Data Applications (3848) SWOT Analysis / TOWS Matrix / MBA Resources
Software & Programming
Strategy / MBA Resources
Introduction to SWOT Analysis
SWOT Analysis / TOWS Matrix for Data Applications (Japan)
Based on various researches at Oak Spring University , Data Applications is operating in a macro-environment that has been destablized by – increasing energy prices, increasing transportation and logistics costs, there is increasing trade war between United States & China, increasing household debt because of falling income levels, banking and financial system is disrupted by Bitcoin and other crypto currencies, customer relationship management is fast transforming because of increasing concerns over data privacy, increasing government debt because of Covid-19 spendings,
competitive advantages are harder to sustain because of technology dispersion, talent flight as more people leaving formal jobs, etc
Introduction to SWOT Analysis of Data Applications
SWOT stands for an organization’s Strengths, Weaknesses, Opportunities and Threats . At Oak Spring University, we believe that Data Applications can use SWOT analysis as a strategic management tool to assess the current internal strengths and weaknesses of the Data Applications, and to figure out the opportunities and threats in the macro environment – technological, environmental, political, economic, social, demographic, etc in which Data Applications 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 Data Applications can be done for the following purposes –
1. Strategic planning of Data Applications
2. Improving business portfolio management of Data Applications
3. Assessing feasibility of the new initiative in Japan
4. Making a Software & Programming sector specific business decision
5. Set goals for the organization
6. Organizational restructuring of Data Applications
Strengths of Data Applications | Internal Strategic Factors
What are Strengths in SWOT Analysis / TOWS Matrix / Weighted SWOT Analysis
The strengths of Data Applications are -
Innovation driven organization
– Data Applications is one of the most innovative firm in Software & Programming sector.
Training and development
– Data Applications has one of the best training and development program in Technology industry. The effectiveness of the training programs can be measured in – employees retention, in-house promotion, loyalty, new venture initiation, lack of conflict, and high level of both employees and customer engagement.
Ability to lead change in Software & Programming
– Data Applications is one of the leading players in the Software & Programming industry in Japan. Over the years it has not only transformed the business landscape in the Software & Programming industry in Japan but also across the existing markets. The ability to lead change has enabled Data Applications in – penetrating new markets, reaching out to new customers, and providing different value propositions to different customers in the international markets.
Superior customer experience
– The customer experience strategy of Data Applications in Software & Programming industry is based on four key concepts – personalization, simplification of complex needs, prompt response, and continuous engagement.
Successful track record of launching new products
– Data Applications has launched numerous new products in last few years, keeping in mind evolving customer preferences and competitive pressures. Data Applications 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.
Diverse revenue streams
– Data Applications is present in almost all the verticals within the Software & Programming industry. This has provided Data Applications 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.
Low bargaining power of suppliers
– Suppliers of Data Applications in the Technology sector have low bargaining power. Data Applications has further diversified its suppliers portfolio by building a robust supply chain across various countries. This helps Data Applications to manage not only supply disruptions but also source products at highly competitive prices.
High switching costs
– The high switching costs that Data Applications 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.
Learning organization
- Data Applications 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 Applications is open place that encourages instructiveness, ideation, open minded discussions, and creativity. Employees and leaders at Data Applications emphasize – knowledge, initiative, and innovation.
High brand equity
– Data Applications has strong brand awareness and brand recognition among both - the exiting customers and potential new customers. Strong brand equity has enabled Data Applications to keep acquiring new customers and building profitable relationship with both the new and loyal customers.
Effective Research and Development (R&D)
– Data Applications has innovation driven culture where significant part of the revenues are spent on the research and development activities. This has resulted in – Data Applications staying ahead in the Software & Programming industry in terms of – new product launches, superior customer experience, highly competitive pricing strategies, and great returns to the shareholders.
Operational resilience
– The operational resilience strategy of Data Applications comprises – understanding the underlying the factors in the Software & Programming 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.
Weaknesses of Data Applications | Internal Strategic Factors
What are Weaknesses in SWOT Analysis / TOWS Matrix / Weighted SWOT Analysis
The weaknesses of Data Applications are -
Low market penetration in new markets
– Outside its home market of Japan, Data Applications needs to spend more promotional, marketing, and advertising efforts to penetrate international markets.
Capital Spending Reduction
– Even during the low interest decade, Data Applications 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 Software & Programming industry using digital technology.
Lack of clear differentiation of Data Applications products
– To increase the profitability and margins on the products, Data Applications needs to provide more differentiated products than what it is currently offering in the marketplace.
Slow to strategic competitive environment developments
– As Data Applications is one of the leading players in the Software & Programming industry, it takes time to assess the upcoming competitions. This has led to missing out on atleast 2-3 big opportunities in the Software & Programming industry in last five years.
Products dominated business model
– Even though Data Applications has some of the most successful models in the Software & Programming industry, this business model has made each new product launch extremely critical for continuous financial growth of the organization. Data Applications should strive to include more intangible value offerings along with its core products and services.
Skills based hiring in Software & Programming industry
– The stress on hiring functional specialists at Data Applications 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.
