Week 2 DQ Responses Big Data Subject: Big Data Analytics Q1. Please read the below paragraph and write your opinion. You have any suggestions? Note: 1

Week 2 DQ Responses Big Data

Subject: Big Data Analytics

Q1. Please read the below paragraph and write your opinion. You have any suggestions?

Note: 100 words with intext citation and references please.

Technological advances have led to the generation of huge amounts of data that has to the creation of big data concepts to define large amounts of data that is unmanageable. For meeting future as well as present social needs such as healthcare provision new approaches for the data organization to get meaningful information is necessary (Pries & Dunnigan, 2016). The ability to detect patterns and transform big volumes of data into actionable information for decision-making is needed. Big data is currently being used in healthcare to provide solutions.

          Big data analysis is complex because it requires a combination of electronically captured information. Unstructured data does not have a predefined structure such as audio files, images, and video recordings are unorganized that require more processing. The data is also challenging in searching for it and indexing by first establishing its relevance which is also difficult (Sharda et al., 2017). Data can be used to predict both current and future trends. 

 Some of the questions that can be helpful in data collection from the patients can be;

1. What medical history do you have?

2. What medications do you take both prescription and non-prescription?

3. What is your history of alcohol and smoking history?

Q2. Please read the below paragraph and write your opinion. You have any suggestions? Note: 100 words with intext citation and references please.

The terms ’unstructured’, ‘semi-structured’, and ‘structured’ often come up when discussing about data analytics.
Structured data is data that has been transformed into an understandable format, usually by following a nicely defined data model. The data model is able to map raw data into pre-defined fields. SQL databases, with all the rows and columns, is a great example of structured data. Structured data is oftentimes quantifiable.
Semi-structured data is somewhere between structured data and unstructured data. It has some consistent characteristics, but it doesn’t have enough rigidity to fit into a relational data model. We can attach organizational properties to semi-structured data to make it more manageable.
Unstructured data is data that shows up in its raw, unprocessed form. Qualitative data represents a good portion of the unstructured data. These data typically is hard to organize into pre-defined data models. NoSQL databases are more suited to manage unstructured data.
In the context of the DIKW model, structured data & unstructured data need to come together in order to be extracted into useful information.
In the case of a healthcare clinic, both quantitative data and qualitative data can come in handy. The unstructured, qualitative data is equally important for diagnosing as quantitative health measurements, if not more. Not everything about a person’s health condition is quantifiable, otherwise doctor’s jobs would become extinct. A doctor’s brain is like a supercomputer, it automatically processes any unstructured data, and then turns it into descriptive information, then extracts it into useable knowledge, and eventually into forward-looking wisdom.

Subject: Internship

Q3. Please read the below paragraph and write your opinion.

Note: 100 words with intext citation and references please.

The company I work for was originated in China and only started doing business in US since recent years. It is a different perspective than the companies that were exposed to diversity environment since foundation. Increase diversity and have team mebers from differnt background is benefitial for a healthy work environment is something my employer is reaching for. The most common challenges we face with diversity is the language barrier. Most of our teams are located in China and a large portion of our clients speaks very little English. Many documentations are only available in Chinese as well. Thus any new recruit we hire, we would have to pair them with someone who speaks Chinese, and they are getting their trainning from this person while all the documents are being translated as we use them.
This is very challenging to both the Chinese team members and the recruits who don’t speak Chinese. We use a lot of automatic translations to address the communication, yet there are still meetings hosted by engineers who don’t speak English at all.
My company is trying to approach this method by hiring more people who speaks English and with in-depth background with the technology we use. Also we are inviting all the team members to produce more documentation translations so we can help new recruits who don’t speak Chinese. It is slow but a moving progress, and my employer feel optimistic about future diversity.

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