There are two potential meaning related to the term Business Intelligence. The first one is Human Intelligence, which are applied minimum in business activities. This Intelligence in business field has human cognitive faculties and artificial intelligence technologies merged up for decision making and management supporting system in various business problems (George, Kumar and Kumar 2015). The Second one is related to the intelligence as the information valued for the relevance and currency. This consists of knowledge and information as well as technological efficiency in the management of the organization or any individual business.
Business intelligence thus consist a large category of technologies and applications for analyzing of data and hence providing access to the enterprise to help the users for better decision making and exploration of the business ideas. The business in today’s world has been increasing its standards, automation and technological aspects which are generally called the competitive intelligence that aims completely over the external competitive environment (Hofmann and Klinkenberg 2013). In ideal situation, the information has been gathered and based on the actions of competitors the decisions are made accordingly. There is lesser payable amount on the internal information gathering system.
Back-end and front-end business processes execution there uses an internet based computer and communication that develops the strategies for the better integration of supply chain (Imtiyaj 2015). There has been a concern with the activities within the organization. For the adoption of several new technologies, models and strategies of e-business approach for the supply chain integration and hence is developed accordingly.
The internet is used by the business for gaining global visibility across the extended network. There is a serious need of Big Data in the business intelligence for the increment of the technical aspects of the company as well as for the data storage and data warehousing process (Jain and Sharma 2017). This concept is totally based on the increasing collaboration within government organization, customer and private companies
We are choosing the topic Business Intelligence- Big Data for supply chain and operations management, for the further research of the study. This assignment is intended to discuss the various aspects by which provides the ability of Business Intelligence technology the historical, present and predictive views over the operations of business on the collection and extraction as well as the analysis of the business data that improves decision making and saves the business from the disasters that may occur potentially.
2. Data Collection and Storage
There is a requirement of three different layers in the concept of Big Data before the display of application: more data, processing system and analytics. Big Data as discussed by Gartner “Big Data is high-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation (emphasis added)”, has three transitions from the data collected and stored. Data warehousing has set a trend for storing the data in a mush saver form (Kasemsap 2015). The forces of market, combination of all the technologies and economic constraints, that has the ability to examine the supply chain. The company has to strive to achieve greater goals the company has to keep the competitiveness, coordination as well as the collaboration.
This collaboration has to be within the approach of the supply chain generally known as the integration of the supply chain. E-commerce and E-business is quite different form each other. E-business can be described as the adoption of the internet that accelerates the goals of the integration of supply chain. This includes globalization of businesses, increase in complexity of supply networks, the propagation of variety of products and minimizing of the life cycle of the product. The interactions of the website and the customer using it have always a supply chain intimating within it but not necessarily always in a positive way (Kedar et al. 2013).
There has been a rapid argument over the development of the supply chain for the betterment of lifestyle of people. Hence, the rapid improvement of the internet, cloud and internet of things like the wireless devices ad more has change the way of life for the people using it, providing a growth to the three V’s- Volume, Velocity and Variety of the supply chain in business of Big Data.
2.1. Data collection system
In present, the aspect of business is no longer unknown to data. The managers of supply chain tracks down the data, produces report against it and forecast them when needed. Hence the exploration of data to Big Data has been easily adapted by the companies. There can be a question arising, that what kind of data must be collected and how?
As the organizations continues to gather the large amount of data sets, sometimes the data set gets unexpectedly large and hence creates difficulty for the companies to manage (Larose 2014). It gets tough to manage, analyze and store such large amount of data. So for this data collection process, the volume of Big Data is quite valuable which is potentially called the Data Mining process. Data mining is basically the technique by which the companies convert the raw data into useful information.
There are techniques in which batches of data have been collected by analyzing the patterns and thus how the business gathers knowledge about the customers and creates effectiveness in their marketing strategies (Provost and Fawcett 2013). This also may help in the increase of the sale of the product or service provided by the organization, while decreasing the cost. Thus, the main motive of Data mining is to collect the data and analyze it from various perspectives and hence summarize the data into meaningful information which could be used for increasing the revenues and cut costs.
