the factors which influence customer behavior. Additionally, it helps to understand the products while supporting towards predicting the sales volumes.
There is a difference between information visualization and visual analytics. Heer, & Perer (2014) depicted that information visualization enables the brains to digest large amount of data while offering comprehensive and understandable way.
However, visual analytics supports the firm to understand the behavior of web visitors or the web clients, which supports the firm to optimize the user experience.
This supports the firm to increase the website conversion rates. Liu, et al. (2014) stated that information visualization deals in leveraging the human brain while creating better visualizations.
Moreover, it offers adequate interaction techniques which enable the users to answer the questions about the data. In the contrary, visual analytics supports to give more concern towards computation and analytical reasoning.
Business reports are the essential part of the company as it conveys the information about facts, figures to other person, shareholders, and investors in order to take appropriate decision. Business reports includes various types of reports that help the company to communicate necessary information to other departments or company.
Analytical reports: This type of report includes the information along with the analysis of facts as in this report, report writer provide interpretation of data with the figures in order to give the clear understanding about the facts and figures (Malthouse, et al., 2013). Generally, a business uses the analytical report for making decision or to work on the certain problem.
For Example, analytical reports include the progress report of the management in which explains that what has occurred and mark analysis on it. Likewise, sales report requested by the business owner in order to know the reasons of declining in sales of particular location.
Informational reports: Information report only includes the facts, data and information without including the detail explanation of the data. This type of report gives information about the particular subject matter without including any type of explanations.
For Example, this type of report is developing for the reasons of applications of new positions requested by the company owner to prepare the job description and specifications.
Recommendations and research reports: In this type of report, writer gives the detail information about the particular problem by recommending the action plan in order to perform the certain task. This report is based on the research performed by the person in order to solve the problem.
For example, this type of report gives the detail explanation about the reason of declining in sales with suggesting the actions that could be taken for overcoming the issues. Thus, these reports detail the topic by suggesting the various options for eliminating the challenge and issues.
The good dashboard is that, which makes the complex data simpler with the help of data visualization. The huge information is collected and is analyzed for making the process simpler and understandable by the users.
In addition, good dashboard represents the whole information in a concise manner. It helps in connecting the data with other activities in order to maximize the output. Thus, the visual layout of the dashboard plays an important role in interpreting and analyzing the information in a critical manner.
On the other hand, the good dashboard helps in expressing in the information of the data in a critical manner with the help of data visualization method (Iandoli, et al., 2014). The correct display of information helps in analyzing the data in a significant manner. Moreover, the good dashboard helps in grasping real view after extracting correct information.
Business performances management includes the performances of the organisation though helping the management to make improvement in their organisation performance in a way to achieve one or more predetermined goals.
Business performances management includes the main activities such as selection of goals and measurement of information that is relevant to an organization’s progress against the goal and lastly, management includes the employees and group with the aim to making improvements in future performances against these goals (Chang, 2014).
Thus, such practices used by the management for the business performances management. The main purpose of the management to adopt this method is that it is a systematic process of grouping the employees, individual & group and allows them to improve in their actions for achieving the mission and goals.
Business performances measurements: It is the tool that consists of set of quantifiable metrics to measure the business performances by tracking the current status of project or process. This tool compares the company performances with the pre-set goals or objectives.
It includes balance score card that assess the financial and non- financial performances, internal process and the customers. The main purpose of this technique is to help the manger to take day to day decisions and properly implement the strategies.
The major differences between the terminologies are that performances management guides in terms of how to manage the particular strategy and how optimally perform the certain strategy. In case of performances measurements, it gives the detail information about the current status of the business performances by tracking its position.
The measurement of business is performed in regular manner for taking day to day decisions while, business management strategy is implemented in irregular manner.
Balanced scorecard has the four perspectives which highly influence on the subsequent thinking about the subject. Individual organizations can change these perspectives according to the need for the purpose of making them suitable according to their own circumstances.
The four perspectives of balanced scorecard are financial perspective, customer perspective, internal process perspective, and learning and growth perspective (Tjader, et al., 2014).
This perspective enables to focus towards financial performance of the organization. This perspective covers revenue and profit margin of the commercial firms however in the context of NGO firms it cover budget and cost-saving targets.
Hearst, Laskowski, & Silva (2016) stated that the financial condition of the organization represents the health of the organization so this perspective supports to identify the financial performance.
Under this perspective, balance scorecard gives focus on the performance targets as it enables to relate with the customers and the market in which company is dealing. This perspective supports to cover the customer growth. It also focuses towards service targets, market share and branding objectives (Bhattacharya, et al., 2014).
For this purpose, typical measurements and KPIs use by the researcher which enables towards including the customer satisfaction, service levels, market share, net promoter scores, brand awareness, etc.
