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The ability of Business Intelligence (BI) technologies to provide historical, current, and predictive views of business
operations based on the collection, extraction, and analysis of business data to improve decision has been the basis
of several studies. More recently, “Big Data” and “Big Data Analytics” have further stirred the interest of researchers and practitioners alike. You have been requested to prepare a report focusing on one of the following
topics:


1. Big Data for Supply Chain and Operations Management
2. Sports Analytics
3. Agricultural Analytics
4. Fraud Detection in Banking Sector
5. Big Data for Sentiment Analysis

Your target audience is executive business people, who have extensive business experience but limited ICT knowledge. They would like to be informed as to how new Big Data technologies may be beneficial to their business. Please note that standard report structure, including an executive summary, must be adhered to.

The main body of the report should include the following topics.
Executive Summary: You are required to provide brief but comprehensive synopsis of your proposal, which highlights its key points.
Introduction: You are required to introduce the topic of the report in an extremely engaging manner which arouses
the reader's interest. You are required to give a detailed general background that indicates the overall “plan” of the
report.

Discussion of topics: You are required to discuss the following topics in depth. Your discussion should display deep analysis of issues with no irrelevant info.
Task 1. Data Collection and Storage
• Data collection system (what kind of data should be collected and how)
• Storage system (what are the requirements to the storage and how to achieve them)
Task 2. Data in Action
• Consumer-centric product design (what is it and how to do it)
• Recommendation system (what is it and how to do it)
Task 3. Business continuity
• How online business can survive in case of power outage or other disasters?

Discussion of topics

Big data is the enormous volume of data that are complicated and cannot be analyzed or efficiently handled and processed by traditional tools. Depending on how data are collected and the tools that were used to collect data, they keep on streaming in and piling up from time to time for storage. Since reaching the customers in their various places physically is difficult, companies and business organizations use social media and other platforms to collect data in order to meet the demands of their customers.

Due to this therefore, big data sentiment analysis prove to play important role in organizing and exhaustively deriving the meaning of the collected data for decision making in business (Cambria et al, 2013). Unlike small and medium data, big data cover a wider and detailed range of customer requirements that can be important in solving most of the problems facing the customers outside there about the products produced by a particular business. Interest of adopting the use of big data by various business organizations have grown in the past few years since its emergence (Chen et al, 2012). As a result of this, many technologies such as the Hadoop, Real-Time solutions, Cloud Solutions and many more have come to light and are gaining popularity with the continued rise of big data adoption by business organizations (Kunzea, 2015). Change in data collection tool has also resulted to change in the type of big data collected. Tools like social media on the internet collect large volume of unstructured data that are then organized by No-SQL databases for storage (Wu et al, 2014). Woolworths is one of the companies in Australia that put big data into practice where they spend heavily on it to enjoy its fruits. The supermarket is currently facing a slow market growth as a result of competition in the market. Venturing into big data was now aimed at capturing all the information from customers through the social media that could help them handle the problems that are currently there or might be detected by the data in the future. 

Data collection system

With reliance on Quantium as one of the Australia’s oldest and biggest data company, Woolworths assigned them to collect big data and help in carrying out big data analytics. The Quantium Company collects data from the field through the internet portals such as google and social media about Woolworths’ products from their customers. Data that are mostly collected through such data collection tools are unstructured where the users of the social media just come up with any issue concerning the business that was not structured or even taken care of in the earlier preparation of the data (Tufekci, 2014). The said portals where the customers drop their comments about the products that are sold by the business (Woolworths) will be streamed to the business’ database for storage and can be retrieved at any time in future for analysis and interpretation. Once the data have been collected, the specialists handling big data will therefore continue further to make use of the data through carrying out big data and sentiment analysis.

