How Big Data Is Different SWOT Analysis / TOWS Matrix / Weighted SWOT Analysis
Innovation & Entrepreneurship
Strategy / MBA Resources
Case Study SWOT Analysis Solution
Case Study Description of How Big Data Is Different
This is an MIT Sloan Management Review article. Many people in the information technology world believe that "big data"will give companies new capabilities and value. But companies have been dealing with an exponentially increasing amount of data, and much of it in forms that are impossible to manage by traditional analytics. "Big data"includes information such as call center voice data, social media content and video entertainment, as well as clickstream data from the web. The authors posit that organizations that are learning to take advantage of big data are beginning to understand their business environments at a more granular level, are creating new products and services, and are responding more quickly to change as it occurs. These companies stand apart from those with traditional data analysis environments in three critical ways. First, rather than looking at data to assess what happened in the past, these organizations consider data in terms of flows and processes, and make decisions and take actions quickly. In addition, organizations already involved with big data are taking a lead on hiring -and training -data scientists and product and process developers as opposed to data analysts. And finally, advanced organizations are moving analytics from IT into their core business and operational functions. As big data evolves, a new information ecosystem is also evolving, a network that is continuously sharing information, optimizing decisions, communicating results and generating new insights for businesses.
Authors :: Thomas H. Davenport, Paul Barth, Randy Bean
Swot Analysis of "How Big Data Is Different" written by Thomas H. Davenport, Paul Barth, Randy Bean includes – strengths weakness that are internal strategic factors of the organization, and opportunities and threats that Data Organizations facing as an external strategic factors. Some of the topics covered in How Big Data Is Different case study are - Strategic Management Strategies, IT and Innovation & Entrepreneurship.
Some of the macro environment factors that can be used to understand the How Big Data Is Different casestudy better are - – supply chains are disrupted by pandemic , increasing government debt because of Covid-19 spendings, increasing household debt because of falling income levels, banking and financial system is disrupted by Bitcoin and other crypto currencies, challanges to central banks by blockchain based private currencies, central banks are concerned over increasing inflation, competitive advantages are harder to sustain because of technology dispersion,
there is backlash against globalization, increasing commodity prices, etc
Introduction to SWOT Analysis of How Big Data Is Different
SWOT stands for an organization’s Strengths, Weaknesses, Opportunities and Threats . At Oak Spring University , we believe that protagonist in How Big Data Is Different case study can use SWOT analysis as a strategic management tool to assess the current internal strengths and weaknesses of the Data Organizations, and to figure out the opportunities and threats in the macro environment – technological, environmental, political, economic, social, demographic, etc in which Data Organizations 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 How Big Data Is Different can be done for the following purposes –
1. Strategic planning using facts provided in How Big Data Is Different case study
2. Improving business portfolio management of Data Organizations
3. Assessing feasibility of the new initiative in Innovation & Entrepreneurship field.
4. Making a Innovation & Entrepreneurship topic specific business decision
5. Set goals for the organization
6. Organizational restructuring of Data Organizations
Strengths How Big Data Is Different | Internal Strategic Factors
What are Strengths in SWOT Analysis / TOWS Matrix / Weighted SWOT Analysis
The strengths of Data Organizations in How Big Data Is Different Harvard Business Review case study are -
Analytics focus
– Data Organizations 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 Thomas H. Davenport, Paul Barth, Randy Bean 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.
Ability to lead change in Innovation & Entrepreneurship field
– Data Organizations 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 Data Organizations in – penetrating new markets, reaching out to new customers, and providing different value propositions to different customers in the international markets.
Diverse revenue streams
– Data Organizations is present in almost all the verticals within the industry. This has provided firm in How Big Data Is Different 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.
Operational resilience
– The operational resilience strategy in the How Big Data Is Different 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.
Sustainable margins compare to other players in Innovation & Entrepreneurship industry
– How Big Data Is Different firm has clearly differentiated products in the market place. This has enabled Data Organizations to fetch slight price premium compare to the competitors in the Innovation & Entrepreneurship industry. The sustainable margins have also helped Data Organizations to invest into research and development (R&D) and innovation.
