Relational Data Models in Enterprise-Level Information Systems SWOT Analysis / TOWS Matrix / Weighted SWOT Analysis
Technology & Operations
Strategy / MBA Resources
Case Study SWOT Analysis Solution
Case Study Description of Relational Data Models in Enterprise-Level Information Systems
Provides an overview of some important aspects of the relational data models that underlie today's enterprise-level information technologies, such as enterprise resource planning and customer relationship management. Key concepts covered include relational data and integration. A stylized example is provided.
Swot Analysis of "Relational Data Models in Enterprise-Level Information Systems" written by Mark Cotteleer includes – strengths weakness that are internal strategic factors of the organization, and opportunities and threats that Relational Enterprise facing as an external strategic factors. Some of the topics covered in Relational Data Models in Enterprise-Level Information Systems case study are - Strategic Management Strategies, IT and Technology & Operations.
Some of the macro environment factors that can be used to understand the Relational Data Models in Enterprise-Level Information Systems casestudy better are - – talent flight as more people leaving formal jobs, geopolitical disruptions, customer relationship management is fast transforming because of increasing concerns over data privacy, banking and financial system is disrupted by Bitcoin and other crypto currencies, central banks are concerned over increasing inflation, increasing transportation and logistics costs, increasing inequality as vast percentage of new income is going to the top 1%,
wage bills are increasing, increasing commodity prices, etc
Introduction to SWOT Analysis of Relational Data Models in Enterprise-Level Information Systems
SWOT stands for an organization’s Strengths, Weaknesses, Opportunities and Threats . At Oak Spring University , we believe that protagonist in Relational Data Models in Enterprise-Level Information Systems case study can use SWOT analysis as a strategic management tool to assess the current internal strengths and weaknesses of the Relational Enterprise, and to figure out the opportunities and threats in the macro environment – technological, environmental, political, economic, social, demographic, etc in which Relational Enterprise 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 Relational Data Models in Enterprise-Level Information Systems can be done for the following purposes –
1. Strategic planning using facts provided in Relational Data Models in Enterprise-Level Information Systems case study
2. Improving business portfolio management of Relational Enterprise
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 Relational Enterprise
Strengths Relational Data Models in Enterprise-Level Information Systems | Internal Strategic Factors
What are Strengths in SWOT Analysis / TOWS Matrix / Weighted SWOT Analysis
The strengths of Relational Enterprise in Relational Data Models in Enterprise-Level Information Systems Harvard Business Review case study are -
Ability to lead change in Technology & Operations field
– Relational Enterprise is one of the leading players in its industry. Over the years it has not only transformed the business landscape in its segment but also across the whole industry. The ability to lead change has enabled Relational Enterprise in – penetrating new markets, reaching out to new customers, and providing different value propositions to different customers in the international markets.
High switching costs
– The high switching costs that Relational Enterprise 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
- Relational Enterprise 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 Relational Enterprise is open place that encourages instructiveness, ideation, open minded discussions, and creativity. Employees and leaders in Relational Data Models in Enterprise-Level Information Systems Harvard Business Review case study emphasize – knowledge, initiative, and innovation.
Operational resilience
– The operational resilience strategy in the Relational Data Models in Enterprise-Level Information Systems 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.
Training and development
– Relational Enterprise has one of the best training and development program in the industry. The effectiveness of the training programs can be measured in Relational Data Models in Enterprise-Level Information Systems 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.
Effective Research and Development (R&D)
– Relational Enterprise 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 Relational Data Models in Enterprise-Level Information Systems - staying ahead in the industry in terms of – new product launches, superior customer experience, highly competitive pricing strategies, and great returns to the shareholders.
Strong track record of project management
– Relational Enterprise is known for sticking to its project targets. This enables the firm to manage – time, project costs, and have sustainable margins on the projects.
Ability to recruit top talent
– Relational Enterprise is one of the leading recruiters in the industry. Managers in the Relational Data Models in Enterprise-Level Information Systems are in a position to attract the best talent available. The firm has a robust talent identification program that helps in identifying the brightest.
Highly skilled collaborators
– Relational Enterprise 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 Relational Data Models in Enterprise-Level Information Systems HBR case study have helped the firm to develop new products and bring them quickly to the marketplace.
Innovation driven organization
– Relational Enterprise is one of the most innovative firm in sector. Manager in Relational Data Models in Enterprise-Level Information Systems Harvard Business Review case study can use Clayton Christensen Disruptive Innovation strategies to further increase the scale of innovtions in the organization.
Successful track record of launching new products
– Relational Enterprise has launched numerous new products in last few years, keeping in mind evolving customer preferences and competitive pressures. Relational Enterprise 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.
Digital Transformation in Technology & Operations segment
- digital transformation varies from industry to industry. For Relational Enterprise digital transformation journey comprises differing goals based on market maturity, customer technology acceptance, and organizational culture. Relational Enterprise 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.
Weaknesses Relational Data Models in Enterprise-Level Information Systems | Internal Strategic Factors
What are Weaknesses in SWOT Analysis / TOWS Matrix / Weighted SWOT Analysis
The weaknesses of Relational Data Models in Enterprise-Level Information Systems are -
Lack of clear differentiation of Relational Enterprise products
– To increase the profitability and margins on the products, Relational Enterprise needs to provide more differentiated products than what it is currently offering in the marketplace.
Ability to respond to the competition
– As the decision making is very deliberative, highlighted in the case study Relational Data Models in Enterprise-Level Information Systems, in the dynamic environment Relational Enterprise has struggled to respond to the nimble upstart competition. Relational Enterprise has reasonably good record with similar level competitors but it has struggled with new entrants taking away niches of its business.
High cash cycle compare to competitors
Relational Enterprise 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.