Data collection system

Storage system

Where and how big data are stored stands to be a big question to all those who wish to adopt its use. Retrieval of structured data through traditional methods include rational databases, data warehouses etc. collected data are taken from operational data stores where they are uploaded using Extract Transform Load (ELT) which are responsible for extraction of data from outside sources and change the data to fit the needs in operation thereafter loading data into the databases (Katal et al, 2013). Before the data is made available for data mining, the data is cleaned, transformed and made ready for analytical functions (Sagiroglu, 2013). The traditional Enterprise Data Warehouse (EDW) do not allow the incorporation of new sources of data until when the data is cleansed and integrated. Irrespective of quality of data, all data sources are attracted by big data environments since they are made magnetic. Storage of big data should be in a flexible way in such a way that data can rapidly be adapted and easily produced by the analysts (Herodotou et al, 2011). Due to this, agile database is needed where its physical and logical contents can be easily adapted with fast data evolution. The study and analysis of enormous dataset by the analysts require the incorporation and use of complicated statistical methods to excavate data up and down. Deep big data repository also need to be done in order to have sophisticated algorithm runtime engine served. For proper management of unstructured data that are collected from various sources including social media are stored in No-SQL databases (Moniruzzaman and Hossain, 2013). Such databases are always focused to carry out massive scaling, simplify application development and data model flexibility. Data management and storage are separated in No-SQL, this is contrary to relational databases that manage structured data. Tasks of managing data that are to be written in specific database languages are written in the application layer since such databases focus much on high performance scalable data storage (Moniruzzaman and Hossain, 2013). Elimination of disk input/output enables real time response from the database since data in server memory is managed by in-memory databases. Silicon based main memory are used for storage of big data instead of using mechanical disk drives hence boosting performance magnitude allowing development of new applications (Plattner and Zeier, 2011).

Consumer Centric product design

This is the process of setting goods and services towards the needs, wants and demerits on the side of the consumers both as a result of designing and improving the quality of product, service and even the contents (Tseng and Piller, 2011). Such process is seen to put designers into task since they have to critically think and analyze how the consumers (customers) will be able to interact with the products and also try to verify the assumptions of the process at each and every stage. In the process, when the designers are looking and testing into each and every stage, some few adjustments will be required on both the products and the management techniques (Tseng and Piller, 2011). All these are done with the aim of optimizing and meeting the goals of the products through building how the customers will be interacting with the products instead of coming up with the products that will need the customers to adjust their behaviors towards fitting to the requirement of the products. Huge returns can be expected from consumer centric design since it is much onto the customers’ satisfaction therefore goods and services are made available to meet all the customers’ requirement which will boost the sales thus maximizing the profit of the business since many customers will be attracted towards the goods. This design process matters a lot since the customers have control over what they want than ever before since they can be having the related and alternative products that come through widespread of internet that is to the reach of almost everyone in the world today (Pang et al, 2015). Customers can therefore perform google search to obtain goods they want incase what they have at their reach do not meet their demands and this is what the consumer centric design is trying to work hard to minimize at all cost since the businesses want to keep their customers.

Storage system

Above all, consumer centric design is not only concerned with the good services you provide to the customers through goods and services you offer them but also the experience of the customers in the post purchasing process of that same good and services (Tseng and Piller, 2011). Through the collected data and carrying out sentiment analysis, the analysts are supposed to use analytical techniques to predict customers’ needs. This will enable the business production team come up with goods and services that bombast the customers’ experience with what they did not expect to have in the market but will suddenly have close touch and experience with the product. In that case therefore, consumer centric product design can be achieved through the following practices. Firstly, the belief that customer comes first before anything else and thus ensuring that good brands that are concerned with fulfilling the customer needs. This perception makes the business to value the customer and see the world through their customers and attaching themselves to a belief that there is no success without the customers (Zomerdijk and Voss, 2010). Customer data are used to capture the demands of the customers as revealed from big data sentiment analysis by the big data analysts in order to achieve consumer centricity. Enough research and analysis of the big data obtained from the customers towards their experience to the products and their expectations of the products, brands of goods and services that are produced are towards meeting the wants needs and also ensuring that all other goods and services are produced in consideration to those wants and needs. Once the needs and wants of the customers are met by the produced products, this will tap the consumers to stick to the company’s products. Due to this therefore, in order to reduce the current problems and deteriorating market, Woolworths is supposed to build its products towards the customers’ needs and wants for it to stay competitive in the market.  