Superior customer experience
– The customer experience strategy of Data Organizations in the segment is based on four key concepts – personalization, simplification of complex needs, prompt response, and continuous engagement.
Ability to recruit top talent
– Data Organizations is one of the leading recruiters in the industry. Managers in the How Big Data Is Different are in a position to attract the best talent available. The firm has a robust talent identification program that helps in identifying the brightest.
Effective Research and Development (R&D)
– Data Organizations 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 How Big Data Is Different - 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 Innovation & Entrepreneurship segment
- digital transformation varies from industry to industry. For Data Organizations digital transformation journey comprises differing goals based on market maturity, customer technology acceptance, and organizational culture. Data Organizations 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.
Strong track record of project management
– Data Organizations is known for sticking to its project targets. This enables the firm to manage – time, project costs, and have sustainable margins on the projects.
Learning organization
- Data Organizations 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 Organizations is open place that encourages instructiveness, ideation, open minded discussions, and creativity. Employees and leaders in How Big Data Is Different Harvard Business Review case study emphasize – knowledge, initiative, and innovation.
Organizational Resilience of Data Organizations
– The covid-19 pandemic has put organizational resilience at the centre of everthing that Data Organizations does. Organizational resilience comprises - Financial Resilience, Operational Resilience, Technological Resilience, Organizational Resilience, Business Model Resilience, and Reputation Resilience.
Weaknesses How Big Data Is Different | Internal Strategic Factors
What are Weaknesses in SWOT Analysis / TOWS Matrix / Weighted SWOT Analysis
The weaknesses of How Big Data Is Different are -
High operating costs
– Compare to the competitors, firm in the HBR case study How Big Data Is Different has high operating costs in the. This can be harder to sustain given the new emerging competition from nimble players who are using technology to attract Data Organizations 's lucrative customers.
Slow decision making process
– As mentioned earlier in the report, Data Organizations has a very deliberative decision making approach. This approach has resulted in prudent decisions, but it has also resulted in missing opportunities in the industry over the last five years. Data Organizations 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.
Lack of clear differentiation of Data Organizations products
– To increase the profitability and margins on the products, Data Organizations needs to provide more differentiated products than what it is currently offering in the marketplace.
Workers concerns about automation
– As automation is fast increasing in the segment, Data Organizations 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.
Capital Spending Reduction
– Even during the low interest decade, Data Organizations has not been able to do capital spending to the tune of the competition. This has resulted into fewer innovations and company facing stiff competition from both existing competitors and new entrants who are disrupting the industry using digital technology.
Products dominated business model
– Even though Data Organizations 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 - How Big Data Is Different should strive to include more intangible value offerings along with its core products and services.
Need for greater diversity
– Data Organizations has taken concrete steps on diversity, equity, and inclusion. But the efforts so far has resulted in limited success. It needs to expand the recruitment and selection process to hire more people from the minorities and underprivileged background.
Increasing silos among functional specialists
– The organizational structure of Data Organizations is dominated by functional specialists. It is not different from other players in the Innovation & Entrepreneurship segment. Data Organizations needs to de-silo the office environment to harness the true potential of its workforce. Secondly the de-silo will also help Data Organizations to focus more on services rather than just following the product oriented approach.
Low market penetration in new markets
– Outside its home market of Data Organizations, firm in the HBR case study How Big Data Is Different needs to spend more promotional, marketing, and advertising efforts to penetrate international markets.
Compensation and incentives
– The revenue per employee as mentioned in the HBR case study How Big Data Is Different, is just above the industry average. Data Organizations 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 bargaining power of channel partners
– Because of the regulatory requirements, Thomas H. Davenport, Paul Barth, Randy Bean suggests that, Data Organizations 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.
Opportunities How Big Data Is Different | 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 How Big Data Is Different 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 Organizations can explore opportunities that can attract volunteers and are consistent with its mission and vision.
