Question

Key roles for a successful analytics project. Select a data analytics of your choice and discuss how the following roles add value to this initiative: Business User, Project Sponsor, Project Manager, Business Intelligence Analyst, Database Administrator, and Data Engineer. Again, please note that you will discuss specific activities that each of these roles may perform on a data analytics project that you select.

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Answer 1

Data analytics has been known to be one of the most important parts from which the companies are being affected a lot. It enhances the business along with making the business survive the customer's satisfaction throughout the journey. We will start by explaining the data analytics using different techniques and how to maintain data analysis in the organization.

OLAP:

• OLAP (Online Analytical Processing) is one of the technology behind the BI (Business Intelligence) applications. It is a powerful technology for the data discovery which includes the capabilities of report viewing, complex analytical calculations and also certain kind of planning.
• It mainly performs multidimensional analysis of the business data and also provides the results of complex calculations, trend analysis and also the sophisticated data modeling.
• It helps the end-users to perform the ad hoc analysis of data in the multiple dimensions thereby providing the insights and also understanding they need for better decision making.

Advantages of OLAP:

• OLAP technology has achieved the ability of faster access for the shared multidimensional information. It also helps in maximizing the efficiency of business intelligence.
• It also helps in building a successful business continuity plan analyzing the report and multiple operational activities. It also consists of one most important advantage that is it can create very fast aggregations and calculations of the underlying data sets.

Hence, these are the advantages of OLAP.

Data Mining:

Data mining is the most used technique for enhancing the versions of an organization when it comes to analyzing things for the organization in order to make decisions, assumptions, etc. It is the best technique, now we will list down some of the ways it helps organizations with. Here is the list:

• It helps organizations in making better decisions.
• It improves the security risk posture for the organizations.
• It also helps in improved planning and forecasting.
• It also makes the improvement in competitive advantages.
• It reduces the costing of the organization.
• It also helps in getting new customer acquisitions.
• It also helps in the development of new products.
• It plays a major role in the development of customer relationships.

Hence, these are the ways in which data mining helps organizations.

Example of using data mining techniques:

• The most known example of these techniques such as clustering, classification, etc are been used in E-commerce websites where the user's actions are being noted and results are been shown accordingly.
• Suppose, if a user has viewed a pair of shoes which are from an "XYZ" company then he will start to have suggestions of similar shoes from the same company.

Hence, this is how data mining techniques are been commonly used in organizations.

Example of Anomaly Detection:

• Suppose for an example, if there are a certain number of transactions made for a particular product is 900$ and one or two transactions made for the products is relatively low such as 100$ then we know that whether there is something fraudulent going on or some other measures to buy this product has been done.

Hence, this is one of the examples of anomaly detection.

Is an open-source data mining tool better than a commercial one? Why or why not?

• The open-source data mining tools will be the best as they have higher advantages than the commercial one. The explanation for this will be written as we go down:
  • When it comes to open source the best programming languages in the current market Python and R will add on to the features of the open-source tools.
  • We all know how much powerful Python and R are in terms of the libraries and the framework they offer for the data analysis and visualization tools. This will be the major reason for using open source tools.
  • The larger community in terms of quantity and quality is also one of the reasons to use open-source data mining tools. As there would be more and more contribution to the community which will lead to faster bug fixes.
  • There will be standardized modules and the functionalities which can be used by each one of the people who is making out for the community for free.
  • One can also find new talent to work with these technologies as newcomers would not have access to commercial software which will make them eligible to work only with open source tools without providing them with any training.

What are the security implications of insufficient data classification?

The data classification is one of the most important things that should be done in order to gain somethings which are fruitful from data and must also be managed but what if the data classification is not done properly.

• There are many classifications of data being practiced in the world in which we can find out many possible outcomes for the classification of the data. There is been some certain classification such as :
  • Top Secret
  • Secret
  • Confidential
• The most important is to be able to classify all the data according to the given classification but if the classification is not being done properly there can be many issues such as files can be misplaced in any of the sections and later on it can deal problems.
• If the data is been misplaced to any of the other sectors of data in which it should not be then what will happen. The security must be strong for that. Hence, we can start by implementing certain things in our security plans. They are as follows:
  • We will develop a good data classification scheme if the old one is not been able to classify the data appropriately.
  • We will let us understand what is achievable through the data realistically and then classify the data
  • We can classify the data strategy as soon as the data is approved to any one of the sectors.
  • Each of the sectors will consist of various security provisions so that it won't be easy at all to break into its security.
  • Aligning the data with the best frameworks will also be the best practice to save the data.
  • The classification of the network is required instead of the data.

Hence, these are some of the security implications in case of insufficient data classification.