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This article was originally published on CIO.com by Metis Strategy Partner Chris Davis, Managing Director Tony Qamar, and Senior Associate Grace Cozier.
CIOs are under pressure to deliver on AI’s promise. Yet history shows that many digital transformations have underdelivered — not because of strategy, process or technology, but because of people. Without engaged, enabled and aligned employees, even the most promising AI initiatives will stall. Models may be built, but they won’t be trusted or adopted. Workflows may be reengineered, but without buy-in, they’ll sit unused.
At Metis Strategy, our work alongside technology leaders, driving some of the world’s most complex transformations, has resoundingly taught us that success is always people first. The same lessons apply today as organizations pursue AI-powered transformations: those that succeed put people and culture at the center. Culture shapes every element of the operating model — what decisions are made, how decisions are made, how resources are prioritized and how quickly the company adapts.
Despite strong intentions and significant investments, many transformations falter because leaders misjudge where the true risks lie. A common failure pattern is over-indexing on technology or process while neglecting people. New AI platforms or workflows are often launched with great fanfare, but without engagement, enablement and alignment, employees struggle to adapt and value goes unrealized.
As the ADKAR framework highlights, sustainable change requires more than a solution — it requires awareness, desire, knowledge, ability and reinforcement at the individual level.
Another pitfall occurs when transformations become process exercises rather than engines of business value. When initiatives are framed as IT-only or designed around governance for its own sake, they lose relevance and fail to inspire the commitment needed for lasting change.
Transformations also stall when priorities are scattered or poorly communicated. Without enterprise-wide alignment, programs become optional, teams are spread too thin and execution suffers. In these cases, even well-designed AI strategies cannot deliver impact.
Finally, cultural resistance or a lack of trust can quietly derail transformations. Employees who do not see how AI initiatives align with their own incentives may disengage, breeding fatigue and undermining momentum. Without trust in data, models and leadership, adoption falters no matter how advanced the technology.
For CIOs, avoiding these patterns is essential. The organizations thriving in the AI era are those that design transformations with people and culture at the center from the very beginning. Through our client work, we’ve identified four cultural elements that consistently shape transformation outcomes — and four actions leaders can take today to strengthen them.
Successful transformations align operating models with customer needs. Too often, companies define themselves as “sales-led” or “product-led” and allow that orientation to drive key decisions. While this can create enterprise clarity, it risks sidelining customers or alienating parts of the organization.
In contrast, enterprises that adopt a true customer-first approach build resilience and agility across functions. Their operating models align incentives, prioritize experiences and ensure teams move in step with market needs. As explored in Achieving product success: A five-step framework for customer-centric design, an article written by Metis Strategy’s own Andre Lopes de Carvalho, organizations that embed customer centricity into their design and delivery processes create more value and are better equipped to sustain transformation.
Revisit your mission, values and incentive structures. Ensure they reinforce a culture where the customer is the anchor for decision-making at every level and for every department.
Digital and AI transformations are demanding. Employees must adapt to new ways of working, acquire new skills and deliver against rising expectations — all while managing day-to-day responsibilities. Without meaningful investment in people, fatigue and disengagement set in, jeopardizing transformation success and putting millions of dollars of investment at risk.
Leading organizations counter this by embedding continuous learning into their cultures. Targeted learning and development (L&D) programs, aligned with transformation priorities, not only upskill employees but also strengthen commitment to shared goals. The best programs address both business outcomes and personal growth — answering “what’s in it for me” alongside company goals.
Provide protected time and resources for development. Tie L&D investments directly to transformation outcomes to ensure employees are motivated and equipped to deliver. Acknowledge that meaningful learning requires space — even during working hours.
Leadership sets direction, but decision-making structures determine speed. Transformations falter under traditional command-and-control models, where approvals and escalations slow progress. The fewer people responsible for making decisions, the slower progress will be made.
Our experience shows that pushing decisions closer to the problem accelerates execution, improves accountability and fosters innovation. Empowered teams make better, faster choices — particularly when leaders set clear guardrails and reinforce trust. A decision-making framework, for example, clarifies which choices can be made locally and which warrant escalation, enabling faster, distributed decision-making.
Reduce layers of approval and empower small, cross-functional teams with decision rights. Ensure leadership behaviors reinforce empowerment, not control.
No transformation follows a straight path. Markets shift, technologies evolve and customer expectations change. AI adoption in particular requires experimentation — testing models, validating outcomes and adapting quickly to advances. Thriving organizations build cultures of experimentation where teams are encouraged to test, learn and adapt at speed.
We’ve seen this approach succeed when companies create the time and structure for experimentation, whether through dedicated capacity, lightweight governance or innovation programs. Not all experiments can succeed, so rather than penalizing failures, promote a culture that encourages learning from them. The result is not just innovation but resilience — a workforce confident in navigating uncertainty. As Jamie Engstrom emphasized in Technology, talent and transformation at caterpillar, curiosity and collaboration ensure experimentation is both grounded in business value and disciplined execution.
Normalize test-and-learn practices across teams. Protect capacity for experimentation, ensure lessons are shared and reward adaptability as much as execution.
Every transformation involves new strategies, processes and technologies. What ultimately determines success or failure is whether leaders place people and, by extension, culture, at the center. People shape decision-making, innovation and execution; they are the connective tissue of the operating model. CIOs who recognize culture as a true differentiator are better positioned to navigate complexity, make effective decisions and deliver sustained impact.
Wolters Kluwer’s ongoing transformation from a traditional publishing business to a cloud-first, AI-driven enterprise illustrates how people and culture drive successful AI-powered transformations. When we spoke with CIO Mark Sherwood, he discussed how customer centricity is reinforced through business relationship managers (BRM) who bridge technology and business units, ensuring digital investments are aligned with customer and market needs.
Learning and development are emphasized as employees build new skills in cloud, AI and data governance, enabling them to adapt to rapidly evolving technologies. Distributed decision-making is strengthened by embedding BRMs to bring tech and business closer together, ensuring alignment and enabling smarter choices on priorities and resources. Agility is fueled by a test-and-learn mindset, with experimentation balanced by strong ethical AI practices and disciplined measurement.
By maintaining a strong culture and people-first approach, Wolters Kluwer has accelerated its evolution and positioned itself as a leader in delivering trusted, technology-enabled solutions across industries.
CIOs today face constant change — none more urgent than the rise of AI. Shifting customer expectations, new competitive threats and accelerating disruptions are magnified in the AI era. Anchoring transformation in people and culture is what turns volatility into opportunity and lasting advantage.
For CIOs, the mandate is clear: build operating models that not only work today but also adapt continuously for tomorrow. That requires embedding four cultural practices at the center of every digital and AI transformation:
When CIOs champion these actions, they move beyond technology deployment to transformation that is embraced, scaled and sustained. Put people first and digital and AI transformations become not only achievable, but sustainable.
This article was originally published on CIO.com by Metis Strategy Partner Chris Davis, and Senior Associate Andre Lopes.
Product management has become a critical competitive advantage as organizations shift from project-based delivery to product-centric operating models. Standing up dedicated product teams and reworking delivery processes are critical steps in this evolution, but many companies stall after these initial changes. Too often, product teams remain constrained by process-heavy ways of working that undermine the agility they’re aiming for.
Traditional product models often prioritize process adherence and delivery velocity, sometimes at the expense of insight and adaptability. In contrast, intelligence-enabled product models use real-time data and AI tools to inform decisions, prioritize tasks effectively and continually improve outcomes.
Product intelligence transforms how product teams operate day-to-day. By embedding real-time data and predictive insights into workflows, teams can shift from calendar-driven delivery cycles to adaptive, insight-led decisions. AI-powered tools surface trends, flag risks and automate repetitive tasks, freeing up teams to reinvest time in strategic discovery, cross-functional collaboration and outcome refinement.
AI Agents, when deliberately embedded, become operational collaborators. They help teams analyze customer behavior, predict needs, optimize roadmaps and automate routine management tasks. This frees human teams to focus on innovation and value creation. By operationalizing product intelligence, organizations empower their product teams to make smarter decisions, iterate faster and deliver outcomes more consistently.
For technology leaders, the message is clear: moving to a product model is only the beginning. Building intelligence-enabled product teams is now essential to delivering sustained value at scale.
Before exploring how product intelligence can transform organizations, it’s important to recognize the limitations of traditional product management.