Finding new ways to collaborate
– Covid-19 has not only transformed business models of companies in Innovation & Entrepreneurship industry, but it has also influenced the consumer preferences. Data Organizations can tie-up with other value chain partners to explore new opportunities regarding meeting customer demands and building a rewarding and engaging relationship.
Better consumer reach
– The expansion of the 5G network will help Data Organizations to increase its market reach. Data Organizations 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, Data Organizations is facing challenges because of the dominance of functional experts in the organization. How Big Data Is Different 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 Data Organizations 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 Innovation & Entrepreneurship segment.
Reforming the budgeting process
- By establishing new metrics that will be used to evaluate both existing and potential projects Data Organizations can not only reduce the costs of the project but also help it in integrating the projects with other processes within the organization.
Using analytics as competitive advantage
– Data Organizations 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 How Big Data Is Different - to build a competitive advantage using analytics. The analytics driven competitive advantage can help Data Organizations to build faster Go To Market strategies, better consumer insights, developing relevant product features, and building a highly efficient supply chain.
Loyalty marketing
– Data Organizations 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 Data Organizations 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.
Creating value in data economy
– The success of analytics program of Data Organizations has opened avenues for new revenue streams for the organization in the industry. This can help Data Organizations to build a more holistic ecosystem as suggested in the How Big Data Is Different case study. Data Organizations 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 Data Organizations in the consumer business. Now Data Organizations can target international markets with far fewer capital restrictions requirements than the existing system.
Learning at scale
– Online learning technologies has now opened space for Data Organizations 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.
Developing new processes and practices
– Data Organizations can develop new processes and procedures in Innovation & Entrepreneurship 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.
Threats How Big Data Is Different External Strategic Factors
What are Threats in the SWOT Analysis / TOWS Matrix / Weighted SWOT Analysis
The threats mentioned in the HBR case study How Big Data Is Different are -
Barriers of entry lowering
– As technology is more democratized, the barriers to entry in the industry are lowering. It can presents Data Organizations with greater competitive threats in the near to medium future. Secondly it will also put downward pressure on pricing throughout the sector.
Easy access to finance
– Easy access to finance in Innovation & Entrepreneurship field will also reduce the barriers to entry in the industry, thus putting downward pressure on the prices because of increasing competition. Data Organizations can utilize it by borrowing at lower rates and invest it into research and development, capital expenditure to fortify its core competitive advantage.
Stagnating economy with rate increase
– Data Organizations 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.
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 Organizations.
Increasing international competition and downward pressure on margins
– Apart from technology driven competitive advantage dilution, Data Organizations 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 How Big Data Is Different .
Environmental challenges
– Data Organizations 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 Organizations can take advantage of this fund but it will also bring new competitors in the Innovation & Entrepreneurship industry.
Consumer confidence and its impact on Data Organizations 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. Data Organizations 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.
Learning curve for new practices
– As the technology based on artificial intelligence and machine learning platform is getting complex, as highlighted in case study How Big Data Is Different, Data Organizations may face longer learning curve for training and development of existing employees. This can open space for more nimble competitors in the field of Innovation & Entrepreneurship .
Regulatory challenges
– Data Organizations 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 Innovation & Entrepreneurship industry regulations.
Shortening product life cycle
– it is one of the major threat that Data Organizations is facing in Innovation & Entrepreneurship sector. It can lead to higher research and development costs, higher marketing expenses, lower customer loyalty, etc.
High dependence on third party suppliers
– Data Organizations 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.
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.
Weighted SWOT Analysis of How Big Data Is Different 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 How Big Data Is Different 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 How Big Data Is Different 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 How Big Data Is Different 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 How Big Data Is Different is to provide strategic recommendations includes – joining likelihood of external strategic factors such as opportunities and threats to the internal strategic factors – strengths and weaknesses. You should start with external factors as they will provide the direction of the overall industry. Secondly by joining probabilities with internal strategic factors can help the company not only strategic fit but also the most probably strategic trade-off that Data Organizations needs to make to build a sustainable competitive advantage.