Primarily, data mining process is used to find patterns or correlations among various areas in large relational database. There are six main tasks where the Data mining are mainly used: Classification, Prediction, Estimation, Association rules, Description and Clustering. Classification is the process to generalize the data according to the instance. Several types of classifications of algorithms in the data mining like k-nearest neighbor classifier, Apriori, Decision tree, Naive Bayes and AdaBoost. This kind of pattern examines the features of the objects that have been newly represented and assigned in a predefined class. For dealing with the continued valued outcome the Estimation has been used.
This gives input data and estimated the valued output in the form of income, height or may be credit card balance. Prediction is the statement in which an expected outcome has been explained, based on knowledge and experience but not always applicable. The Association rule implies to the associated relationship within the set of objects in a database system. Cluster is said to be the most vital unsupervised learning problem.
2.2. Storage system
The data mining process extracts the raw data and processes them into useful information, but where are these data and information are stored? These data and information are stored in a warehouse. After centralizing the data into a meaningful database program, it is referred as the data warehousing (Rusu, Triantafyllidis and Kremers 2015). The data warehousing system helps in analyzing as well as utilizing of data. It is used to support the management of decision making process.
After 1990’s, the wave of the front-end tools allows the end users to conduct queries and hence report on available stored data in operational databases. There are difficulties in the approach, that the tools are only effective with end users those having knowledge about databases within medium- to high-degree.
The organizational Big Data are stored in the operational system in data warehousing. All of the data are not transferred to the warehouse. Frequently only a small amount of data has been transferred in the process of transformation, extraction and load (ETL). For making the access of the end user much easier the data is transferred in an organized manner within the warehouse as a relational database.
The activities during the data warehousing process are generally termed as the business intelligence (Ryoo 2017). The main purpose of data warehousing is to collect and process the data and hence analyzing the result for the solution of business problems. Data warehousing provides the storage of metadata that may include software programming of data, data summaries and rules for organizing data which can be easily indexed and searched, especially using the Web tools.
There are several characteristics of Data Warehousing: Organization, Consistency, time variant, Nonvolatile and Relational. In the organization data has been organized by detailed subject and thus contains the information only relevant to the decision support. There are several different codes by which the database may get encoded, hence consistency in each manner of the warehouse (Sekaran and Bougie 2016). There are data that are kept over time such that they could be used in future comparison and forecasting. There is a chance of new relational data to get replaced the old data, and sometimes the data does not get updated as well. The data warehousing uses the relational structure.
Data warehousing serves several benefits like the easier availability of data to the users because of organized form of data which are located on specific places, providing a consolidate view of corporate data rather than providing pieces of views which are differently formatted and the ability to reach the data in an easier method.
3. Data in Action
There has been a significant hype around Big data and analytics, critically said as the Data in action in business processes that helps in scaling, choosing appropriate designs, brings change in the management system, incentives and accountability of people (Shmueli and Lichtendahl 2017)
3.1. Consumer-centric product design
Customer centric is basically a way to do business with the customers in such a technique that helps in providing positive experience to the customers both before and after the sale for driving the business repeatedly, loyalty of the customers and profits. This customer centric company can help in providing and offering better services to the customers.
Customers centric offers greater experience from the awareness stage rather than just offering customers services, through purchasing processes and thus by the post purchase process (Shmueli, Patel and Bruce 2016). It offers a strategy based upon putting the need of the customer first rather than core of the business. CRM software put the core of the business to the customers by collecting wealth of the data giving full 360 views to the customers. This can later be enhanced for better customer’s experience.
Challenges faced by the business intelligence to become a customer centric organization are: There is a power shift between brands and customers that has happened in the economic downturn. There is a service across multiple devices and even in real time that presents a huge challenge for many brands (Swan 2013). Thus, the customers get much selective about spending money on brands. The brand that gets the higher vote, earn higher priority, enhances the relationship between the organization and customers and gains respect and reputation. And at the same time social media marketing, recession and social selling exploded over the scene and hence the mobile become on e of the main part of the journey of the customers.