Originally developed at Procter & Gamble in the 1930s to manage physical product lines, Product Management evolved into a structured function designed to represent customer needs and coordinate cross-functional teams. While modern product management emphasizes customer-centricity and data-informed decisions, it often defaults to process management instead of value delivery in practice. While this model was effective for managing physical products real-time digital landscape is defined by continuous customer feedback, rapid iteration cycles and the need for real-time decision-making. Meeting these demands requires greater adaptability, integrated intelligence and faster response across product teams. Although product intelligence has gained the most traction in digital product environments, its principles are increasingly relevant to physical products as well, especially those enhanced through connectivity, embedded software or data-generating sensors.
Agile standups, roadmaps and delivery rituals have become the focus, not the customer outcomes they’re meant to drive. Teams operate in silos, working from disconnected data streams that limit insight-driven decisions. Delivery velocity is prioritized over outcome quality, leading to feature releases without meaningful customer or business impact. Feedback loops are slow and fragmented, and discovery efforts are frequently bypassed to meet delivery deadlines. For example, studies show that up to 60% of product teams regularly skip or compress discovery due to delivery pressure, resulting in lower adoption and misaligned priorities (Productboard, 2023)
The result: incremental improvements, low user adoption and limited value creation. Legacy mindsets and rituals can create the illusion of control. But without connected intelligence, they rarely lead to meaningful momentum or measurable value.
Market leaders are moving beyond incremental change by integrating intelligence into every decision layer. Amazon processes 150 million customer interactions daily to inform product decisions. Netflix’s AI-driven approach generates $1 billion annually in retention value.
Product intelligence transforms how Fortune 500 companies build and scale products. It is the systematic integration of real-time customer data, predictive analytics and AI automation into every product decision, from initial concept to post-launch optimization. The result is products that adapt to market demands in real time, not quarters. For executives, this means shortening time to value, increasing customer lifetime value and reducing the cost of poor product market fit. Rather than replacing human decision making, this strategy leverages AI Agents as embedded assistants, helping teams prioritize work, identify opportunities and automate routine tasks so they can focus on solving real problems. These AI Agents are not standalone bots. Rather, they are embedded systems and tools that support product teams with real-time analytics, insight generation and intelligent automation.
Product intelligence brings three practical shifts:
Products must be built as continuous feedback systems from day one. Telemetry, behavioral data and customer signals should flow seamlessly into product workflows, providing teams with the live intelligence needed to prioritize work, refine roadmaps and respond to user needs dynamically. For example, a SaaS company started instrumenting its onboarding flow with telemetry, revealing where users dropped off within the first minute of interaction. This insight led to a refined user experience that improved activation by ~25% over the next three months.
Where traditional teams optimize based on periodic reviews and lagging metrics, AI-first product organizations enable continuous optimization. AI Agents analyze real-time data streams to guide dynamic backlog adjustments, identify emerging opportunities and automate routine prioritization, turning reactive planning cycles into proactive, adaptive operations. One enterprise product team uses an AI anomaly detection tool to flag unusual drops in engagement within hours, triggering real-time hypothesis testing and backlog reprioritization, removing the need to wait for quarterly product reviews.
AI Agents function as virtual team members, automating reporting, generating recommendations and handling operational tasks like backlog grooming, performance analysis and opportunity scoring. Rather than relying solely on human analysis, teams collaborate with embedded AI Agents to make faster, smarter decisions throughout the product lifecycle. In practice, some teams deploy AI Assistants to monitor product metrics and auto-generate weekly status summaries, saving several hours of reporting each sprint.
By adopting product Intelligence, organizations enable their teams to move beyond static roadmaps and reactive processes, combining human creativity with data-driven support to deliver sustained product value.
For product intelligence to deliver impact, technology leaders must focus on practical implementation. The following four-step framework provides a roadmap for embedding intelligence across product operations:
Ensure that every product team consistently captures usage data. Mandate telemetry instrumentation from the ground up, and consolidate this data in shared, accessible platforms. This foundational step eliminates silos and ensures that product teams have the necessary insights to guide decision-making.
Example:A SaaS product team instrumented its onboarding flow to track where users dropped off. The data revealed a significant drop at a specific step, prompting a UX redesign.
Impact:Improved activation by 25% in one sprint and created a replicable process for funnel optimization.
Rather than relying solely on centralized analytics or adding more analysts to squads, equip every product team with embedded AI tools. These tools should analyze telemetry data, surface actionable insights and automate operational tasks like backlog grooming and opportunity scoring. Teams should treat these AI tools as integrated assistants that enhance productivity, enabling human team members to focus on strategic work and creative problem-solving.
Example:An engineering team deployed an internal machine learning system to automatically assign support tickets based on topic and severity. The system resolved a substantial portion of tickets without human intervention.
Impact:Reduced resolution time by 21% and automated over 30% of incoming tickets with 75% accuracy.
Evolve beyond static, feature-driven roadmaps. Leverage AI tools to monitor real-time performance data and customer signals, using this intelligence to dynamically adjust priorities. Link investment decisions and resource allocation to outcome-based metrics, ensuring that product planning remains responsive and aligned to business goals.
Example:A consumer tech company used AI-driven analytics to continuously monitor user behavior. When engagement dropped, the team dynamically adjusted roadmap priorities in real time.
Impact:Enabled mid-cycle roadmap changes that improved retention without waiting for quarterly reviews.
Position AI capabilities as core infrastructure, not as standalone tools. AI-enabled insights, automation and data pipelines should be integrated into your product development ecosystem, on par with cloud platforms and engineering environments. This ensures that intelligence is embedded directly into daily operations, supporting sustained value delivery at scale.
Example:A software team implemented an AI engine to automatically analyze their backlog and flag high-risk technical debt. They used it to prioritize and reduce aged tickets.
Impact:Accelerated backlog cleanup and reduced risk from unresolved issues through proactive triage.
By following this framework, technology leaders can systematically integrate product intelligence into their operating models, empowering their teams to make smarter decisions, accelerate iteration and drive continuous product value. These four moves are not standalone initiatives; they form a mutually reinforcing system. Data instrumentation enables insight. Embedded AI enables decision velocity. Roadmap evolution and infrastructure maturity ensure durability at scale. When fully embedded, this model reduces decision latency, accelerates feedback loops and improves alignment between user needs and delivery outcomes.
C-level executives driving product intelligence transformations share three non-negotiable mindset shifts. Leaders who resist these changes watch their organizations lose market share to more adaptive competitors.
First, leaders must move from building more to building smarter. Traditional metrics like feature velocity or backlog completion create the illusion of progress without guaranteeing value. Product intelligence reframes the goal: every build decision should be informed by real-time intelligence and tied to measurable outcomes.
Second, shift from managing backlog velocity to managing product intelligence as a strategic asset. The data, insights and AI-powered recommendations should be treated as foundational infrastructure to enable, not an afterthought. Managing the flow and use of intelligence across product teams becomes as important as managing the features themselves. As one CIO described it, “We used to celebrate velocity. Now we celebrate validated learning.”
Finally, move from supporting isolated product teams to cultivating an insights-driven, enterprise-wide product culture. Product teams must evolve from isolated builders into learning engines. These teams should operate within an intelligence-driven organization where shared dashboards, cross-team data access and structured learning loops turn every product into a source of insight for the rest of the business.
These shifts require intentional leadership. Technology executives must actively challenge legacy thinking and embed intelligence into the organizational fabric, not just within product teams, but across the entire enterprise.
For technology leaders, the imperative is clear: redefining how your organization builds isn’t about discarding product management, it’s about evolving it. The move to product-centric operating models was a necessary first step. But in today’s environment, it’s no longer sufficient.
Product intelligence represents the future of building. AI Agents, real-time data and continuous optimization are not enhancements; they are the infrastructure upon which adaptive, customer-centric organizations are built. Intelligence must become the core operating system of your product function.
The organizations that lead in this next era will be those that treat product intelligence not as a competitive advantage, but as a foundational requirement for digital competitiveness. Technology leaders must act now. That means embedding intelligence across their teams, redefining their operating models and preparing their organizations to build not just faster, but smarter. Of course, this shift will not happen all at once. It requires thoughtful, phased investment, talent upskilling and a culture that embraces data-informed collaboration.
This journey begins with four foundational moves: instrumenting products with data, embedding intelligence in every team, enabling real-time roadmaps and treating AI as part of your operating infrastructure.
Product intelligence isn’t optional. It’s the foundation for sustained digital relevance and market leadership. The organizations that embrace intelligence now won’t just move faster. They will set the pace for how their industries innovate, grow and lead.
For those ready to begin, start with a single product team. Equip them with telemetry tools and a lightweight AI assistant to analyze usage data and automate backlog triage. Use that experience to shape a broader rollout.
