In his provocative 2003 Harvard Business Review article “IT Doesn't Matter,” Nicholas Carr argued that information technology (IT) had become a commodity, stripped of strategic advantage [1]. As a data translator working at the intersection of business, data, and IT teams, I find Carr's assertion not just misguided but potentially dangerous for organizations navigating today’s data-driven landscape. While Carr’s cautionary notes about overinvestment have merit, his core argument fundamentally misunderstands the evolving role of IT in modern enterprises.
The Fallacy of IT Commoditization
Carr’s central thesis is that as IT becomes ubiquitous, it loses its power to provide a competitive advantage, much like electricity or railroads in earlier eras [1]. This analogy, however, fails to capture the complex, evolving nature of modern IT systems. Unlike electricity, which has a relatively stable and standardized delivery, IT encompasses a vast array of constantly evolving tools, processes, and paradigms.
Consider the rise of cloud computing since Carr’s article. While cloud services are widely available, organizations leveraging these technologies gain significant advantages. Amazon’s transformation from an e-commerce giant to a cloud computing leader with Amazon Web Services (AWS) demonstrates how innovative approaches to IT can create new markets and competitive advantages [2]. Similarly, big data analytics and artificial intelligence have opened new frontiers for organizations to differentiate themselves through superior data utilization [3].
The real challenge—and opportunity—lies not in the mere possession of technology but in its strategic implementation and the cultivation of a data-driven culture. This is where the role of data translators becomes crucial, bridging the gap between technical capabilities and business needs.
The Strategic Value of Integrated IT and Data Initiatives
From my perspective as a data translator, IT’s actual value emerges when it’s seamlessly integrated with data initiatives and aligned with core business objectives. This view aligns with John Seely Brown and John Hagel’s rebuttal to Carr, emphasizing IT’s “indirect effects” [4]. They argue that IT’s strategic importance lies not in the technology itself but in how organizations leverage it to transform their operations and create new capabilities.
However, achieving this integration is far from trivial. It requires a deep understanding of business processes and technological capabilities and the ability to communicate effectively across these domains. For instance, when implementing a data analytics platform, success depends on the technology chosen and how well it’s tailored to the organization’s specific needs and integrated into existing workflows.
PROCTOR & GAMBLE 2000s
Procter & Gamble's (P&G) digital transformation in the early 2000s is a prime example of this integrated approach. Facing market share and profitability challenges, P&G embarked on an ambitious journey to integrate IT deeply into its business strategy [5]. They didn’t just adopt new technologies; they reimagined their entire business processes, from supply chain management to consumer engagement.
Key aspects of P&G's transformation included:
Digitization of Business Processes: P&G digitized end-to-end business processes, fundamentally changing how work was done [6].
Data-Driven Decision Making: They invested heavily in analytics capabilities, creating a centralized analytics team that worked closely with business units [7].
Innovation in Consumer Engagement: P&G leveraged digital technologies to create new ways of engaging consumers, such as virtual stores, to test product placement strategies [8].
IT as a Strategic Partner: Perhaps most importantly, P&G elevated IT from a support function to a strategic partner involved in business strategy discussions from the outset [9].
The results were significant. By 2011, P&G had reduced IT costs by about 20% while increasing capabilities, improved productivity across the board, enhanced its ability to bring innovations to market faster, and strengthened its competitive position in key markets [10].
P&G's case demonstrates that when strategically integrated and coupled with strong data capabilities, IT can provide a significant competitive advantage. It's a clear counterpoint to Carr's argument, showing that IT can be a key differentiator even in industries not traditionally seen as tech-centric.
UPS 2012-PRESENT
United Parcel Service (UPS)’s AI-driven digital transformation provides a compelling example of how strategic IT integration, mainly focusing on AI and advanced analytics, can drive significant business value. Facing increasing competition and the need for more efficient logistics, UPS embarked on an ambitious journey to leverage AI, big data, and advanced analytics to optimize its operations and enhance customer service [11].
Key aspects of UPS's transformation include:
ORION (On-Road Integrated Optimization and Navigation): Launched in 2012, ORION is an AI-powered route optimization system. It uses advanced algorithms to calculate the most efficient delivery routes, considering factors like traffic patterns, package priorities, and service commitments [12].
Network Planning Tools (NPT): Implemented in 2018, NPT uses AI and machine learning to optimize the flow of packages through UPS's logistics network. It can create optimal operating plans and make real-time adjustments based on changing conditions [13].
UPS My Choice: This AI-powered platform gives customers predictive alerts about their delivery times and allows them to adjust delivery locations and times. It uses machine learning to improve the accuracy of its predictions over time [14].
Chatbots and Virtual Agents: UPS has implemented AI-powered chatbots and virtual agents to handle customer inquiries, reducing the load on human customer service representatives and providing 24/7 support [15].
The results of these initiatives have been substantial:
ORION saves UPS an estimated 100 million miles driven annually, reducing fuel consumption by 10 million gallons and carbon emissions by 100,000 metric tons [2].
The company has seen a 3-5% reduction in daily miles driven, translating to $300-$400 million in annual savings [16].
UPS My Choice has over 67 million members globally, improving customer satisfaction and reducing failed delivery attempts [17].
The AI-powered Network Planning Tools have led to a 20% reduction in operating costs in the areas where they've been implemented [3].
UPS’s digital transformation goes beyond surface-level changes, involving fundamental shifts in company operations. It demonstrates a deep integration of AI and advanced analytics into core business processes, from route planning to customer interaction.
