SILOED! Data vs Product! ??
Mohammed Brueckner
Strategic IT-Business Interface Specialist | Microsoft Cloud Technologies Advocate | Cloud Computing, Enterprise Architecture
Our drive to build ever-more-complex systems for digital transformation is hampered by a classic anti-pattern: organizational silos. These silos, reflected in our system architecture, impede customer value delivery. They stem from our habit of structuring teams by domain, not by customer value flow.
As Mel Conway so astutely observed, "organizations which design systems ... are constrained to produce designs which are copies of the communication structures of these organizations". In other words, our system architecture is a mirror of our organizational structure. And when that structure is fragmented into silos, our systems suffer accordingly.
The consequences of these silos are far-reaching. They lead to a proliferation of hyper-specialized teams, each with their own technical expertise but often lacking in business and domain knowledge. They create a culture of fragmentation, where data is hoarded and access is restricted, rather than being treated as a product that can be leveraged across the organization. And they ultimately hinder our ability to innovate, as we struggle to integrate disparate systems and teams.
But inefficiency is just the tip of the iceberg. Silos are becoming a major roadblock on the digital transformation highway. As we pour resources into cutting-edge tech like cloud and AI, we're inadvertently creating fertile ground for new silos to sprout and solidify. Consider Microsoft's hefty investments in regional expansion, data centers, and cloud/AI initiatives (think $17 billion regionally between 2023 and 2024, with data center spending exceeding $9 billion!). Without careful planning, these investments could simply reinforce existing silos. It's high time we confront the silo stranglehold and break free.
In this article series, we'll explore the problem of silos in depth, and examine the strategies we can use to break them down and create a more integrated, customer-centric approach to system design.
This time around, we'll discuss:
Data Teams vs Product Teams - the heat is on! ??
In the AI gold rush, where our understanding (map) of the technology is constantly evolving (shifting land) and the field itself is rapidly changing, collaboration between Data and Product teams is the key to turning data into alchemical gold. Borrowing from Simon Wardley's concept, traditional roadmaps might not be as helpful here.
While AI integration is surging, it's a messy dance between ambition and practicality. Data and Product must forge a deep partnership, not just an agreement, to transform raw data into successful AI products. This requires a shared language, a Data & AI Product Mindset, and a culture of experimentation - the fuel for innovation and transformation.
Data and Product must forge a deep partnership, not just an agreement, to transform raw data into successful AI products.
Communication
AI collaboration faces hurdles. Data teams (meticulous cartographers) need time for accurate maps, while Product teams (intrepid explorers) race to market. This timing gap creates missed deadlines and disharmony.
Data values precision, Product prioritizes user impact. This difference breeds frustration and a sense of undervalued contributions.
Imagine two talented musicians playing different tunes. We need a symphony!
The fix: adaptation and understanding. Teams must step outside their silos and see each other's viewpoints. It's challenging, but essential for unlocking AI's potential.
The Rise of AI in the Workplace
AI is rapidly transforming the workplace, quietly weaving itself into the fabric of our work lives. It's not a fad, it's a revolution. AI automates tasks, boosts creativity, and pushes the boundaries of what's possible. (Compare: 2024 Work Trend Index Annual Report , thanks for pointing it out, Jack Rowbotham )
Generative AI is leading the charge, offering AI companions that write emails, generate reports, and even suggest strategies. This translates to increased productivity, a creativity boost, and happier employees.
Work is changing too. Roles are blurring, collaboration is increasing, and AI's data analysis is making decisions more informed and strategic.
Leaders are crucial. They need to champion AI, guide its adoption, and create a workforce comfortable with these new tools. Training and support are key to empowering employees and making them active participants in the AI revolution.
The Need for a Common Language and Mindset
Data and Product teams speak different languages (algorithms vs. user experience). This makes collaboration hard.
The solution is a multi-pronged attack:
Beyond language, there needs to be a shared mindset that blends data analysis with product vision. This is an ongoing process that adapts to the changing AI landscape.
Here are some successful examples of how a common language and mindset led to better AI products:
The Culture of Experimentation
Experimentation is the secret sauce that turns ideas into reality. This chapter dives into how a culture of experimentation fuels business transformation and the creation of mind-blowing Data & AI Products, where the limits of what's possible are constantly being shattered.
At the core of experimentation is the idea that innovation isn't a straight, predictable path, but a series of calculated risks and explorations. It's the guts to jump into the unknown, armed with guesses and a willingness to learn from the results, that lays the foundation for uncovering new ideas and breakthroughs.
A culture of experimentation also accepts that failure is inevitable, but not a roadblock, it's a stepping stone to success. Often, the seeds of success are planted after failed experiments. And teams gain valuable lessons that guide their next moves.
The bottom line - in IT Architecture Practitioner Terms
Our current siloed approach is hindering our ambitious AI projects. Data teams are hyper-focused on high-fidelity data (precision), while Product teams prioritize immediate market needs. This lack of collaboration between Architecture Development Principles (ADPs) and Business Architecture (BA) in the TOGAF ADM phases is creating a significant disconnect.
Here’s the brutal truth: Without a fundamental shift towards Integrated Information Infrastructure Architecture (IIIIA) – fostering collaboration across disciplines – our AI initiatives risk failure. Untapped data potential (untransmuted data) will lead to poorly designed products that miss the mark on user needs and fail to deliver the expected business value.
Facing the truth can be tough, but it's the first step to progress. How will we tackle these hurdles? Are we ready to shape the future or leave it to chance?
Learn more about the way of the IT Architect:
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CSM | Marketing Strategist | Scaling Mid-Market B2B & SaaS Through Personalized Marketing Outreach
6 个月Breaking down silos is essential for a unified approach in delivering customer value. Looking forward to the rest of the series.
Dedicated to Bringing People Together | Building Lasting Relationships with Clients and Candidates
6 个月Excited to see the transformation unfold! ?? Mohammed Brueckner
Principal Architect | B2B Digital Products, SAFe Program Management
6 个月Thank you Mo for this article, I enjoyed reading it, I completely share your point of view, I can think of an additional indirect cultural aspect resulting from organizational silos: they prevent employees from feeling connected to the larger business objective.