Why Big Data Isn't Enough SWOT Analysis / TOWS Matrix / Weighted SWOT Analysis
Leadership & Managing People
Strategy / MBA Resources
Case Study SWOT Analysis Solution
Case Study Description of Why Big Data Isn't Enough
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?
Swot Analysis of "Why Big Data Isn't Enough" written by Sen Chai, Willy Shih includes – strengths weakness that are internal strategic factors of the organization, and opportunities and threats that Data Scientific facing as an external strategic factors. Some of the topics covered in Why Big Data Isn't Enough case study are - Strategic Management Strategies, and Leadership & Managing People.
Some of the macro environment factors that can be used to understand the Why Big Data Isn't Enough casestudy better are - – increasing transportation and logistics costs, supply chains are disrupted by pandemic , talent flight as more people leaving formal jobs, there is backlash against globalization, increasing energy prices, customer relationship management is fast transforming because of increasing concerns over data privacy, competitive advantages are harder to sustain because of technology dispersion,
digital marketing is dominated by two big players Facebook and Google, there is increasing trade war between United States & China, etc
Introduction to SWOT Analysis of Why Big Data Isn't Enough
SWOT stands for an organization’s Strengths, Weaknesses, Opportunities and Threats . At Oak Spring University , we believe that protagonist in Why Big Data Isn't Enough case study can use SWOT analysis as a strategic management tool to assess the current internal strengths and weaknesses of the Data Scientific, and to figure out the opportunities and threats in the macro environment – technological, environmental, political, economic, social, demographic, etc in which Data Scientific operates in.
According to Harvard Business Review, 75% of the managers use SWOT analysis for various purposes such as – evaluating current scenario, strategic planning, new venture feasibility, personal growth goals, new market entry, Go To market strategies, portfolio management and strategic trade-off assessment, organizational restructuring, etc.
SWOT Objectives / Importance of SWOT Analysis and SWOT Matrix
SWOT analysis of Why Big Data Isn't Enough can be done for the following purposes –
1. Strategic planning using facts provided in Why Big Data Isn't Enough case study
2. Improving business portfolio management of Data Scientific
3. Assessing feasibility of the new initiative in Leadership & Managing People field.
4. Making a Leadership & Managing People topic specific business decision
5. Set goals for the organization
6. Organizational restructuring of Data Scientific
Strengths Why Big Data Isn't Enough | Internal Strategic Factors
What are Strengths in SWOT Analysis / TOWS Matrix / Weighted SWOT Analysis
The strengths of Data Scientific in Why Big Data Isn't Enough Harvard Business Review case study are -
Operational resilience
– The operational resilience strategy in the Why Big Data Isn't Enough Harvard Business Review case study comprises – understanding the underlying the factors in the industry, building diversified operations across different geographies so that disruption in one part of the world doesn’t impact the overall performance of the firm, and integrating the various business operations and processes through its digital transformation drive.
Sustainable margins compare to other players in Leadership & Managing People industry
– Why Big Data Isn't Enough firm has clearly differentiated products in the market place. This has enabled Data Scientific to fetch slight price premium compare to the competitors in the Leadership & Managing People industry. The sustainable margins have also helped Data Scientific to invest into research and development (R&D) and innovation.
Organizational Resilience of Data Scientific
– The covid-19 pandemic has put organizational resilience at the centre of everthing that Data Scientific does. Organizational resilience comprises - Financial Resilience, Operational Resilience, Technological Resilience, Organizational Resilience, Business Model Resilience, and Reputation Resilience.
Analytics focus
– Data Scientific is putting a lot of focus on utilizing the power of analytics in business decision making. This has put it among the leading players in the industry. The technology infrastructure suggested by Sen Chai, Willy Shih can also help it to harness the power of analytics for – marketing optimization, demand forecasting, customer relationship management, inventory management, information sharing across the value chain etc.
Cross disciplinary teams
– Horizontal connected teams at the Data Scientific are driving operational speed, building greater agility, and keeping the organization nimble to compete with new competitors. It helps are organization to ideate new ideas, and execute them swiftly in the marketplace.
Highly skilled collaborators
– Data Scientific has highly efficient outsourcing and offshoring strategy. It has resulted in greater operational flexibility and bringing down the costs in highly price sensitive segment. Secondly the value chain collaborators of the firm in Why Big Data Isn't Enough HBR case study have helped the firm to develop new products and bring them quickly to the marketplace.
Successful track record of launching new products
– Data Scientific has launched numerous new products in last few years, keeping in mind evolving customer preferences and competitive pressures. Data Scientific has effective processes in place that helps in exploring new product needs, doing quick pilot testing, and then launching the products quickly using its extensive distribution network.
