Why Big Data Isn't Enough

Why Big Data Isn't Enough Description

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?

Case Description Why Big Data Isn't Enough

Strategic Managment Tools Used in Case Study Analysis of Why Big Data Isn't Enough

STEP 1. Problem Identification in Why Big Data Isn't Enough case study

STEP 2. External Environment Analysis - PESTEL / PEST / STEP Analysis of Why Big Data Isn't Enough case study

STEP 3. Industry Specific / Porter Five Forces Analysis of Why Big Data Isn't Enough case study

STEP 4. Evaluating Alternatives / SWOT Analysis of Why Big Data Isn't Enough case study

STEP 5. Porter Value Chain Analysis / VRIO / VRIN Analysis Why Big Data Isn't Enough case study

STEP 6. Recommendations Why Big Data Isn't Enough case study

STEP 7. Basis of Recommendations for Why Big Data Isn't Enough case study

Case Analysis of Why Big Data Isn't Enough

Why Big Data Isn't Enough is a Harvard Business (HBR) Case Study on Leadership & Managing People , Texas Business School provides HBR case study assignment help for just $9. Texas Business School(TBS) case study solution is based on HBR Case Study Method framework, TBS expertise & global insights. Why Big Data Isn't Enough is designed and drafted in a manner to allow the HBR case study reader to analyze a real-world problem by putting reader into the position of the decision maker. Why Big Data Isn't Enough case study will help professionals, MBA, EMBA, and leaders to develop a broad and clear understanding of casecategory challenges. Why Big Data Isn't Enough will also provide insight into areas such as – wordlist , strategy, leadership, sales and marketing, and negotiations.

Case Study Solutions Background Work

Why Big Data Isn't Enough case study solution is focused on solving the strategic and operational challenges the protagonist of the case is facing. The challenges involve – evaluation of strategic options, key role of Leadership & Managing People, leadership qualities of the protagonist, and dynamics of the external environment. The challenge in front of the protagonist, of Why Big Data Isn't Enough, is to not only build a competitive position of the organization but also to sustain it over a period of time.

Strategic Management Tools Used in Case Study Solution

The Why Big Data Isn't Enough case study solution requires the MBA, EMBA, executive, professional to have a deep understanding of various strategic management tools such as SWOT Analysis, PESTEL Analysis / PEST Analysis / STEP Analysis, Porter Five Forces Analysis, Go To Market Strategy, BCG Matrix Analysis, Porter Value Chain Analysis, Ansoff Matrix Analysis, VRIO / VRIN and Marketing Mix Analysis.

Texas Business School Approach to Leadership & Managing People Solutions

In the Texas Business School, Why Big Data Isn't Enough case study solution – following strategic tools are used - SWOT Analysis, PESTEL Analysis / PEST Analysis / STEP Analysis, Porter Five Forces Analysis, Go To Market Strategy, BCG Matrix Analysis, Porter Value Chain Analysis, Ansoff Matrix Analysis, VRIO / VRIN and Marketing Mix Analysis.

We have additionally used the concept of supply chain management and leadership framework to build a comprehensive case study solution for the case – Why Big Data Isn't Enough

Step 1 – Problem Identification of Why Big Data Isn't Enough - Harvard Business School Case Study

The first step to solve HBR Why Big Data Isn't Enough case study solution is to identify the problem present in the case. The problem statement of the case is provided in the beginning of the case where the protagonist is contemplating various options in the face of numerous challenges that Data Scientific is facing right now. Even though the problem statement is essentially – “Leadership & Managing People” challenge but it has impacted by others factors such as communication in the organization, uncertainty in the external environment, leadership in Data Scientific, style of leadership and organization structure, marketing and sales, organizational behavior, strategy, internal politics, stakeholders priorities and more.

Step 2 – External Environment Analysis

Texas Business School approach of case study analysis – Conclusion, Reasons, Evidences - provides a framework to analyze every HBR case study. It requires conducting robust external environmental analysis to decipher evidences for the reasons presented in the Why Big Data Isn't Enough.

The external environment analysis of Why Big Data Isn't Enough will ensure that we are keeping a tab on the macro-environment factors that are directly and indirectly impacting the business of the firm.

What is PESTEL Analysis? Briefly Explained

PESTEL stands for political, economic, social, technological, environmental and legal factors that impact the external environment of firm in Why Big Data Isn't Enough case study. PESTEL analysis of " Why Big Data Isn't Enough" can help us understand why the organization is performing badly, what are the factors in the external environment that are impacting the performance of the organization, and how the organization can either manage or mitigate the impact of these external factors.

How to do PESTEL / PEST / STEP Analysis? What are the components of PESTEL Analysis?

As mentioned above PESTEL Analysis has six elements – political, economic, social, technological, environmental, and legal. All the six elements are explained in context with Why Big Data Isn't Enough macro-environment and how it impacts the businesses of the firm.