The best practices that can be recommended to make the organization customer centric are:
The most important customer centric metric which should be monitored carefully are customers limitless value and churn rate (Witten et al. 2016). Customer centric plan is what the brand commits about and hence analyzes plans and implements. Customer services which are carefully formulated are focused on creating and keeping the loyal and profitable customers. Their belief is that there cannot be any success in business without having the trust of the customer’s and thus, tries to see the world from a customer’s perspective. Hence the customer centric organizations are both long and complex but can be beneficial for both the employees of the organization as well as the customers.
3.2. Recommendation system
Data Mining and the Data Base Management System are the techniques from where knowledge has been obtained by gathering raw data and turning them to useful information. To maintain this process a new systematic way has been introduced called the database. There is collection of data that has been organizes in a certain form like the tuples and the attributes that are kept in ordered manner. Giving a proper access to the data and even in easier way. In order to obtain knowledge from a collection of data, business intelligence methods are used (Wixom et al. 2014).
The most powerful and brand new technology is Data mining and it has greater potential to help the business environment to focus on the only essential information in the warehousing. It becomes easier to make decision for the improvement of business intelligence. Data Mining is the technique in which the company turns raw data and information into useful information.
By data mining in business, the organization can know more about their customer and develop more effective marketing strategy. It basically depends on the computer processing, data collection and data warehousing. This helps in keeping track of the customer’s buying record in the retail shops or in super markets, which helps in decision making process of the customer’s liking and disliking and the marketing technique of the shop. Data mining can be a cause for concern to prove certain hypothesis (Shmueli and Lichtendahl 2017).
To provide competitive advantages in business the main role of Data Mining in Business Optimization in Data Mining. Data Ming has primarily six techniques for analyzing the data: Classification, Regression, Clustering, Association Rule Learning, Anomaly Detection and Summarization. There are three main areas where data mining is applied successfully: Retail, Banking and Insurance. To drive the growth of analytics in Business there is a requirement of increase in data resources and thus in data mining as well. The application of data mining is realized by the business along with the competitive edge. For the development of knowledge based industry data mining and business intelligence works hand in hands.
4. Business continuity
A practice to expanding the area of business or social contacts through internet connection over various social media site like Twitter, LinkedIn, Google+, Facebook etc. is commonly called the Social Networking. This network establishes interconnection of online communities which helps in making contacts (Wu et al. 2014). The companies and organization gets much easier access to the number of customers through this social media site beside the traditional method of marketing, this helps in increasing the advertisement of the respective industry and hence promotes the brand.
Marketers and Businessmen use social networks for the increment of brand recognition and gain loyalty from consumers. In old and traditional management approach, the organization basically focuses on the performance of short term horizons, perspective from demanding outside, extrinsic sanctions and rewards, explicit control and coordination, pushing the managerial qualities and determining assets as the business organization’s core resource (Willis2013). But the modern management approach suggests the organization to focus on the perspective over the positive core, longer term performance horizon, intrinsic sanctions and rewards, implicit controls and coordination, pulling the managerial qualities and much more. There are several more benefits of social network in this revolutionary world of business.
The main advantage of social networking for businessman and marketers are that the company uses the social network to improve conversion rates and provides an access to variety of organization and old, recent and new customers. Sharing various types of blogs, posts, images etc. may lead customers to the charm of the organization and hence their compliments on company’s offering encourages new customers to buy the product and use the service accordingly. Promoting the company’s product or service helps in increasing the value of the brand authority. Increase in posts, makes the company rank higher in the search engine which helps in establishing the brand as trustworthy, legitimate and credible (Wixom et al. 2014). The company uses the social media network to demonstrate the level of its customer service and hence enrich the relationship with the consumers.
There are power outages and other natural disasters by which the business can get affected. But it is required for the business to keep continuity for the development and better settlement in the future. Business disasters can be technical, natural or human made. The technical and human made disasters includes infrastructural or power failure, hazardous material spills, chemical threat, nuclear power plant meltdown or blast and biological weapons, explosions, cyber-attacks, civil unrest or any kind of act of terrorism. Whereas, natural disasters include earthquakes, floods, hurricane, landslides, tornadoes, tsunamis and pest infestation.