This article was published originally on CIO.com by Metis Strategy Partners Michael Bertha and Chris Davis.
We have helped dozens of organizations make the leap from project to product. Most of the time, the new product teams launch with great fanfare. They’re cross-functional. Agile. Empowered. They’ve got the right tools and a healthy backlog. It feels like all the ingredients are there for the best meal of their lives.
But eventually, they run into the same old bottleneck: the funding model. Even after they’ve shifted the operating model, funding remains stuck in the past. Product teams are still forced to navigate the approval gauntlet every time scope shifts or priorities evolve, undermining the autonomy and agility that was promised in the first place.
Time and again, we hear from digital and IT leaders that this is the biggest missing piece. They know the funding model is the least mature, least understood element of the product operating model, and maybe the biggest unlock of all.
They tell us they plan annually, even though work gets delivered in sprints. They say budgets are fixed at the start of the year and don’t evolve with the business. They love the idea of investing in capabilities and outcomes, but still plan as if every move is a one-off. And from a finance lens, they struggle to quantify total cost of ownership or link spending to actual results.
Sound familiar?
The biggest challenge with modernizing the funding model usually isn’t the concept, it’s the org chart. Finance still holds the keys…which is ironic, because the finance team, the one that always asks tech to speak the language of the business, is the one group that stands to benefit most. Maybe they just need it explained in their own terms.
Shifting the funding model is no different than shifting the operating model. Just like product teams are oriented around business capabilities and value delivery, financial models should be too.
That means no more budgets sliced by platform, middleware or tool. No more endless line items for niche SaaS products or integration frameworks that only obfuscate true value.
Instead, imagine a clean, business-facing view: a single budget line for each capability — commerce, fulfillment, billing — with one dollar figure representing all labor, tools and infrastructure required to deliver that capability. Each one paired with the business metrics it’s meant to move.
Now you’re not defending the cost of seventeen tools. You’re talking about the total cost of ownership for your fulfillment capability. And you’re evaluating that investment against improvements in delivery time or customer satisfaction. In short, you’re finally speaking finance’s language.
So, how do you get there?
First, define the shape of your portfolio. Many of our clients target an 80/20 split: 80% of the work is driven by perpetually funded product teams. These teams manage their backlogs and sequence work based on business needs across run, grow and transform domains. The remaining 20% covers traditional projects — things like ERP overhauls or standing up entirely new business models — which will still require standard business-case funding.
Our focus here is the 80%.
The next step is to calculate total cost of ownership for each product team. Think of it like activity-based costing: you’re mapping inputs and processes back to outcomes.
We always recommend starting small. Begin with direct costs — internal labor, contractors and product-specific software — those items that easily map to only one product team. Then, as you mature, layer in shared tools, infrastructure, travel and services. At the most advanced stage, you might include shared functions like cybersecurity, cloud, end-user services, telecom and even the business and functional resources contributing to the tech agenda — though, like any overhead mapping exercise, it gets political fast.
For anything you can’t allocate, put it in a tech overhead category, or assign it proportionally across product teams based on headcount. Don’t let perfection get in the way of progress.
Once you’ve defined your allocation model, you’re ready to begin what we typically see as a three-year journey. Why three years? Because the old ways of working are so entrenched that even a crawl, walk, run is likely to be a hard pill to swallow. Also, the launch of a new funding model will typically need to align with your annual budget cycle.
With a portfolio view and allocation approach in hand, start modeling your new financials — at least on paper.
Build a simple version of your budget with three sections: product teams (80%), initiatives (20%) and tech overhead (if you didn’t choose to allocate it back to the products). For each product line, include the total cost of ownership and the business metrics the team is driving. Show this quarterly. Share it with your tech leaders and finance partners. Get them used to seeing costs this way and using it to influence decisions on capacity and prioritization.
The goal in year one is not to overhaul the funding process, it’s to shift the mindset. To move from a view of “what we’re spending on marketing software and data” to “what we’re investing in customer acquisition.” Importantly, in this phase, you’re only allocating IT costs. Functional or business-side resources embedded in teams stay in their existing cost centers for now.
Now that you’ve got TCO in place, plus strategic roadmaps and metrics co-authored with your business partners, you’re ready for the next step.
Each product team goes into annual planning as a “project” — because that’s the language finance still speaks. Tactically, the project is called by the capability or product name: “improve customer acquisition,” as opposed to the “integrate third-party data for personalization project.” That detail may be one opportunity on your roadmap, but it is a means to an end and subject to change. This gets you the full year’s funding in one go. No mid-year scrambling. No fits and starts. Your teams can flex scope as needed and prioritize outcomes, not just output.
Governance becomes much simpler. The 80% of work that lives in product teams gets approved up front and measured by results. The 20% continues through traditional review. Remember, the up-front funding of product teams is a privilege. It can be revoked if, for example, the benefits do not justify the TCO for multiple quarters.
You can stop here. This is a huge win. You’ve modernized the way product teams are funded, made life easier for finance and created a more coherent conversation about value. But if you want the full unlock — if you want end-to-end clarity — there’s one more step.
The final stage is full cost transparency at the enterprise level.
That means allocating business and functional spend to the same product capabilities. It means cost-center owners are charged based on their contribution to product teams. It means embedding this allocation logic into your general ledger to automate reporting and show real-time ROI. For example, the “improve customer acquisition” team will include software, infrastructure, data, IT labor, marketing team labor, licensing for imagery used in marketing content, digital ad spend and other related costs. The metrics are “cost of customer acquisition” and “marketing qualified leads.” The engineer on the team knows the status of these metrics daily — because the business functional and technical resources are working toward shared outcomes — not just outputs.
Done right, this is the holy grail: product-level TCO, enterprise-wide. Side-by-side with metrics. Always up to date. Always tied to outcomes.
Product operating models are built on autonomy, speed and shared ownership. But those ideals don’t reach their full potential if legacy funding models force teams to ask permission every time priorities shift.
If you’ve already evolved the way your teams work, it’s time to evolve the way they’re funded.
Because the ability to modernize your funding model, especially in a way that brings finance along, isn’t just a win for IT. It’s a signal that you’re ready to drive transformation across the business.
Adobe, Equinix, Lenovo, and G-P share the five difference makers that will help companies successfully harness global talent to compete at speed and scale.
This article was originally published on CIO.com by Chris Davis, Partner at Metis Strategy and Kelley Dougherty, Associate at Metis Strategy
To succeed as a large, global company, there is no choice but to harness the power of technology talent around the world. There simply aren’t enough people with the right skills, and at the right cost within a single location, to support the innovation and operational demands of a modern organization.
Organizations have implemented a variety of workforce models over the last two decades or so, but each has eventually proved to leave them with more questions than answers. The global outsourcing trend of the 1990s and early 2000s addressed capacity restraints through low-cost labor markets, but the lack of ownership and the transaction-based working relationships meant organizations outsourced not just the work, but also the accountability. The early 2010’s practice of co-locating talent supercharged collaboration, but also limited organizations’ ability to scale with a workforce based in high-density, cost-prohibitive metros. By 2020, many technology leaders began revisiting the idea of building dedicated, employee-based teams in lower-cost global locations, but the remote workforce model experienced its own set of challenges throughout the Covid-19 pandemic.
Fast forward to today. Many global technology leaders continue to struggle to find a balance between cost efficiencies, team productivity, and the human aspects of employment. However, there were a few pioneers that preemptively and effectively prepared for a distributed remote workforce and therefore were able to flourish at a time when others scrambled to adjust to the new normal.
Adobe, Equinix, Lenovo, and G-P were strategically situated and equipped to achieve the ideal duality: leverage global talent to realize cost efficiencies and realize the effectiveness of an agile team, yet in a distributed operating model. The key design principle that they all share is this:
To make global, distributed teams successful, they established dedicated decision-making power in distributed locations with full-stack teams of business and technology employees that can autonomously deliver end-to-end value.
The technology and HR leaders at each of these organizations shared their insights into building successful global teams that can sustainably drive innovation at scale. They highlighted five themes that can make or break the development of a global operating model:
We will explore each of these below:
Implementing operating structures that leverage distributed decision-making in the context of a coordinated strategy requires cross-functional credibility and finesse. The ability to drive strategy across several departments or teams with varying functions and skill sets is a rare talent, and leadership needs to build this muscle to ensure distributed teams are aligned for execution.