Furthermore, in order to maximize customers’ product and service experience, the business needs to produce brands that are concerned at building the relationships with the customers (Tseng and Piller, 2011). Once the customers have good relationship with the products of a particular business enterprise, it will be much easier for them to become users of the products and services thus improving the sales. This is only possible if the stored collected data are processed and analyzed for the best of the business. As well, the brands that are aimed at coming up with customer centricity, they need to analyze, come up with proper plans and only implement the customer formulated strategies in order for the price to stay loyal to the customer and for the profitability of the products in the business (Zomerdijk and Voss, 2010). Being that profit is always the aim of each and every business, implementing strategies that will scare away the customers will pull down the number of customers that interact and buy the products hence reducing the sales thus having same effect on acquired profits in business. The brands are therefore to ensure for loyalty of the customers.

Consumer-Centric Product Design

Recommendation system

This is the extensive class that is obtained in the Web application that encompasses the speculation of web users’ reactions to choices. One of the common examples of recommendation system is; by basing on the interest of online readers, you decide to offer them news articles. This focusing can only be with adequate research results considering their past history of online reading (Davidson et al, 2010).

Various technologies are involved in recommendation system that can be categorized into two major categories i.e. content based systems and collaborative filtering systems. Content based system work by evaluating the feature of the recommended items while collaborative filtering systems work by suggestion an item recommendation majorly basing on commonality measures between the items and users. In such a case, when a user is in demand of a certain product, he/ she will have the recommendation similar to that of other users with similar request or demand (Sharma and Gera, 2013). Recommendation system has various applications that can be applied in business. One of them is product recommendations. Recommendation systems are widely used at online retailers. Online venders are seen to struggle to exhibit their products to each returning user of the system trying to suggest to them the products that they might be interested in buying. The pick of suggestions to particular customers are not done at random, but in regards to some purchasing decisions that were brought up by analogous customers (Davidson et al, 2010). Another application recommendation of movies. A good example for this is Netflix which do offer their customers with suggestion of movies they might be interested in watching. The recommended movies are always from the customer ratings, where the once with the highest ratings are provided and recommended to the users to watch. Correct prediction of ratings is vital in increasing the number of users who would take to the recommendations. Basing on the past read articles, the news service providers try to dedicate some more articles that might interest the readers. Before the recommendation of a particular news to read, filtering is done to the news to ensure that there are similarity with the previously read ones or there is match of words. This same principle is applied for the blog readers and those who watch videos on the YouTube, they might do a search of a movie title by a certain actor or actress then other options by the same actor or actress are displayed so that one can trend with the movies one after the other.

Business continuity

In case of any disaster or power outage, the business should come up with various plans to curb the situation and ensure that business operation do not come to a standstill. Power outage is one of the serious problems that can limit the operation and continuation of the online business. As a result, the business is supposed to point out the systems that could have critical effect on the operation and continuation of the business in advance (Omar et al,2011). On the same, symptoms of power problems are supposed to be identified that could result to high risks of power outage. The checkup should include voltage and current fluctuations and complains about lost data by the users. Machines can be protected against brownout, blackouts and voltage dips using UPS (Uninterruptible power supply). Standby power supply is an offline UPS that is used to deliver power from the grid directly until when the power fails. Upon the failure of power, battery powered inverter turns on and continues to supply power hence ensuring the continuity operation of online business. Ferro-resonant transformer is used to condition grid power by line interactive UPS. This type of UPS maintains steady output voltage from varying input voltage and eliminates noise that could be produced by motors and other devices that might be producing noise in the circuit. Though its protection lasts for a short time, the protection is significant and adequate against voltage variations. Above all, there is true UPS that is used to provide power under all conditions and also ensures that its battery is charged thus ensuring the highest protection to computing equipment. All the discussed different types of UPS offer power protection to the equipment used in the operation of online business. Additionally, there are private generators that should act as the power backup in case of the power outage by coming on a short while after power outage. This option is always the best in case of longer power outages that may go for hour or even days. Emergency power supply from various sources are important in that they ensure for the operation of the business even at the time of power outage since online business greatly depend on power for it operation.