Internal Process Perspective
This perspective supports the firm to focus on internal operational goals which enables to cover the objectives while relating them to the key processes. It supports the firm to deliver the customer objectives to maximize the customer satisfaction. This perspective outlines the internal business processes goals to internally order to push the performance (Hoque, 2014).
Learning and Growth Perspective
Learning and growth perspective supports the firm to focus on the intangible drivers such as human capital, information capital and organization capital which supports the firm for the future context.
To achieve this perspective, typical measures and KPIs are used by the firm which includes staff engagement, performance management scores, skills assessment, corporate culture audits, etc. (Bhattacharya, et al., 2014).
Strategy map enables the firm to provide the linkage between the objectives of each of the four perspectives of a balanced scorecard. Tjader, et al. (2014) stated that without Strategy Map, balanced scorecard does not have a proper Norton & Kaplan Balanced Scorecard.
So, without a strategy map, scorecard will remain only an operational tool, instead of strategy communication and execution. Strategy Maps enables the firm to managing strategy while executing it successfully.
Big Data in common language is the large volume of data that includes both structured and unstructured form and it is used by the business for performing their day to day task.
At the same time, the relevancy of data is depending upon the usage of the company like how much data can be useful for the company (Wu, et al., 2014). Big data is also analysed for decision making based on the insights of the company operations.
Big data also define in the form of three defining properties or dimensions. These properties are volume, variety and velocity etc.
Volume: Organisational collect the data from different sources such as market research, social media and machine data. But storing such big data is tedious task for the business but with the development of the technology equipped system, company can store and easily retrieve the data at any time.
Velocity: Data streams consider as an unbeatable speed that easily deals with the big data and provide the relevant data to the clients/ users at the timely manner. It includes the RFID tags, sensors and smart metering is driving the needs to deal with torrents of data in near-real time.
Variety: big data comes in all types of format including the structured, numeric data form and unstructured txt form data etc. besides that in the form of emails, videos, audios big data has appear. Thus, with the data warehouse system, company can extract and sort the big data in relevant manner and able to quickly deliver to the clients.
The big data constituents by the collecting of data from different sources like from websites, social sites, market researches etc and this process constituent the big data. Big data is so large or complex that it is quite difficult to deal it with the traditional software and systems.
But with the introduction of new or updated system, company could easily manage the big data and easily extract the data from different sources. The data mining system, warehouse system are the key example of managing the big data in a systematic manner.
The three key big data technologies that can transform the business analytics and these are as follows:-
Mapreduces: It is consider as a programming model for processing the big data sets with the parallel distributed algorithm on a cluster. Par reduce is generally used by the banks and financial institutions for handling and managing big data. The Mapreduce functions include two stages par stages and reduce stages.
Map stage includes the process of the input data from the map. The input data is in the form of file and directory that is stored in the hadoop file system. This system process and creates the several chunks of data.
In the reduce stage, reducer job is to process the data that comes from the mapper and further it produce into the output that is also stored in the HDFS.
Hadoop: The apache Hadoop is the software that allows to distributing the process big data sets across the clusters of computers with the help of simple programming models (Schulz, et al., 2015). The important function of the Hadoop is to distribute the big data information to the clients so that they address it properly.
NoSQL: This is the database that provides to storage and retrieve the data that is modelled in means other than tabular relations used in databases. It functions in a way to stores data as documents in a binary form that is column and row form that have a similar structure. Thus, it allows the users to take the sequenced manner or formatted file.
Big data analysis consider as a key privacy system that is also ethically right for the organisation to perform their activities or taking decision based on the big data analysis.
The big data analysis is ethical correct because it allow the users to gather the data from the multiple sources and then process the data for distributing the clients.
This process helps the user to get the sorted, analyzed and systematic data and on that basis decision will prove to be beneficial for the company as compare to make decision on the basis of limited experts’ knowledge (Moro et al., 2015). The big data also helps in bringing the innovation in the business as it provides the transparent information to the company.
The data analysis uses the NoSQL, Hadoop and map reduces programs to analyse the data in a secured manner. Such practices give the clear view of their secured and ethical corrective. Ethical issues that are emerging with the big data analysis are that companies need to consider the core values while using the personal information of the customers.
It is unethical to sell the data of the customer to other companies just for earning the profit. So developing the trust of the customer on the companies is the difficult task. Besides that fulfilling every need of the society and shareholder is also a challenging task for the companies under the big data.
CRISP-DM methodology stands for cross- industry process for data mining. This methodology gives the structured approach for the planning of data mining projects. This is considering as a well proven in the market (Chen et al., 2014). This methodology mainly uses to solve the business problems/ issues. CRISP-DM includes the following steps in the process:-
- Firstly need to business understanding so that problems, goals & objectives, resources and capabilities of the business can be identified
- Secondly need to Data understanding so that it is determine that how much data is useful.