Learning organization
- Data Scientific is a learning organization. It has inculcated three key characters of learning organization in its processes and operations – exploration, creativity, and expansiveness. The work place at Data Scientific is open place that encourages instructiveness, ideation, open minded discussions, and creativity. Employees and leaders in Why Big Data Isn't Enough Harvard Business Review case study emphasize – knowledge, initiative, and innovation.
Effective Research and Development (R&D)
– Data Scientific has innovation driven culture where significant part of the revenues are spent on the research and development activities. This has resulted in, as mentioned in case study Why Big Data Isn't Enough - staying ahead in the industry in terms of – new product launches, superior customer experience, highly competitive pricing strategies, and great returns to the shareholders.
High switching costs
– The high switching costs that Data Scientific has built up over years in its products and services combo offer has resulted in high retention of customers, lower marketing costs, and greater ability of the firm to focus on its customers.
Diverse revenue streams
– Data Scientific is present in almost all the verticals within the industry. This has provided firm in Why Big Data Isn't Enough case study a diverse revenue stream that has helped it to survive disruptions such as global pandemic in Covid-19, financial disruption of 2008, and supply chain disruption of 2021.
Superior customer experience
– The customer experience strategy of Data Scientific in the segment is based on four key concepts – personalization, simplification of complex needs, prompt response, and continuous engagement.
Weaknesses Why Big Data Isn't Enough | Internal Strategic Factors
What are Weaknesses in SWOT Analysis / TOWS Matrix / Weighted SWOT Analysis
The weaknesses of Why Big Data Isn't Enough are -
Capital Spending Reduction
– Even during the low interest decade, Data Scientific has not been able to do capital spending to the tune of the competition. This has resulted into fewer innovations and company facing stiff competition from both existing competitors and new entrants who are disrupting the industry using digital technology.
High cash cycle compare to competitors
Data Scientific has a high cash cycle compare to other players in the industry. It needs to shorten the cash cycle by 12% to be more competitive in the marketplace, reduce inventory costs, and be more profitable.
High dependence on star products
– The top 2 products and services of the firm as mentioned in the Why Big Data Isn't Enough HBR case study still accounts for major business revenue. This dependence on star products in has resulted into insufficient focus on developing new products, even though Data Scientific has relatively successful track record of launching new products.
Skills based hiring
– The stress on hiring functional specialists at Data Scientific has created an environment where the organization is dominated by functional specialists rather than management generalist. This has resulted into product oriented approach rather than marketing oriented approach or consumers oriented approach.
Ability to respond to the competition
– As the decision making is very deliberative, highlighted in the case study Why Big Data Isn't Enough, in the dynamic environment Data Scientific has struggled to respond to the nimble upstart competition. Data Scientific has reasonably good record with similar level competitors but it has struggled with new entrants taking away niches of its business.
Low market penetration in new markets
– Outside its home market of Data Scientific, firm in the HBR case study Why Big Data Isn't Enough needs to spend more promotional, marketing, and advertising efforts to penetrate international markets.
Lack of clear differentiation of Data Scientific products
– To increase the profitability and margins on the products, Data Scientific needs to provide more differentiated products than what it is currently offering in the marketplace.
Interest costs
– Compare to the competition, Data Scientific has borrowed money from the capital market at higher rates. It needs to restructure the interest payment and costs so that it can compete better and improve profitability.
Products dominated business model
– Even though Data Scientific has some of the most successful products in the industry, this business model has made each new product launch extremely critical for continuous financial growth of the organization. firm in the HBR case study - Why Big Data Isn't Enough should strive to include more intangible value offerings along with its core products and services.
Increasing silos among functional specialists
– The organizational structure of Data Scientific is dominated by functional specialists. It is not different from other players in the Leadership & Managing People segment. Data Scientific needs to de-silo the office environment to harness the true potential of its workforce. Secondly the de-silo will also help Data Scientific to focus more on services rather than just following the product oriented approach.
High operating costs
– Compare to the competitors, firm in the HBR case study Why Big Data Isn't Enough has high operating costs in the. This can be harder to sustain given the new emerging competition from nimble players who are using technology to attract Data Scientific 's lucrative customers.
Opportunities Why Big Data Isn't Enough | External Strategic Factors
What are Opportunities in the SWOT Analysis / TOWS Matrix / Weighted SWOT Analysis
The opportunities highlighted in the Harvard Business Review case study Why Big Data Isn't Enough are -
Buying journey improvements
– Data Scientific can improve the customer journey of consumers in the industry by using analytics and artificial intelligence. Why Big Data Isn't Enough suggest that firm can provide automated chats to help consumers solve their own problems, provide online suggestions to get maximum out of the products and services, and help consumers to build a community where they can interact with each other to develop new features and uses.