Conclusion and recommendations:

We can therefore conclude that big data is important and useful to the operation of business and by just collecting big data is not enough but should be followed with expertise analytics that will help draw meaning from bid data. Depending on the type of tool used to collect big data, different types of data are collected ranging from unstructured, semi-structured and structured. Social media and google are found to be major sources of big data thus making the collected data mainly being unstructured that are best handled by special types of databases such as No-SQL. This database ensures that data are stored and when it comes to data action, the data can easily be accessible at a higher rate since they are stored in silicon based memory. Consumer centric design is another important activity in business that will deal with improving the products towards fitting the desires of the consumers so that they can have easy time with the products in the market.

It can therefore be recommended from the above discussion that each business using or aspiring to adopt the use of big data to higher the services of big data analytics specialists. Also, being that most of the data collected are unstructured and from the internet, it is important for the big data analysts to apply sentiment analysis in order to draw meaning out of the data in their analysis. Since ordinary database tools cannot be used to efficiently handle big data, databases such as No-SQL are supposed to be used to easily handle big and unstructured data. Before the data analysis specialists start the analysis process, they are supposed to first understand the data and have vast knowledge with what the data represent. Recommended system is recommended for the online business handlers where they can be suggesting to their customers with similar desire a certain range of goods and services. For the continuous operation of online business, the business operators are supposed to ensure that they have power back up plans (i.e. generators, UPS, e.t.c.) to deal with power outage.

Reference:

  1. Chen, H., Chiang, R.H. and Storey, V.C., 2012. Business intelligence and analytics: From big data to big impact.MIS quarterly, 36(4).
  2. Davidson, J., Liebald, B., Liu, J., Nandy, P., Van Vleet, T., Gargi, U., Gupta, S., He, Y., Lambert, M., Livingston, B. and Sampath, D., 2010, September. The YouTube video recommendation system. InProceedings of the fourth ACM conference on Recommender systems (pp. 293-296). ACM.
  3. Katal, A., Wazid, M. and Goudar, R.H., 2013, August. Big data: issues, challenges, tools and good practices. InContemporary Computing (IC3), 2013 Sixth International Conference on (pp. 404-409). IEEE.
  4. Moniruzzaman, A.B.M. and Hossain, S.A., 2013. Nosql database: New era of databases for big data analytics-classification, characteristics and comparison.arXiv preprint arXiv:1307.0191.
  5. Omar, A., Alijani, D. and Mason, R., 2011. Information technology disaster recovery plan: Case study.Academy of Strategic Management Journal, 10(2), p.127.
  6. Plattner, H., Zeier, A.: 2011. In-Memory Data Management: An Inflection Point for Enterprise Applications. Springer, Heidelberg.
  7. Pang, Z., Chen, Q., Han, W. and Zheng, L., 2015. Value-centric design of the internet-of-things solution for food supply chain: Value creation, sensor portfolio and information fusion.Information Systems Frontiers, 17(2), pp.289-319.
  8. Sagiroglu, S. and Sinanc, D., 2013, May. Big data: A review. InCollaboration Technologies and Systems (CTS), 2013 International Conference on (pp. 42-47). IEEE.
  9. Sharma, L. and Gera, A., 2013. A survey of recommendation system: Research challenges.International Journal of Engineering Trends and Technology (IJETT), 4(5), pp.1989-1992.
  10. Tseng, M.M. and Piller, F. eds., 2011.The customer centric enterprise: advances in mass customization and personalization. Springer Science & Business Media.
  11. Wu, X., Zhu, X., Wu, G.Q. and Ding, W., 2014. Data mining with big data.IEEE transactions on knowledge and data engineering,26(1), pp.97-107.
  12. Zomerdijk, L.G. and Voss, C.A., 2010. Service design for experience-centric services.Journal of Service Research, 13(1), pp.67-82.
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