Using Simulated Experience to Make Sense of Big Data SWOT Analysis / TOWS Matrix / Weighted SWOT Analysis
Technology & Operations
Strategy / MBA Resources
Case Study SWOT Analysis Solution
Case Study Description of Using Simulated Experience to Make Sense of Big Data
In an increasingly complex economic and social environment, access to vast amounts of data and information can help organizations and governments make better policies, predictions and decisions. Indeed, more and more decision makers rely on statistical findings and data-based decision models when tackling problems and forming strategies. So far, discussions of data-based decision making have centered mainly on analysis: data collection, technological infrastructures and statistical methods. Yet another vital issue receives far less scrutiny: how analytical results are communicated to decision makers. Data science, like medical diagnostics or scientific research, lies in the hands of expert analysts who must explain their findings to executive decision makers who are often less knowledgeable about formal, statistical reasoning. Yet many behavioral experiments have shown that when the same statistical information is conveyed in different ways, people make drastically different decisions. Description, the authors note, is the default mode of presenting statistical information. This typically involves a verbal statement or a written report, which might feature one or more tables summarizing the findings. But the authors'own research suggests that descriptions can mislead even the most knowledgeable decision makers. In a recent experiment, they asked 257 economics scholars to make judgments and predictions based on a simple regression analysis. To the authors'surprise, most of these experts had a hard time accurately deciphering and acting on the results of the kind of analysis they themselves frequently conduct. In particular, the authors found that their description of the findings, which mimicked the industry standard, led to an illusion of predictability -- an erroneous belief that the analyzed outcomes were more predictable than they actually were. The authors argue that simulated experience enables intuitive interpretation of statistical information, thereby communicating analytical results even to decision makers who are not knowledgeable about statistics. This is an MIT Sloan Management Review article.
Swot Analysis of "Using Simulated Experience to Make Sense of Big Data" written by Robin M. Hogarth, Emre Soyer includes – strengths weakness that are internal strategic factors of the organization, and opportunities and threats that Statistical Makers facing as an external strategic factors. Some of the topics covered in Using Simulated Experience to Make Sense of Big Data case study are - Strategic Management Strategies, and Technology & Operations.
Some of the macro environment factors that can be used to understand the Using Simulated Experience to Make Sense of Big Data casestudy better are - – increasing government debt because of Covid-19 spendings, geopolitical disruptions, increasing commodity prices, increasing energy prices, competitive advantages are harder to sustain because of technology dispersion, wage bills are increasing, customer relationship management is fast transforming because of increasing concerns over data privacy,
supply chains are disrupted by pandemic , banking and financial system is disrupted by Bitcoin and other crypto currencies, etc
Introduction to SWOT Analysis of Using Simulated Experience to Make Sense of Big Data
SWOT stands for an organization’s Strengths, Weaknesses, Opportunities and Threats . At Oak Spring University , we believe that protagonist in Using Simulated Experience to Make Sense of Big Data case study can use SWOT analysis as a strategic management tool to assess the current internal strengths and weaknesses of the Statistical Makers, and to figure out the opportunities and threats in the macro environment – technological, environmental, political, economic, social, demographic, etc in which Statistical Makers 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 Using Simulated Experience to Make Sense of Big Data can be done for the following purposes –
1. Strategic planning using facts provided in Using Simulated Experience to Make Sense of Big Data case study
2. Improving business portfolio management of Statistical Makers
3. Assessing feasibility of the new initiative in Technology & Operations field.
4. Making a Technology & Operations topic specific business decision
5. Set goals for the organization
6. Organizational restructuring of Statistical Makers
Strengths Using Simulated Experience to Make Sense of Big Data | Internal Strategic Factors
What are Strengths in SWOT Analysis / TOWS Matrix / Weighted SWOT Analysis
The strengths of Statistical Makers in Using Simulated Experience to Make Sense of Big Data Harvard Business Review case study are -
Operational resilience
– The operational resilience strategy in the Using Simulated Experience to Make Sense of Big Data 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.
Innovation driven organization
– Statistical Makers is one of the most innovative firm in sector. Manager in Using Simulated Experience to Make Sense of Big Data Harvard Business Review case study can use Clayton Christensen Disruptive Innovation strategies to further increase the scale of innovtions in the organization.
High brand equity
– Statistical Makers has strong brand awareness and brand recognition among both - the exiting customers and potential new customers. Strong brand equity has enabled Statistical Makers to keep acquiring new customers and building profitable relationship with both the new and loyal customers.
Highly skilled collaborators
– Statistical Makers 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 Using Simulated Experience to Make Sense of Big Data HBR case study have helped the firm to develop new products and bring them quickly to the marketplace.
Diverse revenue streams
– Statistical Makers is present in almost all the verticals within the industry. This has provided firm in Using Simulated Experience to Make Sense of Big Data 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.