Learning at scale
– Online learning technologies has now opened space for Data Scientific to conduct training and development for its employees across the world. This will result in not only reducing the cost of training but also help employees in different part of the world to integrate with the headquarter work culture, ethos, and standards.
Increase in government spending
– As the United States and other governments are increasing social spending and infrastructure spending to build economies post Covid-19, Data Scientific can use these opportunities to build new business models that can help the communities that Data Scientific operates in. Secondly it can use opportunities from government spending in Leadership & Managing People sector.
Harnessing reconfiguration of the global supply chains
– As the trade war between US and China heats up in the coming years, Data Scientific can build a diversified supply chain model across various countries in - South East Asia, India, and other parts of the world. This reconfiguration of global supply chain can help, as suggested in case study, Why Big Data Isn't Enough, to buy more products closer to the markets, and it can leverage its size and influence to get better deal from the local markets.
Reconfiguring business model
– The expansion of digital payment system, the bringing down of international transactions costs using Bitcoin and other blockchain based currencies, etc can help Data Scientific to reconfigure its entire business model. For example it can used blockchain based technologies to reduce piracy of its products in the big markets such as China. Secondly it can use the popularity of e-commerce in various developing markets to build a Direct to Customer business model rather than the current Channel Heavy distribution network.
Redefining models of collaboration and team work
– As explained in the weaknesses section, Data Scientific is facing challenges because of the dominance of functional experts in the organization. Why Big Data Isn't Enough case study suggests that firm can utilize new technology to build more coordinated teams and streamline operations and communications using tools such as CAD, Zoom, etc.
Loyalty marketing
– Data Scientific has focused on building a highly responsive customer relationship management platform. This platform is built on in-house data and driven by analytics and artificial intelligence. The customer analytics can help the organization to fine tune its loyalty marketing efforts, increase the wallet share of the organization, reduce wastage on mainstream advertising spending, build better pricing strategies using personalization, etc.
Developing new processes and practices
– Data Scientific can develop new processes and procedures in Leadership & Managing People industry using technology such as automation using artificial intelligence, real time transportation and products tracking, 3D modeling for concept development and new products pilot testing etc.
Creating value in data economy
– The success of analytics program of Data Scientific has opened avenues for new revenue streams for the organization in the industry. This can help Data Scientific to build a more holistic ecosystem as suggested in the Why Big Data Isn't Enough case study. Data Scientific can build new products and services such as - data insight services, data privacy related products, data based consulting services, etc.
Remote work and new talent hiring opportunities
– The widespread usage of remote working technologies during Covid-19 has opened opportunities for Data Scientific to expand its talent hiring zone. According to McKinsey Global Institute, 20% of the high end workforce in fields such as finance, information technology, can continously work from remote local post Covid-19. This presents a really great opportunity for Data Scientific to hire the very best people irrespective of their geographical location.
Changes in consumer behavior post Covid-19
– Consumer behavior has changed in the Leadership & Managing People industry because of Covid-19 restrictions. Some of this behavior will stay once things get back to normal. Data Scientific can take advantage of these changes in consumer behavior to build a far more efficient business model. For example consumer regular ordering of products can reduce both last mile delivery costs and market penetration costs. Data Scientific can further use this consumer data to build better customer loyalty, provide better products and service collection, and improve the value proposition in inflationary times.
Better consumer reach
– The expansion of the 5G network will help Data Scientific to increase its market reach. Data Scientific will be able to reach out to new customers. Secondly 5G will also provide technology framework to build new tools and products that can help more immersive consumer experience and faster consumer journey.
Using analytics as competitive advantage
– Data Scientific has spent a significant amount of money and effort to integrate analytics and machine learning into its operations in the sector. This continuous investment in analytics has enabled, as illustrated in the Harvard case study Why Big Data Isn't Enough - to build a competitive advantage using analytics. The analytics driven competitive advantage can help Data Scientific to build faster Go To Market strategies, better consumer insights, developing relevant product features, and building a highly efficient supply chain.
Threats Why Big Data Isn't Enough External Strategic Factors
What are Threats in the SWOT Analysis / TOWS Matrix / Weighted SWOT Analysis
The threats mentioned in the HBR case study Why Big Data Isn't Enough are -
Regulatory challenges
– Data Scientific needs to prepare for regulatory challenges as consumer protection groups and other pressure groups are vigorously advocating for more regulations on big business - to reduce inequality, to create a level playing field, to product data privacy and consumer privacy, to reduce the influence of big money on democratic institutions, etc. This can lead to significant changes in the Leadership & Managing People industry regulations.
