Analyzing Big Data: Nielsen's Analytical Maturity and Structure

This report analyzes the use of big data within Nielsen, a marketing research company. It explores how Nielsen utilizes big data for decision-making, including identifying market trends and generating successful marketing plans. The report evaluates Nielsen's analytical maturity, assessing its ability to overcome challenges in data processing and model deployment. It highlights Nielsen's strengths in obtaining data, quantifying problems, and integrating development models. Furthermore, the report proposes a program to enhance Nielsen's data analytics structure, suggesting the implementation of tools like Hadoop and focusing on areas such as clustering, classification, regression, and neural networks to improve big data analysis capabilities. The report concludes by emphasizing the importance of these factors in achieving a modified and improved version of big data analysis within the organization.

Running head: BIG DATA IN ORGANIZATION
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Name of the Student:
Name of the University:
Author Note:

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Big data is using as the analytical tool for the management of the business process in the
organization. In this paper the impact and the use of the big data in Nielsen has been discussed.
The discussion covered the certain topics.
Identification and description of the company:
The chosen company in this discussion is the Nielsen. Nielsen formally known as the AC
Nielsen is marketing research company (Bruns, 2016). The type of work this organization does
needs to process a lot of information, simultaneously. However, the processing and the managing
of the information can be done through the use of big data.
Using big data in decision making process:
The big data process and generate the result after the processing of the large amount of
data. The generation of result consists of the useful information that can help Nielsen to detect
the current trend in the business market. the result can be consists of the overview from the
bunch of surveys generated on a issues. Evaluating these information the Nielsen can predict the
future for a certain business plan or they can generate the successful marketing plan for the
product. In this way, the big data can help in the decision making process for the company in
order to make the successful business plan.
Evaluating the analytical maturity of the company:
There is a certain framework for the evaluation of the analytical maturity. There are
common challenges , the organization has to ace while the development of the business
processing (Alyass, Turcotte & Meyre, 2015). It can be said tat the organization is analytically

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mature if it can face these challenges in an effective way. It has been seen that the Nielsen has
successfully faced the challenges mentioned below:
The Nielsen can easily obtain the data necessary for the building of the blocks.
These models are helpful in the generation of the quantifying problems in the organization. The
Nielsen has the strategy that can mitigate the lack of repeatability during the deployment of the
models and building of the models. The analytical approach taken by the Nielsen can integrate
the different development models across the organization (Du,2014). The organizational
structure can differentiate between the models built from the data and the business plans.
However, the organization faces confusion regarding the above mentioned factors. The higher
level of the analytical maturity can be achieved by the Nielsen by reducing the confusion over
these factors.
A proposed program for the organization that will enhance the data analytics structure in
the organization:
There are certain tools that are use for the analysis of the data. In this context the
organization is already using specific tools for the data analysis (Fan, Han & Liu, 2014). In
order to improvise the system certain software like Hadoop can be used. The improvisation
should also focus on the following areas like-
Clustering and the segmentation: The data can be divided into smaller similar groups.
Classification of the data: The data are classified into the pre defined classes and managed by the
analyst in the clustering model.

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Regression: It is used to discover the relationship between the variables which are
dependent on each other, it also determines the dependency of the value of independent variables
and the changes of the value.
Mining of the item set and association: It finds the relationship between variables which
are statistically relevant.
Correlation and similarity: It used in order o identify the undirected clustering algorithm.
Neural network: the neural network can be used in the analysis for the machine language
driven system , which is helping in the analysis of the large amount of data.
These factors and the attributes can be included in the implementation of the modified big
data program. This will help the Nielsen to achieve the modified version of the big data analysis.

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References
Alyass, A., Turcotte, M., & Meyre, D. (2015). From big data analysis to personalized medicine
for all: challenges and opportunities. BMC medical genomics, 8(1), 33.
Bruns, A. (2016). Big Data Analysis. The Sage Handbook of Digital.
Du, H. (2014). Big Data Analysis. Software Engineering Project. Software Engineering Project.
Fan, J., Han, F., & Liu, H. (2014). Challenges of big data analysis. National science review, 1(2),
293-314.