Why Big Data Isn't Enough Case Study Memo

Case Study Recommendation Memo Assignment

At Fern Fort University, we write Why Big Data Isn't Enough case study recommendation memo as per the Harvard Business Review Leadership & Managing People case memo framework. If you are looking for MBA, Executive MBA or Corporate / Professional level recommendation memo then feel free to connect with us.

Other topics that can be covered in the above case memo are . The recommendations in the case memo are - aligned with strategy of the company, based on robust data, and provide a clear roadmap for execution.

Order Now - Why Big Data Isn't Enough Case Memo

Order Now - Why Big Data Isn't Enough Porter 5 Forces Analysis & Industry Analysis

Why Big Data Isn't Enough Description

Leadership & Managing People Case Study | Authors :: Sen Chai, Willy Shih

This is an MIT Sloan Management Review Article. As "big data" becomes increasingly integrated into many aspects of our lives, we are hearing more calls for revolutionary changes in how researchers work. To save time in understanding the behavior of complex systems or in predicting outcomes, some analysts say it should now be possible to let the data "tell the story" rather than having to develop a hypothesis and go through painstaking steps to prove it. The success of companies such as Google Inc. and Facebook Inc., which have transformed the advertising and social media worlds by applying data mining and mathematics, has led many to believe that traditional methodologies based on models and theories may no longer be necessary. Among young professionals (and many MBA students), there is almost a blind faith that sophisticated algorithms can be used to explore huge databases and find interesting relationships independent of any theories or prior beliefs. The assumption is: The bigger the data, the more powerful the findings. As appealing as this viewpoint may be, authors Sen Chai and Willy Shih think it's misguided - and potentially risky for businesses that involve scientific research or technological innovation. For example, the data might appear to support a new drug design or a new scientific approach when there isn't actually a causal relationship. Although the authors acknowledge that data mining has enabled tremendous advances in business intelligence and in the understanding of consumer behavior - think of how Amazon.com Inc. figures out what you might want to buy, or how content recommendation engines such as those used by Netflix Inc. work - applying this approach to technical disciplines, they argue, is different. The authors studied several fields where massive amounts of data are available and collected: drug discovery and pharmaceutical research; genomics and species improvement; weather forecasting; the design of complex products like gas turbines; and speech recognition. In each setting, they asked a series of questions, including the following: How do data-driven research approaches fit with traditional research methods? In what ways could data-driven research extend the current understanding of scientific and engineering problems? And what cautions did managers need to exercise about the limitations and the proper use of statistical inference?

Purpose of Leadership & Managing People Case Study Recommendation Memo

A Case Study Memo or Case Study Recommendation Memo is a routinely used document in leading organizations, and you may be writing number of such memos to executive leadership to “sell” or elevate an initiative that either you are undertaking or you wanted to kick start. Therefore, it is essential that you have a professional case study recommendation memo.

The purpose of a recommendation memo is to concisely recommend a course of action and provide rationale supporting the recommendation. The case study recommendation memo is a one-two page document (not including exhibits) that recommends your course of action and rationale. This format promotes a concise and clear strategic thought process.

Elements of a Case Study Recommendation Memo for – MBA & Executive MBA

1. First Paragraph of Why Big Data Isn't Enough recommendation memo

• This paragraph expresses your intent or action that you required after reading the Why Big Data Isn't Enough case study (This recommends……).
• Topic overview of the case study (the “what”, not “when” or “how”): costs, funding, etc.
  
• Ends with the hook: selling idea, the “why” or payoff: this part reveals the author’s point of view. What you intend to do after reading the case and it clearly mention your decision.

Order Now - Why Big Data Isn't Enough Case Memo

Order Now - Why Big Data Isn't Enough SWOT & PESTEL Analysis

2. Background of Why Big Data Isn't Enough case study

This paragraph explains why we are talking about this today. It lays out the story. It provides us details from the case story such as -

• Historical perspective on the problem is provided. Details are elaborated that underline the given problem.
• Highlights - what brought us to this moment, why we are in this position, what brought about the need to make this decision.
• Dimensionalize the importance of the problem to the organization and how it is impacting the organization.
• Constraints – Provide a situational analysis based on case study analysis.
• Keep the background section both factual and concise. It is part of the memo where we provide a brief insight into the problem and define the problem.