High level of anxiety and lack of motivation
– the Great Resignation in United States is the sign of broader dissatisfaction among the workforce in United States. Data Scientific needs to understand the core reasons impacting the Leadership & Managing People industry. This will help it in building a better workplace.
Barriers of entry lowering
– As technology is more democratized, the barriers to entry in the industry are lowering. It can presents Data Scientific with greater competitive threats in the near to medium future. Secondly it will also put downward pressure on pricing throughout the sector.
High dependence on third party suppliers
– Data Scientific high dependence on third party suppliers can disrupt its processes and delivery mechanism. For example -the current troubles of car makers because of chip shortage is because the chip companies started producing chips for electronic companies rather than car manufacturers.
Backlash against dominant players
– US Congress and other legislative arms of the government are getting tough on big business especially technology companies. The digital arm of Data Scientific business can come under increasing regulations regarding data privacy, data security, etc.
Easy access to finance
– Easy access to finance in Leadership & Managing People field will also reduce the barriers to entry in the industry, thus putting downward pressure on the prices because of increasing competition. Data Scientific can utilize it by borrowing at lower rates and invest it into research and development, capital expenditure to fortify its core competitive advantage.
Shortening product life cycle
– it is one of the major threat that Data Scientific is facing in Leadership & Managing People sector. It can lead to higher research and development costs, higher marketing expenses, lower customer loyalty, etc.
Trade war between China and United States
– The trade war between two of the biggest economies can hugely impact the opportunities for Data Scientific in the Leadership & Managing People industry. The Leadership & Managing People industry is already at various protected from local competition in China, with the rise of trade war the protection levels may go up. This presents a clear threat of current business model in Chinese market.
Consumer confidence and its impact on Data Scientific demand
– There is a high probability of declining consumer confidence, given – high inflammation rate, rise of gig economy, lower job stability, increasing cost of living, higher interest rates, and aging demography. All the factors contribute to people saving higher rate of their income, resulting in lower consumer demand in the industry and other sectors.
Stagnating economy with rate increase
– Data Scientific can face lack of demand in the market place because of Fed actions to reduce inflation. This can lead to sluggish growth in the economy, lower demands, lower investments, higher borrowing costs, and consolidation in the field.
Technology disruption because of hacks, piracy etc
– The colonial pipeline illustrated, how vulnerable modern organization are to international hackers, miscreants, and disruptors. The cyber security interruption, data leaks, etc can seriously jeopardize the future growth of the organization.
Technology acceleration in Forth Industrial Revolution
– Data Scientific has witnessed rapid integration of technology during Covid-19 in the Leadership & Managing People industry. As one of the leading players in the industry, Data Scientific needs to keep up with the evolution of technology in the Leadership & Managing People sector. According to Mckinsey study top managers believe that the adoption of technology in operations, communications is 20-25 times faster than what they planned in the beginning of 2019.
Increasing wage structure of Data Scientific
– Post Covid-19 there is a sharp increase in the wages especially in the jobs that require interaction with people. The increasing wages can put downward pressure on the margins of Data Scientific.
Weighted SWOT Analysis of Why Big Data Isn't Enough Template, Example
Not all factors mentioned under the Strengths, Weakness, Opportunities, and Threats quadrants in the SWOT Analysis are equal. Managers in the HBR case study Why Big Data Isn't Enough needs to zero down on the relative importance of each factor mentioned in the Strengths, Weakness, Opportunities, and Threats quadrants.
We can provide the relative importance to each factor by assigning relative weights. Weighted SWOT analysis process is a three stage process –
First stage for doing weighted SWOT analysis of the case study Why Big Data Isn't Enough is to rank the strengths and weaknesses of the organization. This will help you to assess the most important strengths and weaknesses of the firm and which one of the strengths and weaknesses mentioned in the initial lists are marginal and can be left out.
Second stage for conducting weighted SWOT analysis of the Harvard case study Why Big Data Isn't Enough is to give probabilities to the external strategic factors thus better understanding the opportunities and threats arising out of macro environment changes and developments.
Third stage of constructing weighted SWOT analysis of Why Big Data Isn't Enough is to provide strategic recommendations includes – joining likelihood of external strategic factors such as opportunities and threats to the internal strategic factors – strengths and weaknesses. You should start with external factors as they will provide the direction of the overall industry. Secondly by joining probabilities with internal strategic factors can help the company not only strategic fit but also the most probably strategic trade-off that Data Scientific needs to make to build a sustainable competitive advantage.