Art Hu and Jeanne Bauer-Hamlett, Chief Information Officer and Executive Director of Human Resources at Lenovo, emphasize the importance of equipping and empowering teams for success. Leaders need not only to identify the appropriate talent to manage and own decisions within global teams, but also to regularly engage with local managers to maintain strategic and process alignment. Middle management will ultimately be the make-or-break layer of the operating structure, so leaders need to ensure they are able to:
Leading global teams also comes with the inherent challenge of limited in-person interactions and face-to-face communication. Technology leaders therefore need to be particularly adept at building trust between themselves and dispersed teams.
“You’ve got to be good with the data, but you better have the emotional intelligence to match it,” says Richa Gupta, CHRO at G-P. This includes displays of empathy, authenticity, and concerted efforts to build human connection, either in-person or virtually.
Leaders should veer on the side of over-communication to break down emotional barriers and establish a sense of transparency across locations and management layers. Adobe CIO Cindy Stoddard explains that she makes it a habit to foster connections, both virtual and in person, throughout Adobe’s IT teams.
Likewise, Milind Wagle, CIO at Equinix, notes that he makes deliberate efforts to visit each of the company’s global teams at least twice a year to alleviate “emotional” distance with his reports and ensure each location feels valued and connected to the organization.
The value of building full-stack global teams is largely driven by the knowledge retention and improved delivery resulting from a sense of organizational identity within teams and the long-term commitment to the organization.
“Prioritizing culture among the leadership team is crucial, as a company’s culture starts at the top and is carried down to employees,” Hu said. Leaders should approach culture as an internal capability that needs to be actively maintained, measured, and nurtured from the top down.
For example, we worked with a retail company that built a nearshore development center in Mexico to maintain time zone alignment while taking advantage of 2-3x cost savings. While onboarding 50 new developers, U.S. team members flew to Queretaro for cross-location agile product operating model training. The team leader in Mexico took the time to educate employees on both Mexican and American business culture, and encouraged empathy and open dialogue between team members and segments of the IT organization. Doing so helped build cultural understanding and trust at the onset of the working relationship.
Indeed, organizations establishing globally distributed teams need to understand and navigate the business and cultural distances that may cause friction across teams and stakeholder groups. Rather than attempt to enforce a blanket uniformity across all offices, technology leaders should aim to strike a balance between promoting a common sense of organizational identity and celebrating the local cultures and customs of each team.
Establishing and maintaining effective employee feedback loops is an essential aspect of promoting a positive workplace culture. Art and Jean explain how they have made intentional efforts at Lenovo to ensure feedback loops to regularly measure employee experience and identify pain points. They note the importance of using outcomes, rather than outputs or behaviors for those measurements. For example, employees at Lenovo are evaluated based on the specific value or outcomes they deliver rather than the amount of time spent online or at a desk.
The same logic can be applied to measuring company cultural efforts. Rather than analyzing the number of diversity workshops or social events scheduled in a given period, leaders should instead assess outcome-based metrics such as:
Initial launches of a global operating model are typically designed with “decision makers” (Product Managers, Business System Analysts, senior Tech leads, and Business Stakeholders) based in the U.S., with Scrum Masters and engineering teams in the alternative location. While this can work, leading organizations tap global talent to build truly global solutions.
For example, one organization stood up a full data platform team, including product management, Scrum Masters, and engineering in India. This was not a subservient team that took orders from the U.S.; they were fully empowered to build the global product for all users.
Another organization built out a business-unit-aligned supply chain team in Brazil to best serve South America. A third built out a team in Singapore to support finance operations and then structured their business product management in the same location to align time zones. In each case, the big shift was allowing these teams to define the strategy, develop the product, launch, and operate it no differently than a team in the U.S.
The appropriate tactical model for each organization will be dependent on the specific needs and responsibilities of teams. For example, Hu notes that “a ‘follow-the-sun’ model can and does work for teams who have well-defined tasks, boundaries, and hand-off protocols.” In contrast, a team that has heavy dependencies and requires more centralized oversight and direction will be better suited to a setup that allows for more time-zone crossover with other teams, or fully accountable teams staffed within the same time zone.
Wagle of Equinix notes the importance of communication within a global operating model, but also emphasizes that cross-team communication does not necessarily need to be more frequent or within a specific forum. Communication should instead be optimized to provide the most time value for teams. Equinix moved away from daily scrum meetings in favor of weekly meetings with daily asynchronous check-ins to reduce meeting exhaustion and allow more time to work on key objectives. Technology leaders should ensure the proper cadences are established for strategic decision-makers and cross-functional teams to discuss key topics such as:
A technology operating model built on agile practices and consistent delivery processes enables teams to reduce operational redundancies, cross-team friction, and internal costs. Stoddard at Adobe describes improving business workflows as “a strategic investment” and notes that her organization focused on establishing systems that “create positive employee and customer experiences in the hybrid world, drive efficiency and productivity, and enable standardization, optimization and consolidation.”
With standard ways of working in place, technology leaders need to define where and how decision-making rights are delegated. The digital-first, hyper-connected nature of the modern workplace means people no longer need to be in a company headquarters to have influence, but organizations need to be intentional about which decisions are delegated to local teams. Technology leaders should have a defined architecture of decision-making rights that enable teams to work asynchronously and deliver autonomous value while ensuring those teams are working harmoniously toward enterprise-level strategic objectives.
“Technology is no longer just about enabling work, it’s the workplace itself,” said G-P’s Richa. Leaders establishing a digital operating model built on distributed teams need to ensure the appropriate tools and systems are in place to support it.
The most fundamental technologies are those enabling a unified and streamlined employee experience, giving teams the day-to-day resources and support they need to do their job. Delivery and project management tools that can be shared across locations will enable teams to have visibility across efforts, monitor risks, and identify dependencies without daily facetime. Milind provides the example of Equinix’s rollout of ‘Operation Collaboration’ that is geared toward maturing the organization’s workplace technologies and meeting experience platform to enable teams to work asynchronously.
Each of these companies above has also invested in technologies to streamline internal processes and reduce the operational risks of distributed teams. Cindy at Adobe advises that by investing in digitization, technology leaders “can help their organizations make the most of data analytics and insights, unlock new business and revenue opportunities, and significantly reduce costs.” These organizations also made strategic pushes to leverage AI and automation to minimize repetitive tasks, reduce time costs, optimize resource utilization, and allow teams to access services and support regardless of location or time zone. Lenovo in particular launched its Premier Support Plus, which “combines AI and human interaction for proactive, predictive, seamless and direct IT support, designed specifically for today’s hybrid workforce.”
The regulatory environment in each team location is the final, and potentially consequential, consideration of a workforce strategy. Among the standard regulatory concerns are those regarding the local labor and employment laws in a given location. Richa at G-P says that establishing a foreign entity and managing local administrative tasks is both costly and time-consuming, and advises that technology leaders work with internal or outsourced HR and legal experts to ascertain the compliance requirements around legal entities, taxes, compensation, benefits, workers rights, and the ability to hire and fire, among others.
The second facet of compliance is more closely aligned to a technology leader’s purview and pertains to the local data, privacy, and intellectual property regulations. Some regions could differ in their approach to data sovereignty and IP protection, so organizations may weigh privacy concerns when determining where and how to store sensitive information. Art at Lenovo advises that leaders have “full awareness of the laws and regulations, and make sure global teams have the tools and processes to adapt to the rapidly changing landscape.”
For organizations contemplating building a global technology operating model, the final big decision is whether your company is willing to truly change its mindset. There is a big difference between a “U.S.-based company that operates internationally” and a “global company that happens to be headquartered in the U.S.”
Not all companies will be ready for it. But, in our view, there is no other option to realize both efficiency and effectiveness in your operating model. Whether proactively or reactively, global companies will have to retool the way they work across these five dimensions to sustainably leverage global talent at scale.
How leaders can drive the coveted project-to-product transformation
This article was originally published on CIO.com by Chris Davis, Partner at Metis Strategy, and Kelley Dougherty, Associate at Metis Strategy.
In this time of fluid markets, fierce competition, and constant disruption, the modern enterprise must stay innovative and agile. It must be ready to evolve at any moment, and deliver quickly, consistently, and reliably through its large-scale software operations.
But it can hardly do so through traditional, monolithic ways of working, particularly those organized around projects. Many companies are therefore reorienting their operating models around end-to-end products. Done well, these transformations make a company nimble. Done poorly, they exhaust the organization and produce little value.
Leaders must transform their organizations methodically along a path that minimizes redundancies, builds momentum, and creates immediate and tangible business value. In this article, we outline the steps to start a product operating model journey, coloring the steps with stories told on the Metis Strategy Podcast by executives from companies like Ascension, Condé Nast, and Hyatt.