How to do PESTEL Analysis for Why Big Data Isn't Enough

To do comprehensive PESTEL analysis of case study – Why Big Data Isn't Enough , we have researched numerous components under the six factors of PESTEL analysis.

Political Factors that Impact Why Big Data Isn't Enough

Political factors impact seven key decision making areas – economic environment, socio-cultural environment, rate of innovation & investment in research & development, environmental laws, legal requirements, and acceptance of new technologies.

Policy Making Impact on Why Big Data Isn't Enough

Government policies have significant impact on the business environment of any country. The firm in “ Why Big Data Isn't Enough ” needs to navigate these policy decisions to create either an edge for itself or reduce the negative impact of the policy as far as possible.

Data safety laws – The countries in which Data Scientific is operating, firms are required to store customer data within the premises of the country. Data Scientific needs to restructure its IT policies to accommodate these changes. In the EU countries, firms are required to make special provision for privacy issues and other laws.

Competition Regulations – Numerous countries have strong competition laws both regarding the monopoly conditions and day to day fair business practices. Why Big Data Isn't Enough has numerous instances where the competition regulations aspects can be scrutinized.

Import restrictions on products – Before entering the new market, Data Scientific in case study Why Big Data Isn't Enough" should look into the import restrictions that may be present in the prospective market.

Export restrictions on products – Apart from direct product export restrictions in field of technology and agriculture, a number of countries also have capital controls. Data Scientific in case study “ Why Big Data Isn't Enough ” should look into these export restrictions policies.

Foreign Direct Investment Policies – Government policies favors local companies over international policies, Data Scientific in case study “ Why Big Data Isn't Enough ” should understand in minute details regarding the Foreign Direct Investment policies of the prospective market.

Taxation & Regulation Impact on Why Big Data Isn't Enough

Corporate Taxes – The rate of taxes is often used by governments to lure foreign direct investments or increase domestic investment in a certain sector. Corporate taxation can be divided into two categories – taxes on profits and taxes on operations. Taxes on profits number is important for companies that already have a sustainable business model, while taxes on operations is far more significant for companies that are looking to set up new plants or operations.

Tariffs – Chekout how much tariffs the firm needs to pay in the “ Why Big Data Isn't Enough ” case study. The level of tariffs will determine the viability of the business model that the firm is contemplating. If the tariffs are high then it will be extremely difficult to compete with the local competitors. But if the tariffs are between 5-10% then Data Scientific can compete against other competitors.

Government Scheme & Subsidies Impact on Why Big Data Isn't Enough

Research and Development Subsidies and Policies – Governments often provide tax breaks and other incentives for companies to innovate in various sectors of priority. Managers at Why Big Data Isn't Enough case study have to assess whether their business can benefit from such government assistance and subsidies.

Consumer protection – Different countries have different consumer protection laws. Managers need to clarify not only the consumer protection laws in advance but also legal implications if the firm fails to meet any of them.

Political System & Stability, and its Impact on Why Big Data Isn't Enough

Political System and Its Implications – Different political systems have different approach to free market and entrepreneurship. Managers need to assess these factors even before entering the market.

Freedom of Press is critical for fair trade and transparency. Countries where freedom of press is not prevalent there are high chances of both political and commercial corruption.

Corruption level – Data Scientific needs to assess the level of corruptions both at the official level and at the market level, even before entering a new market. To tackle the menace of corruption – a firm should have a clear SOP that provides managers at each level what to do when they encounter instances of either systematic corruption or bureaucrats looking to take bribes from the firm.

Independence of judiciary – It is critical for fair business practices. If a country doesn’t have independent judiciary then there is no point entry into such a country for business.

Government attitude towards trade unions – Different political systems and government have different attitude towards trade unions and collective bargaining. The firm needs to assess – its comfort dealing with the unions and regulations regarding unions in a given market or industry. If both are on the same page then it makes sense to enter, otherwise it doesn’t.

Economic Factors that Impact Why Big Data Isn't Enough

Social Factors that Impact Why Big Data Isn't Enough

Technological Factors that Impact Why Big Data Isn't Enough

Environmental Factors that Impact Why Big Data Isn't Enough

Legal Factors that Impact Why Big Data Isn't Enough

Step 3 – Industry Specific Analysis

What is Porter Five Forces Analysis

PESTEL stands for political, economic, social, technological, environmental and legal factors that impact the external environment of firm in Why Big Data Isn't Enough case study. PESTEL analysis of " Why Big Data Isn't Enough" can help us understand why the organization is performing badly, what are the factors in the external environment that are impacting the performance of the organization, and how the organization can either manage or mitigate the impact of these external factors.

Step 4 – SWOT Analysis / Internal Environment Analysis

Step 5 – Porter Value Chain / VRIO / VRIN Analysis

Step 6 – Evaluating Alternatives & Recommendations

Step 7 – Basis for Recommendations

References :: Why Big Data Isn't Enough case study solution