This case study also contradicts Carr’s argument that IT doesn't matter. UPS’s strategic use of AI and advanced analytics has improved operational efficiency, created new service offerings, and enhanced customer experience, providing a significant competitive advantage in the logistics industry.
The Perils of Misalignment: When IT Investments Fail
While I disagree with Carr’s conclusion that IT doesn't matter, his warnings about the risks of overinvestment in IT are well-founded. The business landscape is littered with failed IT projects that promised transformation but delivered disappointment.
Take the case of Ford’s attempt to modernize its purchasing system in the early 2000s. Despite an investment of $400 million, the project was abandoned after deployment issues and resistance from employees and suppliers [18]. This failure wasn't due to the technology itself but to a misalignment between the IT solution and the organization's processes and culture.
As a data translator, I've seen similar scenarios play out on a smaller scale countless times. Organizations invest in cutting-edge data analytics platforms or AI solutions, only to find that they can't effectively leverage these tools because they lack the necessary data infrastructure, skills, or organizational buy-in.
These missteps underscore the critical need for a holistic IT and data initiatives approach. It's not enough to have the latest technology; organizations must also cultivate the skills, processes, and culture needed to leverage these tools effectively. This is where the role of data translators becomes crucial, ensuring that IT and data initiatives are aligned with business needs and organizational realities.
Fostering Innovation Through Thoughtful Integration
Joe Weinman’s analogy of pork bellies and Michelin-star restaurants perfectly captures the potential of IT when skillfully leveraged [19]. It's not about having the latest technology but how creatively and effectively you integrate it into your processes.
As a data translator, I've seen this principle in action many times. For example, a mid-sized manufacturing company I worked with didn’t just implement an IoT system to monitor their production lines. Instead, they integrated this system with their existing data analytics platform and used the insights gained to redesign their production processes completely. The result was a 30% increase in efficiency and a significant competitive advantage in their market.
This kind of innovation doesn't come from IT alone. It requires a deep understanding of business processes, data analysis capabilities, and technological possibilities. It's about asking the right questions, identifying the most impactful areas for improvement, and creatively applying technology to solve real business problems (a special kind of headache).
Conclusion: The Evolving Role of IT in the Data-Driven Enterprise
In conclusion, Carr’s assertion that “IT doesn't matter” misses the mark. IT does matter profoundly, but its value lies not in the technology itself but in how it’s integrated into the organization’s fabric and leveraged to drive innovation and competitive advantage. These must be intentional acts; otherwise, it’s just an IT department buying tools to look for problems to solve.
As data continues to grow in importance, the role of IT is evolving. It's no longer just about providing infrastructure and support; it's about enabling data-driven decision-making and fostering innovation. This evolution demands new skills and roles, including that of the data translator, to bridge the gap between technical capabilities and business needs.
The future belongs not to those who adopt the latest technology but those who can effectively integrate IT, data, and business strategy. In this light, IT isn't just a matter of competitive advantage—it's a fundamental pillar of modern business strategy and operations.
As we move forward, the challenge for organizations will be to cultivate this integrated approach, breaking down silos between IT, data, and business teams. Only then can we fully realize the transformative potential of IT in the data-driven age.
References:
[1] Carr, N. G. (2003). IT doesn't matter. Harvard Business Review, 81(5), 41-49.
[2] Marston, S., Li, Z., Bandyopadhyay, S., Zhang, J., & Ghalsasi, A. (2011). Cloud computing - The business perspective. Decision Support Systems, 51(1), 176-189.
[3] McAfee, A., & Brynjolfsson, E. (2012). Big data: The management revolution. Harvard Business Review, 90(10), 60-68.
[4] Brown, J. S., & Hagel, J. (2003). Does IT matter? An HBR debate. Harvard Business Review, 81(7), 109-112.
[5] Dalziel, C. P. & Shah, J. (2010). Procter & Gamble: A Digital Revolution. INSEAD Case Study.
[6] Galbraith, J. R. (2012). The evolution of enterprise organization designs. Journal of Organization Design, 1(2), 1-13.
[7] Davenport, T. H., & Harris, J. G. (2007). Competing on analytics: The new science of winning. Harvard Business Press.
[8] Wind, Y. J., & Hays, C. F. (2016). Beyond advertising: Creating value through all customer touchpoints. John Wiley & Sons.
[9] Weill, P., & Ross, J. W. (2009). IT savvy: What top executives must know to go from pain to gain. Harvard Business Press.
[10] Bloch, M., & Hoyos-Gomez, A. (2009). How CIOs should think about business value. McKinsey on Business Technology, 18(1), 28-37.
[11] Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108-116.
[12] Chui, M., Manyika, J., & Miremadi, M. (2018). What AI can and can't do (yet) for your business. McKinsey Quarterly, 1, 96-108.
[13] UPS. (2018). UPS Enhances Network With New Automated Sortation Super Hub In Atlanta. UPS Pressroom.
[14] Lee, I. (2017). Big data: Dimensions, evolution, impacts, and challenges. Business Horizons, 60(3), 293-303.
[15] Daugherty, P. R., & Wilson, H. J. (2018). Human + machine: Reimagining work in the age of AI. Harvard Business Press.
[16] Banker, S., Cunnane, C., & Reiser, C. (2018). The Current and Future State of Digital Supply Chain Transformation. ARC Advisory Group.
[17] UPS. (2021). UPS 2020 Annual Report.
[18] Charette, R. N. (2005). Why software fails. IEEE Spectrum, 42(9), 42-49.
[19] Weinman, J. (2012). Cloudonomics: The business value of cloud computing. John Wiley & Sons.