Comprehensive Summary

Module 05, Online Discussion HBR -MITSMR: Why IT Fumbles Analytics Apr 12, 2020 11:23pm Johnson, Kelly Ann Module 5 – Online Discussion HBR -MITSMR Why It Fumbles Analytics Executive Summary What we learn first and foremost about c -suite executives is that they often take IT programs and more specifically data analytics projects for granted and while oversight is present during the implementation phases of such a project, there is a lot to be desired in terms of continued ma intenance and understanding of the system. Even after IT programs such as CRM are implemented, executives seem to be dissatisfied with the end result and that is because they do not spend enough time trying to understand the data that these systems are pro viding. Such data provides insights into a multitude of information essential to understanding other areas of the business apart from mere planning and budgetary goals and performance. According to, (Marchand & Peppard, 2013) executives should focus on exp loring the information that these systems provide and find ways to make this information a valuable asset to the company in ways that can support the decision making process. In order to utilize the information that big data analytics can provide to a corp oration, it is essential to keep in mind the five (5) recommended guidelines that the report provides. First off, one of the most important aspects of these guidelines is to utilize the individuals most attuned with exploiting and interpreting this data an d then implement this within the core processes of the company. This means that managers and department heads cannot simply ignore the information being presented to them and instead can make well informed decisions based off of this information and not ju st make a decision that they think is right without first consulting the processes and systems that were implemented for such purposes (Marchand & Peppard, 2013). Secondly, managers should not always rely on particular assumptions or logic that results from using the data analytics software, rather they should exploit the continuous data being collected over time, and share this data between departments in order to obtain a complete view of how this data affects performance across the various de partments and how this relates to market trends and how better to provide solutions to unique business problems. The third guideline is to include the necessary personnel such as IT professionals who understand the data side of things but also behavioral s cientists who understand how people think and can interpret new ways to improve performance based off of these unique traits and characteristics. By utilizing well trained personnel into these various areas of expertise will allow organizations to know wha t data to use, what questions to ask, and how to interpret these results in order to produce meaningful solutions for the company. The fourth guideline is to focus on learning whereby colleagues share information and interact through collaborative activiti es. This generates trust, cohesion and develops ideas and outcomes that are shared by the entire team thereby fostering a company culture determined to discover and learn from each other. Lastly, c -suite executives should focus more on solving business pro blems by understanding that each process within the normal functioning of a cooperation is interconnected, assists with maintaining and boosting connections and provides a more integrated approach to reducing risk and achieving objectives. Therefore, by ut ilizing these analytical tools, sharing and exploring data across all platforms and departments and cultivating a culture of strategic performance it will allow corporations to take full advantage of the big data systems that they have implemented. 1. Three critical issues of the article. Explain why, analyze and discuss in great detail. • The first critical issue lies within the assumption that both IT systems and data analytics software are one and the same. This is an incorrect assumption and will lead corp orations to making incorrect or misinformed decisions without adequately utilizing the tools that they have at their disposal because they fail to realize the potential of the system is not only in its implementation but in its continued exploitation. • The second critical issue is the use and continued sharing of this data. Not only that but c -suite executives should understand the importance of developing this data through departments as only with departmental exposure and sharing of critical knowledge that each member of these departments possess, can complete discoveries be made. Otherwise data becomes trapped within the vast departmental silos of each of these departments and becomes useless in providing assumptions, forecasts or solutions needed to maint ain a competitive advantage and excel within the target market. • Lastly, it is essential to develop not only baseline questions in relation to the what’s, how’s and why’s of daily operations but to ask second -order questions that can assist management in th e decision -making process. By asking second -order questions management can open up avenues to new processes or initiatives that will result in better performance both internally and externally which leads to improved products, services and customer interac tions and feedback. 