Mid Term Questions Evaluation Research Paper

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new technologies have given birth to data analysis from the IT backrooms, and have increased the possibilities of utilizing the use of data-driven results into every aspect of an organization. However, much as improvements in hardware and software have made the advent of big data usage possible, the only consideration is not technology. It is important for organizations to take very holistic approaches to integrate big data into every aspect of their business procedures, their daily operations, and strategies. Big data brings both challenges and opportunities to every business. To be able to obtain value for big data, it is important to carry out timely analysis of the data, and the result must be such that can influence important business decisions and bring about positive changes. Having the appropriate combination of people, technology, and processes determine the overall effectiveness. Key processes, roles, and functions are optimized by analytics. It can be built upon to aggregate both external and internal data. It makes it possible for organizations to manage large volumes of data, meet all stakeholder-reporting demands, develop important market advantages, improve controls, manage risks, and, eventually improve the performance of the organization by turning relevant information into intelligence. Analytics are able to identify inventive opportunities in major processes, roles, and functions. It creates the needed catalysts for change and innovation -- and helps develop new frontiers for both the business and its customers by challenging the status quo. Sophisticated procedures allow organizations to find out the root causes, carry out the analysis of micro segments of the markets, alter processes, and predict accurately about upcoming events or the propensity of customers to engage, churn, or buy (EY, 2014).

Claims Analytics, Optimization, and Fraud Detection

Analytics and big data can play very important roles in reducing the ever-increasing rate of fraud. In a study carried out by FICO in 2013, over 35% of respondents predicted that about 5-10% of the total claim is represented by fraud. 31% claimed that the cost could be as high as 20%. As these percentages keep increasing, a large number of insurers predict a sharp increase in losses due to fraud. Organizations should device means to discover suspicious claims, improve the effectiveness of investigations and prosecutions. They should also facilitate fast visualization and reporting to enhance continuing antifraud efforts. Analytics and big data can be a part of every fraud background to gain fast supplementary insights.

Customer Retention

Carriers can exploit maintenance efforts by signifying the next viable offer by gaining more knowledge of customers, their needs, and their tendency to churn. Analytics and big data go a long way to help decide whether a customer should be shown a new product or if the customer poses a retention risk to the company. The identification can be carried out while a call-center agent interacts with the policyholder.

Up-sell and Cross-sell

Carriers can advice the right policyholder on the aright action to take at any given time in order to utilize up-sell, cross-sell, loyalty and strategic lifetime value profitability. It can improve on analytics to enhance customer service deliveries, give solutions to service issues, understand the policyholder's motivation better, and improve customer satisfaction (IBM, 2013).

Issues Concerning Big Data Collection

Volume of Data

The data volume measures the amount of data an organization has at its disposal. The organization does not need to own every data it makes use of so long as they are easily accessible. As the volume data increases, different data records lose values about type, age, richness, quantity and other factors.

Data Velocity

Data velocity is responsible for measuring the speed of the data creation, aggregation and streaming. The richness and speed of data utilized in different business dealings have been rapidly increased by e-commerce (for instance, web-site clicks). Managing data velocity goes far beyond a mere bandwidth issue; it is equally an issue of ingest (extract transform-load).

Transport and Storage Issues

Each time we invent a new storage medium, the quantity of data exploded beyond such capacity. The only difference with the recent explosions-which can be traced to the advent of the social media-is that no new storage medium was invented. Additionally, everyone and everything creates data. A good example is electronic devices. So scientists, journalists, writers and other professionals are not the only ones creating data. Most disk technologies today come with a storage limit of 4 terabytes per disk. This means that about 25, 000 disks will be required for every 1 Exabyte.

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It will be hard to attach the required number of disks on a single computer, even if the whole data can be successfully processed with a single computer.

Management Issues

With big data, management will always be the most difficult part to deal with. This difficulty was first experienced over a decade ago in the eScience initiative of the United Kingdom where there was a geographical distribution of data with different entities sharing the ownership and management. Taking care of important issues such as access, utilization, metadata, governance, updating, and reference (In publications) turned to be serious obstacles. Unlike collecting data using manual techniques, which involves very rigorous protocols to ensure validity and accuracy, digital system of data collection is more convenient (Kaisler, Armour, Espinosa, & Money, 2012).

Question 2

Concept of Software-as-a-service (SaaS).

It is common to hear people describe cloud computing as a stack -- a response to the wide range of services established on one another under the moniker Cloud. The National Institute of Standard and Technology (NIST) provides the generally accepted Cloud Computing definition. Software-as-a-service (SaaS) can be rightly defined as: Any software deployed over the internet. With SaaS, every provider gives application licenses to customers either as a service on popular demand, through subscriptions, where they pay by usage or at no charge at all so long as there is an opportunity to generate income from other streams other than the direct user. Recent reports indicate a rapid growth in SaaS popularity and predict a continuing double-digit growth. This growth shows that SaaS will likely become commonplace in every company and thus necessitates the full understanding of the real meaning of SaaS and its suitability (Kepes, 2011).

Strategic analysis of software-as-a-service (SaaS) to the organization to enable them make the right decision on this issue.

Where SaaS Makes Sense

Generally, cloud computing, and SaaS particularly, is a fast-growing method of technology delivery. That said companies considering to adopting Cloud should first consider which application they ought to move to SaaS. In that case, we consider certain solutions as prime choices for a first move to SaaS:

1. Vanilla contributions, where the solution is not differentiated. One good example of vanilla offering would be an email where several competitors make use of similar software. This is because this important technology is important for running the business, but confers no competitive advantage on its own.

2. Application that encourages a notable interplay between the outside world and the organization. For instance, newsletter campaign software for emails.

3. Applications with very significant needs for mobile or web access. A good example will be the software for managing mobile sales.

4. Software designed only for short-term need. A good example will be collaboration software for a precise project.

5. Software with significant demand spikes, for instance billing or tax software used only once every month.

Where SaaS May Not Be the Best Choice

In as much as SaaS remains a very valuable tool, there are some situations where we doubt if it is the right choice for software delivery. Some examples of situations where SaaS may not be suitable include:

1. Applications that require very fast real time data processing.

2. Applications where the regulation or legislation does not allow the external hosting of data.

3. Applications where a premise or existing solution takes care of all the needs of the organization (Kepes, 2011).

Question 3

a. Advantages of Making Use of Mobile Wallet

The important payment tool- 'mobile wallet' offers the following benefits:

1. Keeping the account of the consumer updated with an indication of the amount of reward points the consumer gained from the purchase.

2. Avoiding the consumer's or merchant's purchase difficulty by redeeming or adding loyalty points at (POS) instead of credit or cash.

3. Providing consumers with retail offers and purchase data, based on past purchase data.

4. Giving warning messages to consumers for overdraws and fund deficits.

5. Giving the consumer the option of choosing the bank credit/debit card that offers more interests, redeemable coupons and favorable rate over the others.

6. Activating automatic unlocking of prepaid debit card for all payments made at a specific store or a certain retail category.

7. The recommendation of obtainable accessories in the nearest retail shop or the same store based on the item purchased and the location.

8. Allowing banks to either raise or limit the credit limit with.....

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