Ability to recruit top talent
– Statistical Makers is one of the leading recruiters in the industry. Managers in the Using Simulated Experience to Make Sense of Big Data 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 Statistical Makers 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.
Low bargaining power of suppliers
– Suppliers of Statistical Makers in the sector have low bargaining power. Using Simulated Experience to Make Sense of Big Data has further diversified its suppliers portfolio by building a robust supply chain across various countries. This helps Statistical Makers to manage not only supply disruptions but also source products at highly competitive prices.
Learning organization
- Statistical Makers 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 Statistical Makers is open place that encourages instructiveness, ideation, open minded discussions, and creativity. Employees and leaders in Using Simulated Experience to Make Sense of Big Data Harvard Business Review case study emphasize – knowledge, initiative, and innovation.
Effective Research and Development (R&D)
– Statistical Makers 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 Using Simulated Experience to Make Sense of Big Data - staying ahead in the industry in terms of – new product launches, superior customer experience, highly competitive pricing strategies, and great returns to the shareholders.
Digital Transformation in Technology & Operations segment
- digital transformation varies from industry to industry. For Statistical Makers digital transformation journey comprises differing goals based on market maturity, customer technology acceptance, and organizational culture. Statistical Makers 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.
Training and development
– Statistical Makers has one of the best training and development program in the industry. The effectiveness of the training programs can be measured in Using Simulated Experience to Make Sense of Big Data 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.
Weaknesses Using Simulated Experience to Make Sense of Big Data | Internal Strategic Factors
What are Weaknesses in SWOT Analysis / TOWS Matrix / Weighted SWOT Analysis
The weaknesses of Using Simulated Experience to Make Sense of Big Data are -
Products dominated business model
– Even though Statistical Makers 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 - Using Simulated Experience to Make Sense of Big Data should strive to include more intangible value offerings along with its core products and services.
Lack of clear differentiation of Statistical Makers products
– To increase the profitability and margins on the products, Statistical Makers needs to provide more differentiated products than what it is currently offering in the marketplace.
Compensation and incentives
– The revenue per employee as mentioned in the HBR case study Using Simulated Experience to Make Sense of Big Data, is just above the industry average. Statistical Makers needs to redesign the compensation structure and incentives to increase the revenue per employees. Some of the steps that it can take are – hiring more specialists on project basis, etc.
High cash cycle compare to competitors
Statistical Makers 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.
Employees’ incomplete understanding of strategy
– From the instances in the HBR case study Using Simulated Experience to Make Sense of Big Data, it seems that the employees of Statistical Makers don’t have comprehensive understanding of the firm’s strategy. This is reflected in number of promotional campaigns over the last few years that had mixed messaging and competing priorities. Some of the strategic activities and services promoted in the promotional campaigns were not consistent with the organization’s strategy.
Slow to harness new channels of communication
– Even though competitors are using new communication channels such as Instagram, Tiktok, and Snap, Statistical Makers is slow explore the new channels of communication. These new channels of communication mentioned in marketing section of case study Using Simulated Experience to Make Sense of Big Data can help to provide better information regarding products and services. It can also build an online community to further reach out to potential customers.
Interest costs
– Compare to the competition, Statistical Makers 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.
Workers concerns about automation
– As automation is fast increasing in the segment, Statistical Makers 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.
Slow decision making process
– As mentioned earlier in the report, Statistical Makers 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. Statistical Makers 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.
Increasing silos among functional specialists
– The organizational structure of Statistical Makers is dominated by functional specialists. It is not different from other players in the Technology & Operations segment. Statistical Makers needs to de-silo the office environment to harness the true potential of its workforce. Secondly the de-silo will also help Statistical Makers to focus more on services rather than just following the product oriented approach.
Ability to respond to the competition
– As the decision making is very deliberative, highlighted in the case study Using Simulated Experience to Make Sense of Big Data, in the dynamic environment Statistical Makers has struggled to respond to the nimble upstart competition. Statistical Makers has reasonably good record with similar level competitors but it has struggled with new entrants taking away niches of its business.
Opportunities Using Simulated Experience to Make Sense of Big Data | 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 Using Simulated Experience to Make Sense of Big Data are -
Learning at scale
– Online learning technologies has now opened space for Statistical Makers 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.
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. Statistical Makers can explore opportunities that can attract volunteers and are consistent with its mission and vision.
Low interest rates
– Even though inflation is raising its head in most developed economies, Statistical Makers 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.
Loyalty marketing
– Statistical Makers 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.
Better consumer reach
– The expansion of the 5G network will help Statistical Makers to increase its market reach. Statistical Makers 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
– Statistical Makers 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 Using Simulated Experience to Make Sense of Big Data - to build a competitive advantage using analytics. The analytics driven competitive advantage can help Statistical Makers to build faster Go To Market strategies, better consumer insights, developing relevant product features, and building a highly efficient supply chain.