First, leaders must identify the products around which their operating model will be designed. We define a “product” in this context as:
“a capability or portion of a capability, brought to life through technology, business process, and customer experience, with a continuous value stream, and an ability to measure success independently.”
Therefore, leaders should draw the capability map of their business, showing how value streams and assets are positioned, how they relate to each other, and which of them are immature or missing. These capabilities can then be translated into end-to-end products calibrating for the organization’s size, offerings, and business model.
If an organization has uniform customer offerings and go-to-market motions, then its products should be aligned to the company’s value chain. Such is the case at Ascension, as explained by its Chief Marketing and Digital Experience Officer, Raj Mohan: “We’ve organized our teams particularly broken up by the consumer journey into product teams down that path, and then staff those teams along those journeys itself.”
In practice, products aligned to a customer-facing value-chain might include: Development → Marketing → Sales/Order Management → Fulfillment → Customer Success
Aligned to internal value streams, they might include Financial management, HR management, Legal Management, IT Management, Facilities Management, and Data and Analytics.
In contrast, if an organization has multiple business units, offerings, or go-to-market processes, its products must be defined so they account for each BU’s customers, geographies, and so on. This way, products can still be aligned to value chains but also arranged into broader groups, lines, and teams, each constituting a “deeper” aspect of the value chain.
This is how products have been defined at Condé Nast.
Sanjay Bhakta, Chief Product and Technology Officer at Condé Nast explains that his organization’s product offerings result in them having “some capability within the brands, especially the big brands, that focus on things that may be bespoke or have specific requirements.”
Next, leaders must define the capabilities around which they’ll organize resources and configure the product teams such that they can deliver value autonomously. Mohan suggests that a product team can stand on its own “if, over at least a three-year horizon, you can see clearly that a durable team can bring value that you can sign up for.”
How many product teams should you have? As a rule of thumb: about one tenth as many employees as there are in the organization. Ideally, each product team should comprise seven to nine people, and they should include a product manager, scrum lead, technical lead, and engineers. These might be supplemented by user experience leads for consumer products, other engineers, shared services, or specialists.
A project-to-product transformation requires that an enterprise think first in terms of products, and this shift hangs on the structures and processes by which the company manages its portfolio. A company should organize its portfolio around the outcomes it seeks, and those should in turn dictate the capabilities initially staffed to mature at a higher rate. When resources are limited, start by productizing 2-5 key areas, do it well, and scale from there.
Hyatt, for example, has organized its portfolio around customer-focused capabilities, and so has caused the enterprise at large to think in terms of customer outcomes. As Hyatt’s Global CIO, Eben Hewitt, has explained: “Moving to a product mindset, to me, means, number one, it’s for a customer… You’re thinking about the outcomes that people want.”
Further, an organization will do well to manage its portfolio according to Agile principles and to align its product teams to business outcomes. Not only will product teams then naturally align to each other and their shared objectives; the organization itself will think in terms of products and outcomes.
To manage portfolio by capabilities, use annual planning sessions to craft roadmaps aligned to outcomes and segmented by capability. Such roadmaps can then inform the teams who support those capabilities, and ensure their own roadmaps align to enterprise objectives. These planning sessions also give leaders a chance to decide how to allocate funds. As a rule, the product teams should receive roughly 80% of the organization’s budget, and that allocation should cover their needs end to end to build and manage the lifecycle of the product. The remaining 20% should go toward broader initiatives.
Adopting an Agile mindset and common ways of working early in the journey will help reorient a company reliant on waterfall, project-based operating models towards continuously delivering value. However, frameworks such as Scrum and Kanban are a means to an end. Some organizations conflate a “product” transformation with an “Agile transformation,” and lose themselves in the minutia of adhering to specific ‘rules’ and ceremonies. The key is to create a baseline for teams to form, storm, and norm by reducing confusion of how to transition from a rigid waterfall process to a mindset in which an entire agile product team establishes a shared identity founded in the problem the product solves; not their title or role on a waterfall assembly line.
Bhakta emphasizes that Agile should extend to the relationship between product and engineering. He explains: “[It] helps us do faster decision-making, helps us to get products out into the market faster.”
If organizations are already practicing Agile when they start transforming, then they should focus on infusing into their processes the product mindset. If an organization isn’t so mature, however, then it should train teams on core Agile practices to which they can align their processes.
Ultimately, this transformation largely depends on whether people can successfully serve the role as a Product Manager, and balance the business value, viability, usability, and feasibility to focus teams on shipping products and experiences that users love, adopt, and help improve with feedback.
Therefore, each team needs a Product Manager, who can:
Identifying, training, and upskilling Product Managers, especially for internal products, is often the hardest part of the journey. But to be successful, Product Managers must also have clear scopes of responsibility, the power to execute on them, and feedback loops by which they can measure performance and course-correct.
Each of the steps we’ve covered critically enable teams to scale, and once they’ve been carried out the first time, they tend to act as a flywheel, sustaining themselves with their own momentum and creating excitement within the organization to productize more capabilities.
To gauge success of your product operating model journey, start by:
The journey of maturing a product team is never really complete. Once the teams are launched with the steps outlined in this article, leaders should then do the following at scale, working team by team:
It is our firm belief that adopting a product operating model is the only way to successfully support a scaling organization. But don’t take it lightly; this is a commitment that requires leaders to dedicate at least a year of their time to successfully transform an organization’s mindset.
Personalized customer experiences, automated business operations, and data science-driven insights all depend on the quality and volume of your data. That’s why your data privacy strategy must be more than a policy on ethics.
This article was originally published on CIO.com by Chris Davis, Partner at Metis Strategy and Elizabeth Tse, Associate at Metis Strategy.
Companies continue to face implementation challenges as they rush to comply with data privacy regulations such as Europe’s General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). This is due largely to a mismatch between their management of data and the stringent requirements set by the regulations.
Organizations can address the complexities of privacy regulations via a well-defined data governance framework, which leverages people, processes and technologies to establish standards for data access, management and use. Such a framework also enables companies to address elements of privacy, including identity and access management, consent management and policy definition.
As leaders implement data governance models with privacy in mind, they may face challenges, including lukewarm executive buy-in, lack of a cohesive data strategy, or diverging opinions about how data should be used and handled. To address these obstacles, leaders should consider the following actions:
While a Chief Data Officer or CIO may lead the implementation of a data governance framework or model, data governance should be a shared responsibility across a company. At a minimum, the IT department, privacy office, security organization, and various business divisions should be involved, as each has an important stake in data management. Bringing in a variety of stakeholders early allows firms to establish key data objectives and a broader data governance vision. This collaboration can take the form of a dedicated task force or may involve regular reporting on data governance and privacy objectives to the executive board.
Data privacy, similarly, is also a shared responsibility. All employees have a part to play in maintaining data privacy by following accepted standards for data collection, use and sharing. Indeed, implementing a successful data governance model with privacy in mind requires educating employees on governance concepts, roles and responsibilities, as well as data privacy concepts and regulations (e.g. the definition of “personal information” vs. “consumer information”).
After establishing a governance vision and driving employee awareness, organizations can define their desired data governance roles – such as data owners, data stewards, data architects and data consumers – and tailor the roles to their needs. Some companies may distinguish between data stewards and data owners, for example, with the former responsible for executing daily data operations and the latter responsible for data policy definition. For one client with a large and complex IT department, Metis Strategy established a governance hierarchy with an executive-level board, combined data steward/owner roles, and other positions (e.g. data quality custodians). This structure facilitated ease of communication and enabled the client to scale its data management practices.
In the long term, firms should incorporate data governance and management skills into their talent strategy and workforce planning. Given the expertise required and the shortage of qualified people for some data-intensive roles, organizations can consider enlisting the help of talent-sourcing firms while focusing internal efforts on talent retention and upskilling. As companies’ strategic goals and regulatory requirements change, they should remain flexible in adjusting their data governance roles and ownership.
To respond adequately to consumer privacy-related requests for data, organizations should establish standardized procedures and policies across the data lifecycle. This will allow companies to understand what data they collect, use and share, and how those practices relate to consumers.
For example, the CCPA provides consumers with the right to opt out of having their personal information sold to third parties. If a retailer needed to comply with such a request, it would need to be able to answer questions in the following categories:
Establishing policies and standards for the above can help organizations quickly determine the actions needed to respond to customer requests under privacy regulations. Companies should communicate policies widely and ensure that they are being followed, as failing to do so can propagate the use of inconsistent templates and practices. At one Metis Strategy client, for example, few stakeholders had sufficient awareness of data management and access standards, despite the fact that the client’s IT department had established extensive policies around them.
