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 - – there is backlash against globalization, geopolitical disruptions, increasing commodity prices, increasing household debt because of falling income levels, customer relationship management is fast transforming because of increasing concerns over data privacy, digital marketing is dominated by two big players Facebook and Google, cloud computing is disrupting traditional business models,
increasing government debt because of Covid-19 spendings, challanges to central banks by blockchain based private currencies, 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 -
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.
Cross disciplinary teams
– Horizontal connected teams at the Relational Enterprise 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.
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.
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.
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.
Diverse revenue streams
– Relational Enterprise is present in almost all the verticals within the industry. This has provided firm in Relational Data Models in Enterprise-Level Information Systems 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.
Sustainable margins compare to other players in Technology & Operations industry
– Relational Data Models in Enterprise-Level Information Systems firm has clearly differentiated products in the market place. This has enabled Relational Enterprise to fetch slight price premium compare to the competitors in the Technology & Operations industry. The sustainable margins have also helped Relational Enterprise to invest into research and development (R&D) and innovation.
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.
Analytics focus
– Relational Enterprise is putting a lot of focus on utilizing the power of analytics in business decision making. This has put it among the leading players in the industry. The technology infrastructure suggested by Mark Cotteleer can also help it to harness the power of analytics for – marketing optimization, demand forecasting, customer relationship management, inventory management, information sharing across the value chain etc.
Organizational Resilience of Relational Enterprise
– The covid-19 pandemic has put organizational resilience at the centre of everthing that Relational Enterprise does. Organizational resilience comprises - Financial Resilience, Operational Resilience, Technological Resilience, Organizational Resilience, Business Model Resilience, and Reputation Resilience.
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.
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.
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 -
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.
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 operating costs
– Compare to the competitors, firm in the HBR case study Relational Data Models in Enterprise-Level Information Systems 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 Relational Enterprise 's lucrative customers.
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.
Interest costs
– Compare to the competition, Relational Enterprise 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.
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.
High bargaining power of channel partners
– Because of the regulatory requirements, Mark Cotteleer suggests that, Relational Enterprise is facing high bargaining power of the channel partners. So far it has not able to streamline the operations to reduce the bargaining power of the value chain partners in the industry.
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.
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.
Slow to strategic competitive environment developments
– As Relational Data Models in Enterprise-Level Information Systems HBR case study mentions - Relational Enterprise takes time to assess the upcoming competitions. This has led to missing out on atleast 2-3 big opportunities in the industry in last five years.
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.
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 -
Developing new processes and practices
– Relational Enterprise can develop new processes and procedures in Technology & Operations 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.
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.
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.
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.
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.
Buying journey improvements
– Relational Enterprise can improve the customer journey of consumers in the industry by using analytics and artificial intelligence. Relational Data Models in Enterprise-Level Information Systems 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.
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.
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.
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.
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.
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.
Changes in consumer behavior post Covid-19
– Consumer behavior has changed in the Technology & Operations industry because of Covid-19 restrictions. Some of this behavior will stay once things get back to normal. Relational Enterprise 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. Relational Enterprise 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.
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 -
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.
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.
Consumer confidence and its impact on Relational Enterprise 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.
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.
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.
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.
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 .
Increasing international competition and downward pressure on margins
– Apart from technology driven competitive advantage dilution, Relational Enterprise 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 Relational Data Models in Enterprise-Level Information Systems .
High dependence on third party suppliers
– Relational Enterprise 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.
Environmental challenges
– Relational Enterprise 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. Relational Enterprise can take advantage of this fund but it will also bring new competitors in the Technology & Operations industry.
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.
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.
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.