Amazon, iRobot, and Dominant Technical Strategy
Oh, the hot takes on Amazon buying iRobot. If we focus on the technology alone, all we see is a massive robotics play. iRobot brings significant capabilities to Amazon in the field of robotics and automation.
Another technology view is the data. Those devices map our homes and have a wealth of metadata for Amazon to mine and learn from. Their eCommerce platform will thrive on that data.
The strategists are looking at the competitive advantages and business model. It's a massive boost to Amazon's smart home presence. It gives them a foot in the door to add Ring and Alexa. It adds capabilities to their new line of home robots.
All those benefits are too small to justify the acquisition. They are not interesting enough for Amazon. So, why did they do it?
You can probably guess where I'm going, technical strategy. The advantage of having a hybrid capability set is synthesizing two views. Amazon's technology model reveals how this acquisition fits into a much bigger picture and senior leadership's long-term vision.
So many words have been written for what can be described with a single word. Many of their acquisitions can be explained with that one word. Amazon has several business models, but they all rest on one word. Their recent work on causal machine learning was all to support one word.
In Business Strategy For Data Scientists, I explain a new paradigm for competing across industries. I use case studies from Google and Amazon to describe competing on best-in-class capabilities. For Google, it's their ability to assemble and monetize access to knowledge graphs.
For Amazon, it's scale. They have mastered scale across several use cases. AWS provides access to technical scale. It doesn't matter how many users show up tomorrow or how great the system load; AWS will scale to handle it.
Amazon Prime provides access to a marketplace at scale. Listing products scales distribution as far as demand exists. It also opens a supply chain at scale to help businesses tackle the logistics of meeting demand at scale. The business's production capabilities are the only limit to meeting 100% of demand.
Acquiring iRobot provides it with access to Amazon's ability to scale its business and distribution to rapidly service a much larger share of the total addressable market. There are benefits for Amazon, but the deeper theme is access to scale.
When a business clearly articulates the competitive advantages and best-in-class capabilities created by technology, senior leadership can build strategies that amplify them. The 'Why' behind building scale to be a best-in-class capability is driven by strategy and in support of senior leadership's vision. Technology did not come first. It was built in service of the business. It created new opportunities, and Amazon chose the highest-value opportunities, but only because they had a framework to manage technology's value creation.
That framework is called the technology model.
The Technology Model
The power of a technology model is how it reveals growth opportunities. Technology creates new opportunities, but what SHOULD the business spend its technical resources on? What opportunities SHOULD the company pursue? Strategy planning evaluates available opportunities to answer those questions.
In many businesses, this happens inefficiently because there is no framework to identify, estimate, and prioritize opportunities enabled by technology. Assessing the total costs of pursuing the opportunity is nearly impossible. What happens?
The technology organization brings opportunities to the business that are not aligned with the current strategy. Solutions are built based on what's possible. They are technology in search of utility. It can be built, and there are several options for monetization. However, those rarely align with the business and operating models. The company isn't set up to create value with or monetize the technology.
Solutions do not align with business goals and clash with the business's strategy. The root cause of many data science initiatives' failures can be traced back to this disconnect. Technology cannot drive strategy, but it must be part of the strategic planning process.
The technology model resolves the disconnect. Senior leadership can use the technology model to select the opportunities that best align with the business's strategic goals. The technology model supports the business and operating models. It drives their continuous transformation and innovation.
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The Technical Strategist's Role
Companies like Amazon and Google need a C-level technical strategist because they are technology-first companies. All their growth and value creation are driven by technology. Bezos, Nadella, Pichai, Musk, Zuckerberg, and many others showcase the power of a technical strategist running technology-first businesses.
What about businesses that are not technology first? Most companies do not need a technical strategist as CEO. They need someone to fill the job function for each technical organization. For traditional businesses, the technical strategists help build the technology model and own the technical strategies aligned with their domain expertise.
Anything that creates value for the business needs a strategy, and individual technologies are no different. Companies need someone to own their data and AI strategies. Senior leaders need a partner to manage the technical complexity and build frameworks for leadership to manage value creation.
Marketing Strategists to Supply Chain Strategists combine domain and strategy expertise to help the business succeed. Technical strategy is no different. For data and AI strategies, Data Scientist Strategists bring specialized knowledge and strategic expertise to the role.
Data Scientist Strategists implement business strategy by:
Partnering with senior leadership to define data and AI strategies in support of business goals.
Integrate data and AI initiatives into the transformation roadmap and timeline.
Identifying opportunities to leverage the technologies for competitive advantages, growth, and margin preservation.
Partnering with other business units to create alignment between data and AI strategies and their goals.
Measuring data and AI initiative progress and outcomes.
Creating a connection between the business and data organization centered on value creation.
Strategy must drive technology, and business strategy must drive technical strategy. Someone must own data and AI strategies, or the data organization will be detached from value creation and business needs.