Week 3 project

Running Head: BIG DATA AND SOCIAL NETWORKS 1

BIG DATA AND SOCIAL NETWORKS 4

Big Data and Social Networks

Student’s Name:

Institutional Affiliation:

Course:

Date:

Part I

With business intelligence, this can be hosted locally by the company computers (on-premises) or on the virtual networks e.g. the internet. It is important to understand that a company will not always have the required data to meet its needs. For this reason, external sourcing of data is very important and this is where big data comes in. Big data majorly comes from transactional data, machine data, and social data. For this information to be useful to the company, one has to understand the existing problem to come up with a solution from the big data. Therefore, the first role that big data plays in marketing intelligence is providing answers to the company’s questions. For example, what is the effect of the new management on the company? These will be answered through a number of ways from the three primary sources of big data with every change in the trend being used to come up with an answer to the questions. The second role is the company using this information to refine its marketing strategies. After the company has gotten answers to its earlier problems, it is then used to redefine its strategies. Any gap that needs to be filled through the company marketing strategies is identified and the necessary changes made. Another role played by big data in marketing intelligence is machine training. Where machine learning has to be used as a tool, then machine training must precede. Big data is used as a source of data for machine learning from which machine training will be done to ensure this process is of help to the company and finally help increase their profits.

For cloud business intelligence, it is considered better than on-premises intelligence due to its higher capabilities. The first major role played by cloud business intelligence is accessibility from any browser or device. For this reason, it makes it easy, unlike traditional software which requires to be installed on a device for access. This is a very important tool when it comes to market intelligence since it should be easily accessible when there is a need to use it. Another role played by cloud computing in business intelligence is the security of the data, where it is much secure. This is because, unlike on-premises business intelligence, cloud-hosted intelligence is under more secure management considering the resources which have been invested in data security. Finally, cloud computing has made business intelligence more user-friendly, unlike the traditional on-premises software. This is because cloud computing business intelligence tools they much easier to improve considering they are hosted on virtual networks.

There was a journey from on-premises to cloud computing business intelligence which thoughts will be shared in this part of the paper. The first thing that cloud computing has helped in business intelligence and analytics is making it cheaper and available to more people. On-premise business intelligence software is mostly custom-made and therefore quite expensive (Patel, 2021). This is because they are designed to serve the specific interests of a company and therefore this usually turns out to be very costly. For this reason, business intelligence tools were not being used by many companies. A number of companies considered it only suitable for the big companies which had enough resources to invest in technology. With the coming of cloud computing business intelligence, the idea was to come up with business intelligence tools that could serve the needs of almost all companies. Considering that one virtual hosted software could be used by a number of people this meant it would be cheaper to use. For this reason, a number of companies are able to incorporate business intelligence and analytics into their marketing due to availability and favorable cost. Also unlike on-premises business intelligence, cloud computing has made it more user-friendly. Earlier on, the traditional software required IT specialists to operate them and that would also mean extra cost to the company. This would also mean that the IT specialist had to understand business operations and this made them even more expensive to hire. With the evolution into cloud computing business intelligence, it is more user-friendly. One only requires a bit of training before starting to use the BI tools. This makes it cheaper as labor cost is withdrawn and someone with a better understanding of the business enterprise e.g. the manager can operate the business intelligence and analytics tools.

Big data and cloud business intelligence come along with a number of security and public safety concerns with privacy being a major one. Big data and cloud intelligence sources will mostly violate the privacy of the customers. A good example is transactional data which can be used for fraud purposes. Fraud will mainly happen through phishing where people purport to be from reputable companies and then send emails requiring people to reveal their personal information (Hillier, 2021). This has been highlighted as a major challenge when using these business intelligence tools and therefore must be dealt with. Another issue in big data and cloud intelligence is ethical issues. Considering this is something that is still evolving, it is very hard to establish ethical considerations as new things come up every time. Therefore, some ethical issues in this have not been addressed whereas the public is against some of the things that happen. Therefore, ethical guidelines need to be developed to ensure that the public is being protected from any harm that may come upon them from big data and cloud business intelligence. Another potential problem that may come along with big data and cloud business intelligence is being used for illegal purposes (Hillier, 2021). This can mainly be done in collaboration with companies that perform legal tasks to fund illegal practices with the information. From this, it can then be used to carry out the illegal practices since the information required has been acquired. A good example is terrorism which can be carried out after information has been acquired from big data and cloud business intelligence. This means that some sensitive information should not be available for use through big data and cloud business intelligence.

Part II

The R programming language was developed by the R core team in 1993. Basically written in programming language C, Fortran, and R itself, it was designed for purposes of graphics and statistical computing. Business analytics and intelligence, therefore, rely heavily on this programming language since it is designed specifically to serve these interests (Weston & Yee, 2017). In data science, it is considered one of the best programming languages and is currently ranked 14th in the list of best programming languages. Considering that the software is currently free, this makes it a better option for use by students and other researchers going into data science. The process of installing the R programming language is outlined below (TechVidian, 2021):

1. Going to the CRAN R Project website, choose to download R for Windows, Linux, Mac OS X.

2. Click install for the first time, then download R X.X.X which basically means you’re downloading the latest version of the programming language then save the exe. file.

3. Run the exe. file and remember to follow the given instruction in each step.

4. Select the desired language e.g. English and then proceed to accept the license agreement.

5. Click next and then tick all the components to be installed. Click next and then define the path where you want to install the programming language then proceed by pressing next.

6. Now, wait for the installation process to complete and complete installation by clicking finish.

Every program has its strengths and weaknesses and R is not an exception to this. For this reason, the first strength of R is its open-source nature. This means that anyone could get the underlying code used to run the program and then add onto this their own code. Therefore, it will be very convenient to perform statistical tests after one thinks of them. Also, one can easily add their tools to the existing R program considering it is open source and therefore someone who understands using the program will enjoy this they can make it even more powerful to meet their own needs. In this programming language, bugs are easily identified and fixed (Data Flair). Bugs can be very stressful for anyone using a programming language but with R since one can look at the code they could easily identify when debugging is needed to fix the program. A major weakness with R is the basic security which it lacks and a major consideration for every programming language. For this reason, it cannot be attached to web applications since it is more vulnerable to attacks. R also requires someone with a good foundation of programming since it is a complicated language. Therefore, anyone who lacks programming basics will have a hard time learning the program considering that algorithms are spread across various packages. This means a programmer has to first understand the packages before they can be able to use the R programming language and this keeps away a number of people.

References

Data Flair. (2021). Pros and Cons of R Programming Language. Data Flair. Retrieved 19

September 2021, from https://data-flair.training/blogs/pros-and-cons-of-r-programming-language/.

Hillier, W. (2021). Is Big Data Dangerous? The Risks Uncovered [With Examples].

CareerFoundry. Retrieved 19 September 2021, from https://careerfoundry.com/en/blog/data-analytics/is-big-data-dangerous/.

Patel, N. (2021). The Evolution of Business Intelligence and Analytical Reporting. BMC Blogs.

Retrieved 19 September 2021, from https://www.bmc.com/blogs/analytical-reporting/.

TechVidvan. (2021). Installing R and R-Studio. TechVidvan. Retrieved 19 September 2021,

from https://techvidvan.com/tutorials/install-r/#install-r-windows.

Weston, S., & Yee, D. (2017). Why You Should Become a UseR: A Brief Introduction to R.

Association for Psychological Science - APS. Retrieved 19 September 2021, from https://www.psychologicalscience.org/observer/why-you-should-become-a-user-a-brief-introduction-to-r.