Assessment 2: Technology Assessment Report You need to write a 3000 word Technology Assessment Report. The report relates to an analytics assessment that address and then justifies the potential...

TECHNOLOGY ASSESSMENT REPORT
Big data is the large amount of data that is difficult to store and organize. This includes both structured and unstructured data. This data can be used for analysis of businesses, growth of profits or potential threats to the company. Various e-commerce companies predict user behavior using big data. These data sets are generated exponentially.
The data grows rapidly because it continuously gathers numerous information from wide range of devices including internet of things. It is mainly defined using the three V’s:
Velocity: The speed at which the data is collected.
Volume: The amount of data collected.
Variety: Different types of data that is being collected.
Technologies that are developed to operate on this big data include Cloud computing and business Intelligence.
The efficiencies of various government processes in terms of cost, production and innovation can also be analyzed using big data. Big data analytics has brought a lot of improvement in healthcare by providing personalized medicine and prescriptive analytics, clinical risk intervention and predictive analytics, waste and care variability reduction, automated external and internal reporting of patient data, standardized medical terms and patient registries and fragmented point solutions.  Some sectors needs an improvement while some sectors have already achieved the appropriate facilities for all requirements.
Besides the technical reasons, both Cloud and Big Data have become the darlings of businesses since they reduce the cost of ownership by a great deal. They help companies convert their CAPEX to OPEX since in cloud, one pays for Pay per User. Hence there are no huge capital expenditures up-front. However the key lies in breaking down the hurdles of open source adoption and enterprises becoming agile.
Cloud computing and Big Data both of them bring in various data security and privacy concerns. In this system of integration with big data and cloud computing, enterprises try to build elastic and scalable private solutions to maintain the same value which the enterprises do in order to bring a Scalable Distributed Processing into action. We can clearly see the similarities between big data and cloud computing in the regard of security concern. It is a prime concern to innovate security solutions in order to drive these sectors within enterprise.

CLOUD COMPUTING:
By cloud computing the large amount of data collected from various devices can be organized and analyzed easily. There are 3 major cloud models in cloud computing namely:
IaaS: Infrastructure as a Service allows organizations to outsource all of its computer resources such as servers, storage and network services like content delivery and load balancing. In this type of service, the organization rents out the service but holds the equipment. The service providers should deploy such a system that tasks such as virtual servers and desktop management for different entities (employees and guest), virtual server pools management become automated
PRIVACY ISSUES:
1. Privacy and Security in cloud: When the entire data is stored and managed by an external company, trust between the vendor and client should be really high. As the data is stored in a distant place, there are many incidents about data loss, password leaks etc.
2. Increased Vulnerability: As large amount of work happens over the internet, data is more prone to be leaked by malicious hackers. Due to interdependency in the cloud system, a small compromise in any one of the servers may lead to leaking of large amount of data.
3. Vendor- Lock-In: As there is a large amount of dependency on vendor by the client it is very difficult or impossible to switch from one vendor to another as it is a really cumbersome process to transfer huge amount of data.
4. Limited Control: As the services run on third party equipments, the client has very less control over the data.
5. Downtime: Due to the dysfunctions of systems, from time to time users may have to face loss of services for specific durations. This sort of technical difficulties impact the efficiency of the system greatly.
6. Insecure API’s: All cloud providers provide a set of API’s in order to access their services. If these API’s are implemented badly, there might be some security issued caused due to them as well.
POSSIBLE SOLUTIONS:
1. Data Leakage Protection and Usage Monitoring: To do this, details like who is accessing, when it is accessed, location from where it is accessed and what happens if it is accessed must be known.
In order to monitor these kind of minute details, Modern Rights Management Services can be used to apply all restriction onto the accessing of data which is considered critical by a business. This can be done by creating transparent processes to get details and “self-destruct” mechanisms if sensitive data is trying to be accessed by any other third party.
2. Infrastructure Hardening: We need to make sure that VM images are hardened and clean. This can be done with initial system hardening while creating images. Images can be updated offline with latest technologies.
A firm process must be present in order to confirm that there is no lagging in desired configuration wither due to malicious or non-malicious changes.
3. End to End Encryption: Whole disk encryption can be done by which all data in disk is encrypted including user’s files. This fully secures system from offline hacks. This prevents vendor systems from stealing or accessing the information offline.
End-to-end encryption is one of...