2. Three most relevant lessons learned in the article. Explain why, analyze and discuss in great detail. • A relevant lesson learned from this article is the importance of integration within a corporation and the added advantage that this provides for both internal processes and external results. By promoting an integrated, team -oriented company culture, all personnel will become more attuned with the expectations of management but also focus on achieving company goals and improving perform ance by optimizing the use of the data analytics software to its full potential. • Another relevant lesson learned is not only to utilize IT professionals within the company but to also introduce and integrate behavioral scientists that can determine, culti vate and exploit the behavioral patterns of consumers, front -line staff and competitors. By doing so, the company can better understand how to perceive problems that arise, how to understand these problems and utilize information in order to develop soluti ons, share ideas and the knowledge necessary to continuously rise above the competition. • The third lesson learned from this article is to fully utilize the systems and processes that were implemented at the inception of the data analytics software to the c ompany on a regular bases and not just “set it and forget it”. One of the biggest mistakes is to assume that managers’ decisions are correct whether the data proves or refutes this decision. By not following the insights that this software provides, manage rs will not be able to make the most informed decisions as possible which could lead to problems and unnecessary risks. 3. Three most important best practices of the article. Explain why, analyze and discuss in great detail. • One of the most important best practices expressed within this article is that of solving problems. By regaining a renewed focus on developing risk aversion skills that can be developed and interlaced within the daily operations of the company, management can better control risk. This is the direct opposite of what managers try to do with their new software systems and that is trying to avoid the risks in implementing such systems. Instead they should focus on solving business problems and how this software can uniquely assist in this regard. • Another best practice highlighted in this article is that of identifying the appropriate techniques and tools to utilize and the strength and weaknesses of these particular techniques and tools. By having greater involv ement in developing business insight models and how to determine what is useful data from what is noise is key to utilizing the skills of these data scientists and behavioral scientists to their full potential. • Companies should strive to demonstrate cause and effect in order to discern what the relationships and patterns contained within data can lead to desired or less favorable outcomes. By understanding the problem to be solved, the causes of the problem, the factors that lead to such, and the ability to understand what can be done differently the most useful models can be created through which outcomes can be achieved and expanded upon. 4. How can you relate this article with the topics covered in class ? Explain why, analyze and discuss in great detail. This article relates to the topics covered in class through the importance of data mining and its validity in providing usable data from unstructured data in order to increase business performance and competitive advantage. Further, by utilizing the indivi duals uniquely qualified to extract and interpret this data, companies will be able to optimize their data analytics software to its full potential. 5. Do you see any alignment of concepts descried in this article with the class concepts reviewed in class? Which are those alignments and misalignments ? Why? Explain why and analyze and discuss in great detail. Alignment of concepts can be seen in several areas including the extraction of the most relevant data, how this data is relevant to the project or goal that the company is attempting to achieve and how to optimize this data to make the right decisions. Further, by learning from classification methods data scientists and behavioral scientist can use this data to classify future cases and better predict the best option for company performance and the best option for consumer satisfaction. The concept of creating ensemble models for better predictive analytics is also an alignment of concepts between this article and the textbook as this provides a tool for c ompanies to understand what is working and what is not and how to improve on these advantages and disadvantages that the data presents. References Marchand, D., A. & Peppard, J. (2013). Why It Fumbles Analytics. Harvard Business Review. Retrieved from: -it-fumbles -analytics (Links to an external site.) Sharda, R., Delen, D. & Turban, E. (2018). Business Intelligence, Analytics, and Data Science – 4th Edition. New York. Pearson. from Module 05, Online Discussion HBR -MITSMR: Why IT Fumbles Analytics Apr 16, 2020 11:03pm Johnson, Kelly Ann Hello Dailenys Great thoughts and detailed expression of the points of discussion requested of from this article. I would like to add to this by providing some more information based on the critical issues, lessons learned, best practices and topics covered so far in this course in relation to the article. Based on the best practices that you highlighted pertaining to the cost of the syst em, optimal use of the data that the system provides and the best way for users to understand and incorporate this information into usable insights it is pertinent to understand why the system is being implemented. From there management and staff can deter mine what questions need to be asked of the system, are the solutions provided by the data and algorithms suitable for the problem at hand and how can these be put into practice in the best way. Often times when it comes to data analytics, c -suite executiv es are quick to turn to data scientists and behavioral scientists to make optimal use of the high costs programs that have been put in place and these individuals are great to have in house but they should not forget that vital information can also come fr om involving the entire team of professionals within the company. That is, “1) asking employees to state the objective, 2) giving freedom to employees to be creative in the “how” part of any task, and 3) questioning whether the metrics are in

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