Finding new ways to collaborate
– Covid-19 has not only transformed business models of companies in Technology & Operations industry, but it has also influenced the consumer preferences. Statistical Makers can tie-up with other value chain partners to explore new opportunities regarding meeting customer demands and building a rewarding and engaging relationship.
Harnessing reconfiguration of the global supply chains
– As the trade war between US and China heats up in the coming years, Statistical Makers 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, Using Simulated Experience to Make Sense of Big Data, to buy more products closer to the markets, and it can leverage its size and influence to get better deal from the local markets.
Increase in government spending
– As the United States and other governments are increasing social spending and infrastructure spending to build economies post Covid-19, Statistical Makers can use these opportunities to build new business models that can help the communities that Statistical Makers operates in. Secondly it can use opportunities from government spending in Technology & Operations sector.
Creating value in data economy
– The success of analytics program of Statistical Makers has opened avenues for new revenue streams for the organization in the industry. This can help Statistical Makers to build a more holistic ecosystem as suggested in the Using Simulated Experience to Make Sense of Big Data case study. Statistical Makers can build new products and services such as - data insight services, data privacy related products, data based consulting services, etc.
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 Statistical Makers in the consumer business. Now Statistical Makers can target international markets with far fewer capital restrictions requirements than the existing system.
Remote work and new talent hiring opportunities
– The widespread usage of remote working technologies during Covid-19 has opened opportunities for Statistical Makers 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 Statistical Makers to hire the very best people irrespective of their geographical location.
Building a culture of innovation
– managers at Statistical Makers can make experimentation a productive activity and build a culture of innovation using approaches such as – mining transaction data, A/B testing of websites and selling platforms, engaging potential customers over various needs, and building on small ideas in the Technology & Operations segment.
Threats Using Simulated Experience to Make Sense of Big Data External Strategic Factors
What are Threats in the SWOT Analysis / TOWS Matrix / Weighted SWOT Analysis
The threats mentioned in the HBR case study Using Simulated Experience to Make Sense of Big Data are -
New competition
– After the dotcom bust of 2001, financial crisis of 2008-09, the business formation in US economy had declined. But in 2020 alone, there are more than 1.5 million new business applications in United States. This can lead to greater competition for Statistical Makers in the Technology & Operations sector and impact the bottomline of the organization.
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 Statistical Makers business can come under increasing regulations regarding data privacy, data security, etc.
Regulatory challenges
– Statistical Makers 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 Technology & Operations industry regulations.
Trade war between China and United States
– The trade war between two of the biggest economies can hugely impact the opportunities for Statistical Makers in the Technology & Operations industry. The Technology & Operations 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.
Learning curve for new practices
– As the technology based on artificial intelligence and machine learning platform is getting complex, as highlighted in case study Using Simulated Experience to Make Sense of Big Data, Statistical Makers may face longer learning curve for training and development of existing employees. This can open space for more nimble competitors in the field of Technology & Operations .
Environmental challenges
– Statistical Makers needs to have a robust strategy against the disruptions arising from climate change and energy requirements. EU has identified it as key priority area and spending 30% of its 880 billion Euros European post Covid-19 recovery funds on green technology. Statistical Makers can take advantage of this fund but it will also bring new competitors in the Technology & Operations industry.
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.
Shortening product life cycle
– it is one of the major threat that Statistical Makers is facing in Technology & Operations sector. It can lead to higher research and development costs, higher marketing expenses, lower customer loyalty, etc.
Capital market disruption
– During the Covid-19, Dow Jones has touched record high. The valuations of a number of companies are way beyond their existing business model potential. This can lead to capital market correction which can put a number of suppliers, collaborators, value chain partners in great financial difficulty. It will directly impact the business of Statistical Makers.
Technology acceleration in Forth Industrial Revolution
– Statistical Makers has witnessed rapid integration of technology during Covid-19 in the Technology & Operations industry. As one of the leading players in the industry, Statistical Makers needs to keep up with the evolution of technology in the Technology & Operations 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 international competition and downward pressure on margins
– Apart from technology driven competitive advantage dilution, Statistical Makers 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 Using Simulated Experience to Make Sense of Big Data .
High dependence on third party suppliers
– Statistical Makers 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.
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. Statistical Makers needs to understand the core reasons impacting the Technology & Operations industry. This will help it in building a better workplace.
Weighted SWOT Analysis of Using Simulated Experience to Make Sense of Big Data 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 Using Simulated Experience to Make Sense of Big Data 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 Using Simulated Experience to Make Sense of Big Data 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 Using Simulated Experience to Make Sense of Big Data 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 Using Simulated Experience to Make Sense of Big Data 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 Statistical Makers needs to make to build a sustainable competitive advantage.