To successfully implement data governance frameworks and ensure privacy compliance, firms may also need to address challenges posed by legacy infrastructure and technical debt. For example, data often is stored in silos throughout an organization, making it difficult to appropriately identify the source of any data privacy issues and promptly respond to consumers or regulatory authorities.
Firms also need to evaluate the security and privacy risks posed by outsourced cloud services, such as cloud-based data lakes. Those using multiple cloud providers may want to streamline their data sharing agreements to create consistency across vendors.
Some technologies can help companies leverage customer data while mitigating privacy risks. In a Metis Strategy interview, Greg Sullivan, CIO of Carnival Corporation, noted that data virtualization enhanced his organization’s analytics capabilities, drove down operational and computing costs and reduced the company’s exposure to potential security and privacy gaps.
Companies can also consider new privacy compliance technologies, which can enhance data governance through increased visibility and transparency. Data discovery tools use advanced analytics to identify data elements that could be deemed sensitive, for instance, while data flow mapping tools help companies understand how and where data moves both internally and externally. These tools can be used to help organizations determine the level of protection required for their most critical data elements and facilitate responses to consumer requests under GDPR and CCPA.
Although legacy technology overhauls can be time-consuming and costly, firms that are decisive about doing so can reduce their privacy and security risks while avoiding other challenges related to technical debt.
As the global data privacy landscape evolves, organizations should continuously adapt their data governance models. Companies should proactively address their obligations by designing data governance roles, processes, policies, and technology with privacy in mind, rather than reacting to current and forthcoming privacy legislation. Companies doing so can not only improve risk and reputational management, but also encourage greater transparency and data-driven decision-making across their organizations.
This article originally appeared on CIO.com. Steven Norton co-authored the piece.
You have heard the hype: Data is the “new oil” that will power next-generation business models and unlock untold efficiencies. For some companies, this vision is realized only in PowerPoint slides. At Western Digital, it is becoming a reality. Led by Steve Philpott, Chief Information Officer and head of the Digital Analytics Office (DAO), Western Digital is future- proofing its data and analytics capabilities through a flexible platform that collects and processes data in a way that enables a diverse set of stakeholders to realize business value.
As a computer Hard Disk Drive (HDD) manufacturer and data storage company, Western Digital already has tech-savvy stakeholders with an insatiable appetite for leveraging data to drive improvement across product development, manufacturing and global logistics. The nature of the company’s products requires engineers to model out the most efficient designs for new data storage devices, while also managing margins amid competitive market pressures.
Over the past few years, as Western Digital worked to combine three companies into one, which required ensuring both data quality and interoperability, Steve and his team had a material call to action to develop a data strategy that could:
To achieve these business outcomes, the Western Digital team focused on:
The course of this analytics journey has already shown major returns by enabling the business to improve collaboration and customer satisfaction, accelerate time to insight, improve manufacturing yields, and ultimately save costs.
Driving cultural change management and education
Effective CIOs have to harness organizational enthusiasm to explore the art of the possible while also managing expectations and instilling confidence that the CIO’s recommended course of action is the best one. With any technology trend, the top of the hype cycle brings promise of revolutionary transformation, but the practical course for many organizations is more evolutionary in nature. “Not everything is a machine learning use case,” said Steve, who started by identifying the problems the company was trying to solve before focusing on the solution.
Steve and his team then went on a roadshow to share the company’s current data and analytics capabilities and future opportunities. The team shared the presentation with audiences of varying technical aptitude to explain the ways in which the company could more effectively leverage data and analytics.
Steve recognized that while the appetite to strategically leverage data was strong, there simply were not enough in-house data scientists to achieve the company’s goals. There was also an added challenge of competing with silos of analytics capabilities across various functional groups. Steve’s team would ask, “could we respond as quickly as the functional analytics teams could?”
To successfully transform Western Digital’s analytics capabilities, Steve had to develop an ecosystem of partners, build out and enable the needed skill sets, and provide scalable tools to unlock the citizen data scientist. He also had to show his tech-savvy business partners that he could accelerate the value to the business units and not become a bureaucratic bottleneck. By implementing the following playbook, Steve noted, “we proved we can often respond faster than the functional analytics teams because we can assemble solutions more dynamically with the analytics capability building blocks.”
Achieving quick wins through incremental value while driving solution to scale
Steve and his team live by the mantra that “success breeds opportunity.” Rather than ask for tens of millions of dollars and inflate expectations, the team in IT called the High-Performance Computing group pursued a quick win to establish credibility. After identifying hundreds of data sources, the team prioritized various use cases based on those that met the sweet spot of being solvable while clearly exhibiting incremental value.
For example, the team developed a machine learning application called DefectNet to detect test fail patterns on the media surface of HDDs. Initial test results showed promise of detecting and classifying images by spatial patterns on the media surface. Process engineers then could trace patterns relating to upstream equipment in the manufacturing facility. From the initial idea prototype, the solution was grown incrementally to scale, expanding into use cases in metrology anomaly detection. Now every media surface in production goes through the application for classification, and the solution serves as a platform that is used for image classification applications across multiple factories.
A similar measured approach was taken while developing a digital twin for simulating material movement and dispatching in the factory. An initial solution focused on mimicking material moves within Western Digital’s wafer manufacturing operations. The incremental value realized from smart dispatching created support and momentum to grow the solution through a series of learning cycles. Once again, a narrowly focused prototype became a platform solution that now supports multiple factories. One advantage of this approach: deployment to a new factory reuses 80% of the already developed assets leaving only 20% site-specific customization.
Developing a DAO hybrid operating model
After earning credibility that his team could help the organization, Steve established the Digital Analytics Office (DAO), whose mission statement is to “accelerate analytics at scale for faster value realization.” Comprised of a combination of data scientists, data engineers, business analysts, and subject matter experts, this group sought to provide federated analytics capabilities to the enterprise. The DAO works with business groups, who also have their own data scientists, on specific challenges that are often related to getting analytics capabilities into production, scaling those capabilities, and ensuring they are sustainable.
The DAO works across functions to identify where disparate analytics solutions are being developed for common goals, using different methodologies and achieving varying outcomes. Standardizing on an enterprise-supported methodology and machine learning platform enables business teams faster time-to-insights with higher value.
To gain further traction, the DAO organized a hackathon that included 90 engineers broken into 23 teams that had three days to mock up a solution for a specific use case. A judging body then graded the presentations, ranked the highest value use cases, and approved funding for the most promising projects.
In addition to using hackathons to generate new demand, business partners can also bring a new idea to the DAO. Those ideas are presented to the analytics steering committee to determine business value, priority and approval for new initiatives. A new initiative then iterates in a “rapid learning cycle” over a series of sprints to demonstrate value back to the steering committee, and a decision is made to sustain or expand funding. This allows Western Digital to place smart bets, focusing on “singles rather than home runs” to maintain momentum.
Building out the data science skill set
“Be prepared and warned: the constraint will be the data scientists, not the technology,” said Steve, who recognized early in Western’s Digital journey that he needed to turn the question of building skills on its head.
The ideal data scientist is driven by curiosity and can ask “what if” questions that look beyond a single dimension or plane of data. They can understand and build algorithms and have subject matter expertise in the business process, so they know where to look for breadcrumbs of insight. Steve found that these unicorns represented only 10% of data scientists in the company, while the other 90% had to be paired with subject matter experts to combine the theoretical expertise with the business process knowledge to solve problems.
While pairing people together was not impossible, it was inefficient. In response, rather than ask how to train or hire more data scientists, Steve asked, “how do we build self-service machine learning capabilities that only require the equivalent of an SQL-like skill set?” Western Digital began exploring Google and Amazon’s auto ML capability, where machine learning generates additional machine learning. The vision is to abstract the more sophisticated skills involved in developing algorithms so that business process experts can be trained to conduct data science exploration themselves.