High dependence on Data Applications ‘s star products
– The top 2 products and services of Data Applications still accounts for major business revenue. This dependence on star products in Software & Programming industry has resulted into insufficient focus on developing new products, even though Data Applications has relatively successful track record of launching new products.
Slow decision making process
– As mentioned earlier in the report, Data Applications has a very deliberative decision making approach. This approach has resulted in prudent decisions, but it has also resulted in missing opportunities in the Software & Programming industry over the last five years. Data Applications 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 Data Applications is dominated by functional specialists. It is not different from other players in the Software & Programming industry, but Data Applications needs to de-silo the office environment to harness the true potential of its workforce. Secondly the de-silo will also help Data Applications to focus more on services in the Software & Programming industry rather than just following the product oriented approach.
No frontier risks strategy
– From the 10K / annual statement of Data Applications, it seems that company is thinking out the frontier risks that can impact Software & Programming industry. 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.
Compensation and incentives
– The revenue per employee of Data Applications is just above the Software & Programming industry average. It 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.
Data Applications Opportunities | External Strategic Factors
What are Opportunities in the SWOT Analysis / TOWS Matrix / Weighted SWOT Analysis
The opportunities of Data Applications are -
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 Applications can explore opportunities that can attract volunteers and are consistent with its mission and vision.
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 Applications 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.
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 Applications can use these opportunities to build new business models that can help the communities that Data Applications operates in. Secondly it can use opportunities from government spending in Software & Programming sector.
Learning at scale
– Online learning technologies has now opened space for Data Applications 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.
Redefining models of collaboration and team work
– As explained in the weaknesses section, Data Applications is facing challenges because of the dominance of functional experts in the organization. Data Applications can utilize new technology in the field of Software & Programming industry to build more coordinated teams and streamline operations and communications using tools such as CAD, Zoom, etc.
Changes in consumer behavior post Covid-19
– consumer behavior has changed in the Software & Programming industry because of Covid-19 restrictions. Some of this behavior will stay once things get back to normal. Data Applications 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 Applications 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.
Developing new processes and practices
– Data Applications can develop new processes and procedures in Software & Programming 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 Applications has opened avenues for new revenue streams for the organization in Software & Programming industry. This can help Data Applications to build a more holistic ecosystem for Data Applications products in the Software & Programming industry by providing – data insight services, data privacy related products, data based consulting services, etc.
Low interest rates
– Even though inflation is raising its head in most developed economies, Data Applications 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
– Data Applications 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 Data Applications to increase its market reach. Data Applications 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.
Remote work and new talent hiring opportunities
– The widespread usage of remote working technologies during Covid-19 has opened opportunities for Data Applications 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 Applications to hire the very best people irrespective of their geographical location.
Lowering marketing communication costs
– 5G expansion will open new opportunities for Data Applications in the field of marketing communication. It will bring down the cost of doing business, provide technology platform to build new products in the Software & Programming industry, and it will provide faster access to the consumers.
Threats Data Applications External Strategic Factors
What are Threats in the SWOT Analysis / TOWS Matrix / Weighted SWOT Analysis
The threats of Data Applications are -
Increasing wage structure of Data Applications
– 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 Applications.
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 Data Applications.
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.
Environmental challenges
– Data Applications 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. Data Applications can take advantage of this fund but it will also bring new competitors in the Software & Programming industry.
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 Applications business can come under increasing regulations regarding data privacy, data security, etc.
Consumer confidence and its impact on Data Applications 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 Software & Programming industry and other sectors.
Shortening product life cycle
– it is one of the major threat that Data Applications is facing in Software & Programming sector. It can lead to higher research and development costs, higher marketing expenses, lower customer loyalty, etc.
Stagnating economy with rate increase
– Data Applications 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 Software & Programming industry.
Instability in the European markets
– European Union markets are facing three big challenges post Covid – expanded balance sheets, Brexit related business disruption, and aggressive Russia looking to distract the existing security mechanism. Data Applications will face different problems in different parts of Europe. For example it will face inflationary pressures in UK, France, and Germany, balance sheet expansion and demand challenges in Southern European countries, and geopolitical instability in the Eastern Europe.
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 Data Applications in the Software & Programming sector and impact the bottomline of the organization.
Technology acceleration in Forth Industrial Revolution
– Data Applications has witnessed rapid integration of technology during Covid-19 in the Software & Programming industry. As one of the leading players in the industry, Data Applications needs to keep up with the evolution of technology in the Software & Programming 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.
Easy access to finance
– Easy access to finance in Software & Programming industry will also reduce the barriers to entry in the industry, thus putting downward pressure on the prices because of increasing competition. Data Applications can utilize it by borrowing at lower rates and invest it into research and development, capital expenditure to fortify its core competitive advantage.
Increasing international competition and downward pressure on margins
– Apart from technology driven competitive advantage dilution, Data Applications 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 Data Applications prominent markets.
Weighted SWOT Analysis of Data Applications Template, Example
Not all factors mentioned under the Strengths, Weakness, Opportunities, and Threats quadrants in the SWOT Analysis are equal. Managers at Data Applications 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 Data Applications 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 Data Applications 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 Data Applications 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 Applications needs to make to build a sustainable competitive advantage.