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 commodity prices, increasing energy prices, supply chains are disrupted by pandemic , there is increasing trade war between United States & China, increasing government debt because of Covid-19 spendings, challanges to central banks by blockchain based private currencies, competitive advantages are harder to sustain because of technology dispersion,
geopolitical disruptions, increasing transportation and logistics costs, 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 -
Digital Transformation in Leadership & Managing People segment
- digital transformation varies from industry to industry. For Data Scientific digital transformation journey comprises differing goals based on market maturity, customer technology acceptance, and organizational culture. Data Scientific has successfully integrated the four key components of digital transformation – digital integration in processes, digital integration in marketing and customer relationship management, digital integration into the value chain, and using technology to explore new products and market opportunities.
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.
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.
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.
Training and development
– Data Scientific has one of the best training and development program in the industry. The effectiveness of the training programs can be measured in Why Big Data Isn't Enough Harvard Business Review case study by analyzing – employees retention, in-house promotion, loyalty, new venture initiation, lack of conflict, and high level of both employees and customer engagement.
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.
Innovation driven organization
– Data Scientific is one of the most innovative firm in sector. Manager in Why Big Data Isn't Enough Harvard Business Review case study can use Clayton Christensen Disruptive Innovation strategies to further increase the scale of innovtions in the organization.
Ability to recruit top talent
– Data Scientific is one of the leading recruiters in the industry. Managers in the Why Big Data Isn't Enough are in a position to attract the best talent available. The firm has a robust talent identification program that helps in identifying the brightest.
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.
Strong track record of project management
– Data Scientific is known for sticking to its project targets. This enables the firm to manage – time, project costs, and have sustainable margins on the projects.
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.
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.
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.
Aligning sales with marketing
– It come across in the case study Why Big Data Isn't Enough that the firm needs to have more collaboration between its sales team and marketing team. Sales professionals in the industry have deep experience in developing customer relationships. Marketing department in the case Why Big Data Isn't Enough can leverage the sales team experience to cultivate customer relationships as Data Scientific is planning to shift buying processes online.
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.
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.
No frontier risks strategy
– After analyzing the HBR case study Why Big Data Isn't Enough, it seems that company is thinking about the frontier risks that can impact Leadership & Managing People strategy. But it has very little resources allocation to manage the risks emerging from events such as natural disasters, climate change, melting of permafrost, tacking the rise of artificial intelligence, opportunities and threats emerging from commercialization of space etc.
Workers concerns about automation
– As automation is fast increasing in the segment, Data Scientific needs to come up with a strategy to reduce the workers concern regarding automation. Without a clear strategy, it could lead to disruption and uncertainty within the organization.
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.
Slow decision making process
– As mentioned earlier in the report, Data Scientific has a very deliberative decision making approach. This approach has resulted in prudent decisions, but it has also resulted in missing opportunities in the industry over the last five years. Data Scientific even though has strong showing on digital transformation primary two stages, it has struggled to capitalize the power of digital transformation in marketing efforts and new venture efforts.
High dependence on existing supply chain
– The disruption in the global supply chains because of the Covid-19 pandemic and blockage of the Suez Canal illustrated the fragile nature of Data Scientific supply chain. Even after few cautionary changes mentioned in the HBR case study - Why Big Data Isn't Enough, it is still heavily dependent upon the existing supply chain. The existing supply chain though brings in cost efficiencies but it has left Data Scientific vulnerable to further global disruptions in South East Asia.
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 -
Finding new ways to collaborate
– Covid-19 has not only transformed business models of companies in Leadership & Managing People industry, but it has also influenced the consumer preferences. Data Scientific can tie-up with other value chain partners to explore new opportunities regarding meeting customer demands and building a rewarding and engaging relationship.
Use of Bitcoin and other crypto currencies for transactions
– The popularity of Bitcoin and other crypto currencies as asset class and medium of transaction has opened new opportunities for Data Scientific in the consumer business. Now Data Scientific can target international markets with far fewer capital restrictions requirements than the existing system.
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.
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.
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.
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.
Low interest rates
– Even though inflation is raising its head in most developed economies, Data Scientific can still utilize the low interest rates to borrow money for capital investment. Secondly it can also use the increase of government spending in infrastructure projects to get new business.
Lowering marketing communication costs
– 5G expansion will open new opportunities for Data Scientific in the field of marketing communication. It will bring down the cost of doing business, provide technology platform to build new products in the Leadership & Managing People segment, and it will provide faster access to the consumers.
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.
Leveraging digital technologies
– Data Scientific can leverage digital technologies such as artificial intelligence and machine learning to automate the production process, customer analytics to get better insights into consumer behavior, realtime digital dashboards to get better sales tracking, logistics and transportation, product tracking, etc.
Identify volunteer opportunities
– Covid-19 has impacted working population in two ways – it has led to people soul searching about their professional choices, resulting in mass resignation. Secondly it has encouraged people to do things that they are passionate about. This has opened opportunities for businesses to build volunteer oriented socially driven projects. Data Scientific can explore opportunities that can attract volunteers and are consistent with its mission and vision.
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.
Manufacturing automation
– Data Scientific can use the latest technology developments to improve its manufacturing and designing process in Leadership & Managing People segment. It can use CAD and 3D printing to build a quick prototype and pilot testing products. It can leverage automation using machine learning and artificial intelligence to do faster production at lowers costs, and it can leverage the growth in satellite and tracking technologies to improve inventory management, transportation, and shipping.
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 -
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.
Aging population
– As the populations of most advanced economies are aging, it will lead to high social security costs, higher savings among population, and lower demand for goods and services in the economy. The household savings in US, France, UK, Germany, and Japan are growing faster than predicted because of uncertainty caused by pandemic.
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.
Learning curve for new practices
– As the technology based on artificial intelligence and machine learning platform is getting complex, as highlighted in case study Why Big Data Isn't Enough, Data Scientific may face longer learning curve for training and development of existing employees. This can open space for more nimble competitors in the field of Leadership & Managing People .
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.
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.
Increasing international competition and downward pressure on margins
– Apart from technology driven competitive advantage dilution, Data Scientific can face downward pressure on margins from increasing competition from international players. The international players have stable revenue in their home market and can use those resources to penetrate prominent markets illustrated in HBR case study Why Big Data Isn't Enough .
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.
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.
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.
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.
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.
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.
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.