Designing and future-proofing technology
Many organizations take the misguided step of formulating a data strategy solely about the technology. The limitation of that approach is that companies risk over-engineering solutions with a slow time to value, and by the time products are in market, the solution may be obsolete. Steve recognized this risk and guided his team to develop a technology architecture that provides the core building blocks without locking in on a single tool. This fit-for-purpose approach allows Western Digital to future-proof its data and analytics capabilities with a flexible platform. The three core building blocks of this architecture are:
Designing and future-proofing technology: Collecting data
The first step is to be able to collect, store and make data accessible in a way that is tailored to each company’s business model. Western Digital, for example, has significant manufacturing operations that require sub-second latency for on-premise data processing at the edge, while other capabilities can afford cloud-based storage for the core business. Across both spectrums, Western Digital consumes 80-100 trillion data points into its analytics environment on a daily basis with more analytical compute power pushing to the edge. The company also optimizes where it stores data, decoupling the data and technology stack, based on the frequency with which the data must be analyzed. If the data is only needed a few times a year, the best low-cost option is to store the data in the cloud. Western Digital’s common data repository spans processes across all production environments and is structured in a way that can be accessed by various types of processing capabilities.
Further, as Western Digital’s use cases became more latency dependent, it was evident that they required core cloud-based big data capabilities closer to where the data was created. Western Digital wanted to enable their user community by providing a self-service architecture. To do this, the team developed and deployed a PaaS (Platform as a Service) called the Big Data Platform Edge Architecture using cloud native technologies and DevOps best practices in Western Digital’s factories.
Future-proofing technology: Process & govern data
With the data primed for analysis, Western Digital offers a suite of tools that allow its organizations to extract, govern, and maintain master data. From open source Hadoop to multi-parallel processing, NoSQL and TensorFlow, data processing capabilities are tailored to the complexity of the use case and the volume, velocity, and variety of data.
While these technologies will evolve over time, the company will continually need to sustain data governance and quality. At Western Digital, everyone is accountable for data quality. To foster that culture, the IT team established a data governance group that identifies, educates and guides data stewards in the execution of data quality delivery. With clear ownership of data assets, the trust and value of data sets is scalable.
Beyond ensuring ownership of data quality, the data governance group also manages platform decisions, such as how to structure the data warehouse, so that the multiple stakeholders are set up for success.
Future-proofing technology: Realize value
Data applied in context transforms numbers and characters into information, knowledge, insight, and ultimately action. In order to realize the value of data in the context of business processes – either looking backward, in real time, or into the future – Western Digital developed four layers of increasingly advanced capabilities:
By codifying the analytical service offerings in this way, business partners can use the right tool for the right job. Rather than tell people exactly what tool to use, the DAO focuses on enabling the fit-for-purpose toolset under the guiding principle that whatever is built should have a clear, secure, and scalable path to launch with the potential for re-use.
The platform re-use ability tremendously accelerates time to scale and business impact.
Throughout this transformation, Steve Phillpott and the DAO have helped Western Digital evolve its mindset as to how the company can leverage data analytics as a source of competitive advantage. The combination of a federated operating model, new data science tools, and a commitment to data quality and governance have allowed the company to define its own future, focused on solving key business problems no matter how technology trends change.
Price matters, a lot. In an era of hyper price transparency, the subtlest price discrepancies will drive consumers to purchase on channels with the lowest price. Often consumers make buying decisions in two steps: first, what they want to buy; second, where they will buy. Especially for goods and services that are not substantially differentiated in terms of quality or features, your average consumer will naturally gravitate towards the lowest price. This has been felt in an especially acute manner for retailers such as Best Buy, where consumers go to window shop, but complete their purchases on lower priced ecommerce alternatives (i.e., Amazon, eBay, Jet, etc.). Best Buy has since woken up to the fact that without differentiating the customer experience, they were unable to create stickiness to convert foot traffic. When selling a commodity, or a good/service with a comparably substitute, price parity is arguably the most important driver in decision making. The challenge, of course, is that the manufacturers of a good, or a provider of a service, don’t always own the end touch point with the consumer. Many companies rely on a network of distribution partners to help market and sell their products. While this approach allows companies to scale revenue without the risk of building a massive salesforce, it also means that the manufacturer/provider will not be able to control all the variables that influence consumer’s buying decisions.
To strike the right balance, many companies develop a distribution strategy that comprises two dimensions: direct and indirect sales. Direct distribution focuses on selling directly to customers, while indirect distribution depends on intermediaries to complete a transaction. A distribution strategy needs to be married with a robust approach to inventory management, which may mean different things to a manufacturer than a service provider. Manufacturing firms typically have robust Sales & Operations process (referred to S&OP), during which they forecast sales and ensure there is enough inventory produces and physically distributed to distribution centers or shelf space to meet consumer demand. Service providers tend to look at inventory as an expiring asset: once time has passed, you can no longer sell that service (e.g., once a plane takes off with an empty seat, or a tee time passes without a foursome teeing off).
Although hospitality was one of the first industries to create robust distribution channels and networks through Online Travel Agencies (OTAs) to capture additional business, one of the consequences of that arrangement is that customers were conditioned to view hotel rooms as a commodity where price was the primary decision factor. While OTAs let reviews and minimal merchandising try to differentiate hotels, consumers also got lost in the noise of the difference between one chain versus another.
Over the past 5 years, intermediaries successfully crafted a narrative that they had the consumer’s best interest at heart by negotiating with the hotels, and only the OTAs could be trusted for the lowest price. Some of this was true; you could find lower prices for last minute deals, and there was benefit to both the OTAs and hotel operators that did not want to see a bed go empty. However, as OTAs further influenced the customer experience, and ate into profits with a greater share of bookings, the hospitality, airline, and other industries recognized that they would have to take decisive action to remove price disparity as the primary reason a consumer would purchase products or services on any indirect channel.
One compelling example is Icelandair and El Al who have begun experimenting with displaying sample prices of their competitors on their own websites, to show how competitive their direct prices are, and to hopefully prevent customers from “clicking” away to competitors and other price aggregators. With the explosive growth of options in the online distribution environment, there are two primary factors that companies should concentrate on: Price Integrity and Price Parity.
Price integrity is the concept of a customer being confident that they are purchasing a product of a certain value. While a customer may be willing to pay more or less, depending on the time and place of their purchase, there is a psychological range that they base their expectations on.
Price parity is the practice of maintaining a consistent rate for the same product across all distribution channels, including both owned and partnership channels. Nothing destroys trust more than being able to find a cheaper price on another website, or worse, when a company’s website is cheaper than its stores.
For industries that rely both on direct channels and distribution channels, there is a “co-opetition” relationship in which it is not uncommon for a firm to be competing with their distribution partners for sales. On the one hand, if a consumer wasn’t going to come to AlaskaAirlines.com, they would be more than happy with a referral from KAYAK, or a booking through Expedia to fill an empty seat. But if there was a chance that customer could have booked directly with Alaska Airlines, they would have fought hard to win that booking.
Hospitality and travel companies are in the middle of an ongoing competition with their distribution partners (OTAs and Metasearch engines – METAs) for the future of guest bookings. According to Hitwise, hotel direct booking only made up ~30.56% of online booking market share in 2017, at the same time OTAs continued to eat away further at market share, growing 60 basis points from 2016 to 2017.
While OTAs and METAs have become an invaluable component of hospitality marketing and distribution campaigns, there are contractual violations that stress the trust necessary for heathy “co-opetition” Some OTAs and METAs may display available prices that undercut contracted prices. Often these discounted prices are provided to the OTAs and METAs by wholesalers in violation of price-parity contracts, but the complex web of distribution relationships and flash-speed of online pricing engines makes it difficult for hospitality companies to really hold their distribution partners accountable.
Despite the challenges, companies must maintain a vigilant eye on how inventory and experiences are being displayed by distribution partners to ensure that consumers that may have the inclination to purchase on direct channels are not actively dissuaded from doing so. A successful distribution strategy must be aggressive and can quickly be implemented and maintained by following these six critical steps:
Metric tracking allows you to better understand if your chosen distribution partners are worth their distribution costs. For example, “NRevPAR” (Net Revenue per Available Room) is the industry standard in hospitality for calculating the revenue generated per available room, net of any discounts or commissions paid to intermediaries. Through the re-evaluation of their NRevPAR, hoteliers can evaluate their current distribution partnerships across their current distribution channels to ensure that their distribution costs are harmonized with their expectations for each partner. A significant drop in a key metric is a telltale sign that it is time to either renegotiate with your current distributors or start looking for replacements.
It is imperative that you monitor how and where your inventory is displayed across your distribution partners’ platforms. You want to have the ability to confirm that your partners are playing by the rules as well as ensuring that your offering is not appearing unofficially on other public channels with rogue prices that undercut you and your partners. If a partner determines that your inventory is floating around the public space at prices that undercut their contracted prices, it won’t be long before you observe your inventory being pushed to the bottom of their display pages—if they don’t remove you altogether for being out of parity.