Low market penetration in new markets
– Outside its home market of Relational Enterprise, firm in the HBR case study Relational Data Models in Enterprise-Level Information Systems needs to spend more promotional, marketing, and advertising efforts to penetrate international markets.
High dependence on star products
– The top 2 products and services of the firm as mentioned in the Relational Data Models in Enterprise-Level Information Systems 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 Relational Enterprise has relatively successful track record of launching new products.
Slow to harness new channels of communication
– Even though competitors are using new communication channels such as Instagram, Tiktok, and Snap, Relational Enterprise is slow explore the new channels of communication. These new channels of communication mentioned in marketing section of case study Relational Data Models in Enterprise-Level Information Systems can help to provide better information regarding products and services. It can also build an online community to further reach out to potential customers.
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 Relational Enterprise supply chain. Even after few cautionary changes mentioned in the HBR case study - Relational Data Models in Enterprise-Level Information Systems, it is still heavily dependent upon the existing supply chain. The existing supply chain though brings in cost efficiencies but it has left Relational Enterprise vulnerable to further global disruptions in South East Asia.
Slow decision making process
– As mentioned earlier in the report, Relational Enterprise 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. Relational Enterprise 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.
Products dominated business model
– Even though Relational Enterprise 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 - Relational Data Models in Enterprise-Level Information Systems should strive to include more intangible value offerings along with its core products and services.
Increasing silos among functional specialists
– The organizational structure of Relational Enterprise is dominated by functional specialists. It is not different from other players in the Technology & Operations segment. Relational Enterprise needs to de-silo the office environment to harness the true potential of its workforce. Secondly the de-silo will also help Relational Enterprise to focus more on services rather than just following the product oriented approach.
Skills based hiring
– The stress on hiring functional specialists at Relational Enterprise 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.
Opportunities Relational Data Models in Enterprise-Level Information Systems | 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 Relational Data Models in Enterprise-Level Information Systems are -
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. Relational Enterprise can tie-up with other value chain partners to explore new opportunities regarding meeting customer demands and building a rewarding and engaging relationship.
Creating value in data economy
– The success of analytics program of Relational Enterprise has opened avenues for new revenue streams for the organization in the industry. This can help Relational Enterprise to build a more holistic ecosystem as suggested in the Relational Data Models in Enterprise-Level Information Systems case study. Relational Enterprise 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, Relational Enterprise can use these opportunities to build new business models that can help the communities that Relational Enterprise operates in. Secondly it can use opportunities from government spending in Technology & Operations sector.
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 Relational Enterprise 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.
Harnessing reconfiguration of the global supply chains
– As the trade war between US and China heats up in the coming years, Relational Enterprise 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, Relational Data Models in Enterprise-Level Information Systems, to buy more products closer to the markets, and it can leverage its size and influence to get better deal from the local markets.
Manufacturing automation
– Relational Enterprise can use the latest technology developments to improve its manufacturing and designing process in Technology & Operations 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.
Loyalty marketing
– Relational Enterprise 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.
Low interest rates
– Even though inflation is raising its head in most developed economies, Relational Enterprise 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 Relational Enterprise in the field of marketing communication. It will bring down the cost of doing business, provide technology platform to build new products in the Technology & Operations 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 Relational Enterprise 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 Relational Enterprise to hire the very best people irrespective of their geographical location.
Better consumer reach
– The expansion of the 5G network will help Relational Enterprise to increase its market reach. Relational Enterprise 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.
Redefining models of collaboration and team work
– As explained in the weaknesses section, Relational Enterprise is facing challenges because of the dominance of functional experts in the organization. Relational Data Models in Enterprise-Level Information Systems 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.
Building a culture of innovation
– managers at Relational Enterprise 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 Relational Data Models in Enterprise-Level Information Systems External Strategic Factors
What are Threats in the SWOT Analysis / TOWS Matrix / Weighted SWOT Analysis
The threats mentioned in the HBR case study Relational Data Models in Enterprise-Level Information Systems are -
Increasing wage structure of Relational Enterprise
– 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 Relational Enterprise.
Stagnating economy with rate increase
– Relational Enterprise 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.
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.
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. Relational Enterprise needs to understand the core reasons impacting the Technology & Operations industry. This will help it in building a better workplace.
Technology acceleration in Forth Industrial Revolution
– Relational Enterprise has witnessed rapid integration of technology during Covid-19 in the Technology & Operations industry. As one of the leading players in the industry, Relational Enterprise 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.
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 Relational Enterprise.
Shortening product life cycle
– it is one of the major threat that Relational Enterprise is facing in Technology & Operations sector. It can lead to higher research and development costs, higher marketing expenses, lower customer loyalty, etc.
Easy access to finance
– Easy access to finance in Technology & Operations field will also reduce the barriers to entry in the industry, thus putting downward pressure on the prices because of increasing competition. Relational Enterprise can utilize it by borrowing at lower rates and invest it into research and development, capital expenditure to fortify its core competitive advantage.
Learning curve for new practices
– As the technology based on artificial intelligence and machine learning platform is getting complex, as highlighted in case study Relational Data Models in Enterprise-Level Information Systems, Relational Enterprise 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 .
Regulatory challenges
– Relational Enterprise 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.
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 Relational Enterprise in the Technology & Operations sector and impact the bottomline of the organization.
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. Relational Enterprise 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.
Weighted SWOT Analysis of Relational Data Models in Enterprise-Level Information Systems 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 Relational Data Models in Enterprise-Level Information Systems 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 Relational Data Models in Enterprise-Level Information Systems 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 Relational Data Models in Enterprise-Level Information Systems 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 Relational Data Models in Enterprise-Level Information Systems 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 Relational Enterprise needs to make to build a sustainable competitive advantage.