Checklist

Is the background clear, concise, and easy to follow?
Does it explain why action is needed now?
Does the appropriate sense of urgency come across in the case study?

3. Recommendations for Why Big Data Isn't Enough Case Memo

Recommendations section will provide details regarding what is needed to be done, how it can be done, when to do it and who will do it. It can be elaborated with scenario planning as businesses

• The details of what, when and how. NO 'why'.
• This section should be very specific (100% clear). It must be actionable (How much will it cost, when, how, who). The reader should be able to read this and know how to carry out this recommendation.
• Some cases will require more than one recommendation. It often happens that the firm will require more than one recommendations as there are numerous unknown in the market place.

Checklist

Is the recommendation clear and actionable? Does the firm has capability to implement the recommendations or does it needs to hire fresh talent?

4. Basis for the Recommendations

• Here the reader of the case memo will learn WHY each recommendation is the UNIQUE right thing to do.
• 2-3 solid reasons are typical. The reasons should be backed by clear logic, organization’s vision and mission statements, and robust data analysis.
• Orignal recommendation can be backed by few supporting roadmap to actions. In operations cases the Critical Path Method of PERT can be used to illustrate the point.
• Support includes impact on profit, share, and anything else that can affect long-term business goals of the firm.
• Analysis should address applicable quantitative issues such as NPV, break even analysis, pro forma statement of project budget, sensitivity analysis; as well as qualitative issues, such as, technology consistency, architectural conformance, innovation potential, etc.
• Appeals to precedent and anecdotal evidence in absence of data, but only in limited, carefully constrained manner.
• Shows how the recommendation will put the firm at a competitive advantage or is simply acompetitive necessity.
• The goal is to read the basis and conclude the recommendation.

Checklist

• Is the recommendation an inescapable conclusion of the basis?
• Does the basis for recommendation appropriately consider:
  1. Core competencies and consistency with mission?
  2. External customers and internal clients?
  3. Competitors?
  4. Attractiveness – quantitative measures if applicable (e.g., NPV, ROI, break-even, payback)?
• Are all assumptions explicitly stated (e.g., needs, technology trends)?

5. Discussions

• Outline other alternatives not selected and provide brief reasoning for doing so.
• Discuss risks and key assumptions for Why Big Data Isn't Enough case memo (use full disclosure, reference options grid) of your recommendation.
• When you give a precise number or range, you must support the basis as well.

Checklist

• Is the analysis thorough with key alternatives fairly considered using options grid?
• Risks associated with recommendation for Why Big Data Isn't Enough are properly addressed given the present capabilities and future expectations?

6. Next Steps for Why Big Data Isn't Enough case study memo

• Clearly specify the roadmap of the execution. Provide specific date and action that are required to carry on the next steps.
• Task assignment, objectives, roles and metrics should be mentioned in advance to reduce ambiguity and replication. (what will be done, by whom, and by when)

Checklist

• Clear follow-up/next steps?
• If appropriate, lay out timeline with key milestones to implement recommendation.

7. Exhibits for Why Big Data Isn't Enough case memo

• An Exhibit can be a data chart, map, graph, grid, or simple data table.
• While doing the calculations please mention all the assumptions. The reader won’t able to decipher each of the assumption so make them explicit.
• Exhibits should have Title, sources, footnotes to calculation. The point of the Exhibit should be instantly clear to the reader.
• Exhibits should be cited in the proper order (i.e., do not cite Exhibit 4 first in your Memo and then Exhibit 2).
  

Checklist for Why Big Data Isn't Enough case study memo exhibit

• Is the analysis presented in the case memo - precise, accurate, and data-based?
• Are the exhibits clearly laid out, titled, and referenced in the case study memo?
• Is every assumption mentioned in the case memo is explicitly listed?

NOTE: Every memo may not include every element described above. The specific case will dictate what must be included. For custom case memo please email us or process the order.

You can order Why Big Data Isn't Enough Case Study Recommendation Memo with us at Fern Fort University .