Andrew Sheivachman of Skift pointed out that in 2017, global digital travel sales were projected to reach $189.6 billion in 2017, of which 40 percent was to be attributed to purchases made through mobile (4% gain over 2016). With such a rapid rise in the adoption of mobile booking and shopping, you cannot let your mobile channel development lag. You must work proactively with your distribution partners to refresh user interfaces and user experiences to optimize their mobile shopping experience. Rich content, descriptions, and high-quality photography also allow you to differentiate your product when it is sitting on a digital shelf with comparable products.
Dynamic yield pricing allows you to base your pricing relative to demand and other variables. Dynamic pricing is being employed across various industries to match supply and demand to move expiring inventory: preventing waste in grocery stores, ensuring that there are enough drivers on the road for ride-sharing platforms, or driving loyalty by generating customer-specific fares for airlines. Within the hospitality industry, dynamic pricing allows for inventory to be priced appropriately in response to the timing of a booking, local events, or any occasion that could cause fluctuating demand. Just make sure that your dynamic price is not undercut by a distribution partner or cached by that distribution partner and out of date when prices go back up.
While channels you directly manage (a website, a social presence, in-store), may not be the first point of interaction between you and your prospective consumer, you still can convert customers to complete their purchase through your owned direct purchase channels as you get to know them and earn their attention. In 2015, of booking journeys that were initiated on OTAs – over 34% of bookings were completed through supplier websites. Bolstering your available offers for customers through loyalty programs, subscription email campaigns, and social media can help drive customers from your distribution partners to your direct-booking channels.
Legacy backend systems may cause you millions of dollars in system outages and will almost certainly inhibit your ability to proactively adjust your distribution network. These legacy platforms cause transactional friction during the process in which a supplier’s prices are sent out to the systems of distribution partners, which in turn forces revenue managers to spend hours a day manually validating that prices and inventory are being migrated accurately to various distribution channels and partners. Rate monitoring platforms are now available that allow for revenue managers to monitor the behavior of their distribution partners using automation. The use of these platforms also increases transparency of your distribution partners’ networks. These platforms can be used to not only monitor the integrity and parity of pricing for your own inventory, but they can be used to quickly determine if you are competitively priced across the globe. With our earlier example of Icelandair and El Al, technology can also automatically allow revenue managers to know when their rates are being advertised by competitors (either accurately or inaccurately).
While your distribution partners can help you reach new customers and markets, you must ensure that their role as an intermediary does not equate to them “owning” the customer. It’s the incentive of your distribution partners to provide you revenue, but they are unlikely to share customer information that can be used to convert a customer into a loyal patron (i.e. personal email address, mailing addresses, etc.). Providing an amazing customer experience is the best way to overcome a consumer’s bias to make decisions based on price. If a company can pair a differentiated customer experience, with an enticing loyalty program that rewards purchasing goods or services through direct channels, there is still hope to maintain a balanced distribution strategy.
In early 2015, when The Manitowoc Company decided to split into two companies, the executive leadership called on the CIO, Subash Anbu, to lead the charge.
The transformation would be the most consequential in its 113-year history. Leaders from the company, then a diversified manufacturer of cranes and foodservice equipment, decided that the whole of the diversified organization was no longer greater than the sum of its parts. It would split into two publicly-traded companies: Manitowoc (MTW), a crane-manufacturing business, and Welbilt (WBT), which manufactures foodservice equipment.
The CIO was a natural choice to lead a change of this magnitude because his role allowed him to understand the interconnectedness of the company’s various business capabilities, which processes and technology were already centralized or decentralized, and where there may be opportunities for greater synergy in the future-state companies.
Subject matter expertise, however, would not have been enough to qualify a candidate; the leader had to be charismatic, and Subash was widely recognized for his servant-leadership mentality. That would prove essential to removing critical blockers across the organization.
It was also important that the CIO had long-standing credibility with the Board of Directors, who were the ultimate decision makers in this endeavor.
Subash embraced the daunting challenge, saying, “While change brings uncertainty, it also brings opportunities. Change is my friend, as it is the only constant.”
In some ways, splitting a company into two may be harder than a merger. When merging, you have the luxury of more time to operate independently and merge strategically.
When Western Digital acquired HGST in 2015 and Sandisk in 2016, CIO Steve Philpott decided to move all three companies to a new enterprise resource planning (ERP) system rather than maintain multiple systems or force everyone onto the incumbent Western Digital solution. When splitting a company, there is greater urgency to define the target state business model and technology landscape and execute accordingly.
This split for Manitowoc introduced major consequences for change: duplication of every business function, completed within a fixed four-quarter schedule, while still executing the 2015 business plans. All business capabilities would be impacted, especially Finance, Tax, Treasury, Investor Relations, Legal, Human Resources, and of course, Information Technology.
While the Manitowoc Company had experience with divesting its marine segment (it started as a shipbuilding company in 1902), the scope and scale of the split was unprecedented for the company.
Breaking apart something that has been functioning together is an inherently risk-laden proposition. Subash and his team recognized that to mitigate risk, they would need to be both thoughtfully deliberate in planning and agile in their execution that breaks down big risks into smaller risks, prioritizing speed over perfection.
As Subash led the split of the company into two, he encountered the following risks:
When splitting a public company, the deadlines and outcome are clear. How Subash and the team would execute the split of the company, however, remained largely undefined.
The enormity of the task could have created a paralysis, but the team quickly began working backwards: getting on the same page with the right people; identifying the big-rock milestones; identifying the risks; sketching out a plan to reach the big-rock milestones; breaking the plan into smaller rocks to mitigate risk; and keeping everyone informed as the plan unfolded with greater detail.
In the process, Subash learned five critical lessons that all executives should heed before splitting a company:
Splitting a company requires cross-functional collaboration and visibility at the strategic planning and execution level. Start by creating a Separation Management Office, consisting of senior functional leaders that will oversee the end-to-end split across HR & Organizational Design, Shared Services & Physical Location Structuring, IT, Financial Reporting, Treasury & Debt Financing, Tax & Legal Entity Restructuring, and Legal & Contracts. The Separation Management Office should report to a Steering Committee consisting of the Board of Directors, CEO, CFO, and other C-level leaders. When faced with difficult questions that require a decision to meet deadlines, the Steering Committee should serve as the ultimate escalation point and decision maker to break ties, even if it means a compromise.
A split will require dedicated, skilled resources that understand the cross-functional complexities involved. This project team will need people that understand the interconnectedness of technology architecture, data, and processes, balanced with teams that can execute many detailed tasks. When forming the team, it is important to orient everyone on the common objective to create unity; departmental silos will not succeed. Variable capacity will almost certainly be necessary for major activities, and you may be able to stabilize your efforts by turning to trusted systems integrators or consulting partners to help guide the transition.
Agile evangelists often frown upon working under the heat of a mandated date and scope, but a public split forces such constraints. Treat the constraints as your friend: Work backward to identify your critical operational and transactional deadlines. Ensure the cross-functional team is building in the necessary lead time, especially when financial regulations or audits are involved. Dedicate a budget, but be prepared to spend more than you anticipate, as there will always be surprises to which teams will have to adapt. As part of your project planning, create a risk management framework with your highest priority risks, impacts, and decision makers clearly outlined. When time is of the essence, contingency plans need to be in place to adapt quickly.
Any time a working system is disassembled, there unquestionably will be problems. The key is not to wait for a big bang at the end to see if what you have done has worked. Spending nine months planning for and three months executing this split would have introduced new risks. Instead, Subash and his team built their plan and then iteratively built, tested, and improved in an agile-delivery process. The team was able to identify isolated mistakes early and often, allowing them then to proceed to the following phases with greater confidence—not with bated breath.
In a split, every employee, contractor, supplier, or customer will be impacted. Create a communication plan for the different personas: Steering Committee, operational leaders, functional groups, customers, partners and suppliers, and individual employee contributors. The Manitowoc Company had to communicate on everything from where people would sit, to who would be named as new organizational leaders. In the void of communication, fear and pessimism can creep in. To prevent this, the Separation Management Office launched “Subash’s Scoop,” a monthly newsletter on the separation progress. It brought helpful insight, with a flare of personality, to keep the organization aligned on its common goal.
The Manitowoc Company successfully split into two public companies—Manitowoc (MTW) and Welbilt (WBT)—in March 2016, hitting its publicly-declared target. In fact, many of the critical IT operational milestones were completed in January, well in advance of the go-live date.
Over the last two years, the stock prices for both companies have increased, validating the leadership evaluation that the whole was no longer greater than the sum of its parts.