There are techniques by which the business can acknowledge the Disasters. Disaster Recovery (DR) plan is a technique that can restore and access the data in the event of disasters that may destroy all or part of the business resources. A huge amount of business operations are involved in many aspects, that requires information to function (Jain and Sharma 2017). The DR plan ensures that even after disasters takes place, there is chance of data to get recovered and hence the mission-critical applications will be brought online within the shortest time possible.
There are severe requirements of DR plan in business orientation, in occurrence of any unforeseen event which affects the business organizational purpose. The disaster may potentially harm the business and may bring a halt in the organization, then is where a recovery is needed as fast as possible ensuring the clients to provide service within time. On of the major IT expense that any business may face is the Downtime. According to some studies on Disaster recovery strategy, downtime may last for an hour costing the small scaled companies almost $8,000, any midsize company about $74,000 and for large scale companies almost $700,000.
It is important to identify and anticipate the major business disruptions as soon as possible since there are always a possibility for the negative consequences may turn to unexpected disasters. These disasters may have long term affect over the companies for years leading to loss of productivity, reducing flow of cash through late invoicing, loss of orders, increase in labor costs as the staff may work extra hours for the recovery of downtime and many more. Disaster plan rescue the company’s reputation and also saves from multiple risks like data loss and budget expenses.
There are several new guides for the discovery, implementation and maintenance of total business recovery plan which are extremely vital for the well maintenance and survival of the business (Shmueli, Patel and Bruce 2016). These are very time consuming thus many organization does not have enough resources for this process. Since power outages can occur at any time and at any place, not only by natural disasters but also by accidental processes, there are some tips to save the business to get deployed:
5. Conclusion
The business in today’s world has been increasing its standards, automation and technological aspects which are generally called the competitive intelligence that aims completely over the external competitive environment Data warehousing has set a trend for storing the data in a mush saver form. For the supply chain integration several new technologies, models and strategies have been developed along with the adoption of e-business approach. Back-end and front-end business processes execution there uses an internet based computer and communication that develops the strategies for the better integration of supply chain.
Data mining is basically the technique by which the companies convert the raw data into useful information. There are techniques in which batches of data have been collected by analyzing the patterns and thus how the business gathers knowledge about the customers and creates effectiveness in their marketing strategies. Business intelligence thus consist a large category of technologies and applications for analyzing of data and hence providing access to the enterprise to help the users for better decision making and exploration of the business ideas.
The data warehousing system helps in analyzing as well as utilizing of data. It is used to support the management of decision making process. Customer centric is basically a way to do business with the customers in such a way that provides a positive experience to the customers both before and after the sale for driving the business repeatedly, loyalty of the customers and profits. Data Mining thus helps in providing competitive advantages in business which is the most vital role of Data Mining in Business Optimization.
There are six primary techniques of Data Mining to analyze data: Regression, Clustering, Association Rule Learning, Classification, Anomaly Detection and Summarization. The main advantage of social networking for businessman and marketers are that the company uses the social network to improve conversion rates and provides an access to variety of organization and old, recent and new customers. Power outages and other natural disasters by which the business can get affected. But it is required for the business to keep continuity for the development and better settlement in the future. Business disasters can be technical, natural or human made.
For the growth in business analytics increase in data resources are required and thus data mining is helpful. Business is steadily understanding and realizing the competitive edge of applications in data mining. For the development of knowledge based industry there is a need of Business Intelligence and Data Mining to work hand in hand. Therefore, it is necessary as well as helpful to use Big Data in Business Intelligence.
References
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Hofmann, M. and Klinkenberg, R. eds., 2013. RapidMiner: Data mining use cases and business analytics applications. CRC Press.
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Jain, D. and Sharma, M.Y., 2017. Adoption of next generation robotics: A case study on Amazon.
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