- Thirdly, there is need to prepare the documentation for the data in a systematic manner.
- Modelling: In this step, it is require assessing the models with respect to business success. The models should meet the criteria the business for properly establishing the modules.
- Evaluation provides to look the accuracy and relevancy of the data.
- Deployment plan includes necessary steps that indicate how to perform the activities.
Structured data can be classified into the numeric and categorical form.
Numerical data: these data indicates the measurements such as person heights, weights, IQ and blood pressure etc. it can be broken into the discrete and continuous form.
- Discrete data represents the items that can be easily counted and measured its possible values are listed as 100, 101, 102, and 103 (representing the countable infinite case).
- Continuous data represents the measurements of the possible values that could not be counted and it can be described with the help of intervals on the real number line.
This data indicates the characteristics of the individuals like person gender, their location. So this data does not involve the mathematical meaning but instead of it uses the qualitative data like Yes/No (Larose, 2014).
For example, such as “1” indicating male and “2” indicating female
The main comparison between the two data is that numerical data focuses on the calculation and then measure the data while categorical data more focuses on the qualitative part in which it evaluates the characteristics of the activities and on that basis analysis the data.
At the present time, most of the human beings are connected with each other through social media. On social media, employees of the organization share different forms of information which enables them to connect. However, Yuan, et al. (2013) evaluated that there are few key privacy issues which are associated with data mining in the context of social networking sites.
Wu, et al. (2014) stated that advanced and latest technologies also increasing the concern towards security and privacy issues. If the security issues not address properly within the organization then it may create hurdle towards the fulfillment of the organizational expected growth and opportunities and it also hampers long term success of the firm.
It is identified that data mining consists privacy issues associated with social networking sites. In this context, Bello-Orgaz, Jung, & Camacho (2016) depicted that majority of users does not alter their privacy settings time-to-time and allow the unknown users too to access their personal information.
Moreover, most of the users utilizes genuine names, and photographs in their ID which makes easier to identify and get tracked. Additionally, it is identified that users share too much information on social networking sites which remain accessible to other users and it can create serious implications on their privacy.
This is the main reason behind Facebook criticism as it shows negligence regarding the privacy issues in the default setting for users (Sicari, et al., 2015). So it is essential to increase the concern towards the privacy over social media by following simple security steps while including the steps towards deleting the cookies, browser history, checking the security parameters of the system, etc.
Social networking sites enable the organizations to analyze the performance of their businesses through the like, comment and share of the post through their customers. It enables the firm to get the feedback or response of the consumers regarding the product, services, new launch, discounts, rebates, etc.
In this context, Gandomi & Haider (2015) depicted that social networking sites enable the firm to collect the response from the large size of audiences. This information enables the firm towards increasing the positive word-of-mouth and also enables the firm to create brand awareness and brand recognition in the global market.
Social media networking offers the platforms to the firms to build an effective social relationship with their consumers (Malthouse, et al., 2013). It supports the firm to identify the opportunities for the firm by evaluating the interests, activities, backgrounds, or real-life connections with the customers.
Social network sites enable the firm to represent each user’s social connections with variety of additional services. In the current scenario, it is identified that almost every customer has at least single phone and due to digitalization the trend of spending time over internet is increasing.
Social networking sites enable the firm to make the target audience aware about the firm new product launching, product offering, etc. It supports to discover, read and share the news while focusing towards the information and contents (Nadeem, et al., 2015).
Social network applications support to offer new forms of empowerment and means of information sharing to the firm while creating a bonding between the organization and the customers.
Location based analytics enables the firm to enhance the customer experiences while getting the understanding regarding the real time and offering the relevant messages according to that (Chang, 2014). This system supports the firm to gather the information regarding the customer and customer location through utilizing intelligence system through the GPS services.
It is a powerful tool which monitors the information related to traffic as well as the location to integrate the venue and customer data. For instance, Reebok is the retail firm and it uses the location based analysis system which supports the firm to track its customer location and needs (Cao, et al., 2015).
It enables the firm to offer improved services to their customers and enables the firm to achieve the leading position in the industry. Location based analytics solution combines Wi-Fi access and analytics technology together for the purpose of social engagement.
It offers marketing tools to help businesses to gain valuable insights in the context of real-time data. It allows the firm to offer personalized experiences for all consumers while creating more insights.
Location based solution enables to gather the information related to customer and location intelligence from mobile devices and through Wi-Fi endpoints. Analytics and marketing portal allows the users to offer a powerful tool for effectively monitor the traffic and location information.
It enables the firm to increase the customer satisfaction to the customers while monetizing the data for improved business innovation (Heer, & Perer, 2014). This solution makes easy to increase the customer satisfaction.
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