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1. Executive Summary for this article.

Big data and analytics are becoming a hot topic within the agenda of most corporations. This change in opinion is a result of gawking admiration for companies like Google and Amazon which have soared past their competitors that are not using the same analytical platforms. Big data has the ability today to change business patterns and have a positive effect on performance gains. According to Andrew McAfee and Erik Brynjolfsson, the injection of big data and analytics into firms’ operations result in productivity and profitability at a rate of 5% to 6% higher than other firms at their level. Even with evidence showing the validity of big data and analytics there are those still fulfilled with skepticism. This opinion is probably a result of lost money on data warehousing programs of the past. At that time many experts made promises that did not deliver costing companies in the 1990’s. During this period many company executives lost their positions due to unfruitful plans and investments based off of data warehousing. The lack luster results were a result of slow growth since the systems were not really improved from the way companies and frontline managers decide on business matters. It is completely understandable that C-Suits of today may still have a moment of pause before taking the plunge.

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The article goes on to comment that after working with dozens of companies in six datarich industries that takes three mutually supportive capabilities to fully exploit the data and analytics. Those three areas start with the ability to identify, combine, and manage multiple sources of data. Next must be the capability to build advanced analytics models for prediction and outcomes optimization. Finally, the need for management to get equipped with the power to transform their organizations so that data and models actually lead to better business decisions. This last one is most likely the highest in importance. An integrated approach for data sourcing, model building and organization transformation is needed for positive business impact. Companies must ensure they choose the right data since the world of data and modeling has transitioned over recent years. Without the choice of right data companies may struggle to gain insight on what is important to them or even different from what they already knew.

Managers within companies must get creative in regard to the potential of external and new sources of data like formats of social media and the constant environmental change of internet due to cloud-bases systems. The article also warns on the use of pure data mining as it may flood the user with information that ends up providing inconsistent results. It is clear that companies in a transformation journey must ensure that their people have the knowledge and confidence to perform. Also, familiarity is imperative with the programs and data being used. Continual training and coaching are a must and companies have to use programs to keep employees driven. Company leaders need to focus on targeted output that is relevant to data sourcing, models building, and transformation of organizational culture and flexibility. The era of big data is evolving rapidly which is resulting in a “must act quickly” environment.

2. Critical Issues of this article

The article does cover several critical issues within its content. First it exposes that even with the rise of big data and analytics as well as all of the hype that the trends generate, experience is showing how the majority of companies are unsure how to proceed. Concern in regard to making large investments in big data and advanced analytics remains with leaders. Most in leadership positions are sticking with the mentality that their organization are not ready. It is likely that this is a result of a shortfall in understanding their existing data and how it works. Additionally, there is always the concern on a loss of investment on data-warehousing programs that have a history of not connecting with business process. Many executives were unable to see the involvements of the CRM technologies and were unable to take advantage unless they were willing to carry out a complex process. The associated lack of improvement results in weak performance, poor decision making, malfunction of disconnected systems, and new demands for data management. A final critical issue is that some current IT architecture can prevent the integration of siloed information kept in isolation resulting in a hinderance of communication and cooperation.

3. Relevant Lessons Learned of this article

As time moves forward in the business world it is necessary for skeptical leaders to begin a practical approach to capitalizing on big data and advanced analytics. By doing this they have the ability to enhance both their managerial and decision-making processes. This must be done to reach better performance and profitability within their scope of industry. The skeptical ideals of yesterday should be easier to put behind thanks to social media, improved informational technology and seeing other companies capitalize on taking this data plunge.

A second large lesson is how the possibilities of building advanced analytic models can help not only predict results but also develop plans to optimize those outcomes. Management must transform big data and analytics into their decision-making models. When doing this they simply must ensure they choose the right data and have the properly trained people to do it. Executives witness the success of organizations like Amazon and understand that information technology and data analytics are vital to their business and start to take different approaches.

4. Some of the most important best practices of this article.

Clear strategy on how to use data and analytics to compete and execute the right technology architecture and capabilities is one of the best practices from this article. As data-driven strategies continue to expand, a progressively important point of competitive differentiation will hold fast to make the market more challenging resulting in a push of business leaders to consider useful analytical approach. This article illustrates another best practice according to a research by Andrew McAfee and Erik Brynjolfsson, companies that inject big data and analytics into their operations show productivity rates and profitability that are 5% to 6% higher than those of their peers. Business practices within this everchanging internet era continue to evolve and make data more available as well as accessible in the form of emails, text, videos pictures and more. Older IT structures may slow new types of data sourcing, storage, and analysis while existing IT architecture may prevent the integration of siloed information, the birth of New cloud based technologies allows firms to meet big data demands cost-effectively and facilitate the establishment of an IT infrastructure that drives innovation. To ensure the path for success companies started to model process by determining which factors are impacting sales volumes and other areas of operation to determine data models which would lead to greater insights for making business decisions and are more broadly understood by managers.

5. Relating this article with the topics covered in class.

This article easily related to some of the topics in our class like extrication, transformation, data integration and load processes to understand the importance of business performance management. Also, within the article is some evidence-based examples and in-depth analysis of the topic of Data Analytics. It goes on to point out the way Data analytics models allow managers to forecast and optimize end results. Furthermore, it covered how computerized systems penetrated complex managerial areas as well as the role of data warehouses in decision support as covered in class. As companies continue to learn the needed core skills associated with big data the ability to build superior capabilities may soon become a decisive competitive asset.

6. Alignment of the concepts described in this article with the class concepts

Data warehousing and architecture class concepts teach us to use the right data to conduct analysis and generate summary reports from various other data reported. Furthermore, the concept of Data Warehousing supports data-driven decision making. When we choose the right data, Bigger and better data give companies more-extensive views of their business surroundings. It seems that a data-mining approach can lead to a never-ending research for the true meaning of data despite inconsistency in results, companies can put the concept of data mining to good use. The concepts describe in this article are consistent with how companies actually arrive at decisions to achieve organizational goals. The approach to make analytics work for businesses designed tools for experts, managers and employees to focus on new methods to permeate their firms and engage in organizational changes that enable executives to see what was previously invisible, improves operations, customer experiences, productivity and strategy. Because of the use of analytics as well as the evolution of big data Business Performance Management as we see in class concepts represent a real-time system that alerts managers to potential opportunities, impending problems, and threats, and then empowers them to react through models and collaboration.

Dominic Barton and David Court. (October.2012) Harvard Business Review. Making Advanced analytics work for you. Retrieved from.file:///Users/Newuser/Downloads/Making%20Advanced%20Analytics%20Work%2 0For%20You%20(1).pdf Sharda, R., Delen, D., & Turban, E. (2018) Business Intelligence, Analytics, and Data Science.


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