Discussion

  • Consider the importance of diagnosing business problems and determining analytical methods.
  • Review your Week One assignment where you described a business challenge within an industry of your choice and how it can be reframed as an analytical challenge..

This discussion has 4 parts – 

a. Share a brief summary of your chosen industry, one business challenge, and your view of reframing it as an analytical challenge

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b. Share one academic journal reference that supports your research.

            1. Provide the APA citation

            2. Attach a of the article

            3. Provide a brief summary indicating the article’s significance to your research

Reminder: In your initial response and discussions with your peers, use your own words & share your own perspectives and findings

2

Running head:

DATA ANALYTICS LIFECYCLE

2
DATA ANALYTICS LIFECYCLE

Student name:Manasa Kaveri

Student number:002848344

Professors name: Dr. Helen Schleckser

Submission date:01/08/2020

Data analytics is one key aspect in both data management and governance. Different industries apply data analytics tools and phases to understand different aspects in their business and strategizing and improving their weaknesses. The insurance and finance industry is one among the key industries in most of the developed countries. Finance and insurance company serves almost all populations therefore accessing and receiving a lot of data from their customers (Dietrich, 2016).

Data is a key business drive in this industry and stakeholders experience the necessity to apply the data analytics lifecycles in analyzing all the data they receive in daily basis. Companies in this industry sell financial products either in terms of bonds, insurance or stocks among others. In the USA this industry is one among the largest industries and has more than. 4.7 million establishments with combined annual revenue of $ 4.5 trillion (Briggs, 2017). In this industries success depends on marketing ability, return on investment, business activity and customer income. The largest companies in this industry enjoy success and huge profits due to quality branding and product awareness which results from immense marketing. On the other hand, small companies are able to compete effectively through marketing and effective innovation strategies.

Business challenge

One key challenge experienced in this industry and which remains to be a business challenge is security and data privacy. In the data analytics lifecycle, it is very clear that there are six phase which are iterative and sometimes they each other. These phases are data discovery, data accumulation, data model planning, execution of data models, results communication and finally operationalization. With the realization of big data and data analytics techniques businesses enjoy immense opportunities and data management capability (Zhang, 2018). However, businesses still strive to establish better security and privacy models to secure their data centers and any information which they share with their stakeholders. With the growth in the use of mobile services and online payment methods the privacy and security of customer information and details still remain questionable. Likewise, with the growing cybercrimes and other malware activities the insurance and finance industry still struggle to safeguard customer related data and other relevant data which they receive, send or request from their stakeholders daily.

Reframing the business challenge to analytic challenge

Data security and privacy is a challenge almost in every industry. Organization receiving or haring different information frequently or in continuous mode may find it difficult to attain high data security and privacy standards. In data analytics this challenge can be reframed as follows

Unsecure transaction logs

Organizations dealing with large volumes of data may be unable to secure login activities and data sharing activities. All logins and data transfer within different levels must be protected and only authorized people should be allowed to do the same. Proper monitoring of transaction logs is important.

Poor Validation and data filtration.

All data collected must be validated and filtered to remove any malware or harmful data. This will help improve data security and privacy if it is done during the data input stage.

Insecure distributed framework activities.

Organizations need to secure all authorized mappers and protect any data available. Securing and protecting data in real time Organizations may find it difficult or expensive to conduct regular security checks. It is therefore important to scrutinize data during the first stage of data collection or immediately upon receiving the data.

Lack of data access control tools

Businesses receiving a lot of data may fall to the trap of data insecurity due to lack of proper data access control tools. Organizations dealing with big data during the analytic stage need to employ data access control methods such as user pass words, encryption and data decryption among other techniques (Hoel, Chen, & Cho, 2016).

Poor data classification or authentication

Whether big data or small there is the need to classify and establish data originality during the analytics lifecycle. It is very important to classify data as per its origin so that organizations can be able to certify its originality and transparency.

Conclusion

The data analytics lifecycle is a very important thing within organization dealing with massive data. Proper data collection tools, filtration mechanisms and access control methods are of great importance for the facilitate data security and reliability. On the other hand, organizations need to put in place data governance tools and proper data communication procedures which are not open for manipulation (Sturtevant, Lalancette, Lack, & Schneck, (2018).  Data auditing and constant security checks are also critical in ensuring that data security and privacy are managed.

References

Briggs, (2017). The Impact of Innovation on Financial and Insurance Services Exports. The BE Journal of Economic Analysis & Policy, 17(4).

Dietrich, (2016). U.S. Patent No. 9,262,493. Washington, DC: U.S. Patent and Trademark Office.

Hoel, Chen, & Cho, (2016). Privacy Requirements for Learning Analytics–from Policies to Technical Solutions. In Workshop on Ethics and Privacy for Learning Analytics, Monday.

Sturtevant, Lalancette, Lack, & Schneck, (2018). U.S. Patent No. 10,049,225. Washington, DC: U.S. Patent and Trademark Office.

Zhang, (2018). Big data security and privacy protection. In 8th International Conference on Management and Computer Science (ICMCS 2018). Atlantis Press.

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