Plugging into AI & ML Adoption

Colorful USB plugs moving up like a chart from left to right.

For instance, McKinsey & Company’s analysis, in its blog, “What it takes to rewire a CPG company to outcompete in digital and AI,” highlights a notable disparity: “CPG companies are among the poorest performers in digital and AI maturity, while retailers are near the top.”

This underutilization of AI and ML can be attributed to several factors, including outdated organizational structures, insufficient investment in digital transformation, and a lack of requisite skills within the workforce. To bridge this gap and fully harness the potential of AI and ML, CPG companies need to make strategic changes in their approach.

Current Challenges in AI and ML Adoption

Several key challenges impede the effective adoption of AI and ML within many organizations. Here are some of the most common ones:

  1. Siloed Implementations: Many CPG companies have implemented AI and ML in isolated pockets of their operations rather than across the entire organization. This fragmented approach limits the technologies’ potential benefits because the full value of AI and ML is realized only when these systems are integrated across multiple functions and processes. Without a unified strategy, companies miss out on synergies that could enhance efficiency and innovation.
  2. Legacy Systems: A significant barrier is the reliance on outdated enterprise resource planning (ERP) systems and other legacy technologies. These systems often impede the seamless integration of new technologies, causing delays in digital transformation initiatives. The challenge lies in modernizing these systems without disrupting ongoing operations.
  3. Skill Gaps: The deployment and management of AI and ML systems require specialized skills that are currently in short supply. Many CPG companies struggle to find and retain talent proficient in data science, machine learning, and related fields. This shortage not only hampers implementation but also affects the company’s ability to innovate and stay competitive.
  4. Inadequate Data Management: Successful digital transformation relies heavily on the quality and accessibility of data. Many CPG companies face issues with fragmented and inconsistent data, which complicates the extraction of actionable insights and the making of informed, data-driven decisions. Effective data management is crucial for maximizing the impact of AI and ML technologies.

Investing in AI and ML Organizations

To address these challenges and maximize ROI from AI and ML investments, CPG companies should focus on several key areas of organizational transformation:

  1. Leadership Roles: Introducing roles such as Chief AI Officer (CAIO), Chief Digital Officer (CDO), and Chief Growth Officer (CGO) can provide the necessary leadership and strategic direction for digital initiatives. These roles are critical for bridging the gap between technology and business functions. Leaders in these positions should have a robust background in both technology and business strategy, ensuring that digital initiatives align with overall business objectives. These roles can be newly created or replace existing positions, depending on the organization’s needs.
  2. Skill Development and Talent Acquisition: Addressing the skills gap involves both upskilling existing employees and recruiting new talent with the required expertise. Strategies to close this gap include investing in continuous learning programs, partnering with educational institutions, and implementing reverse mentoring. Reverse mentoring allows younger, tech-savvy employees to share their knowledge with senior leaders, fostering a culture of continuous learning and innovation.
  3. Create Centers of Excellence: Establishing internal centers of excellence dedicated to digital transformation can provide specialized support for the successful integration of AI and ML. These centers should focus on developing and disseminating best practices, offering training and development programs, and ensuring consistent implementation across the organization. Centers of excellence act as hubs of expertise, driving innovation and maintaining high standards for digital initiatives.
  4. Modernize IT Infrastructure: Upgrading legacy systems and adopting flexible, cloud-based solutions can significantly improve data management and facilitate the integration of AI and ML technologies. This modernization should be part of a comprehensive digital transformation strategy that aligns with both immediate and long-term business goals. Cloud-based solutions offer scalability and flexibility, enabling companies to adapt quickly to changing technological and market conditions.
  5. Partnerships with Other Organizations: Forming strategic partnerships with technology providers, startups, and academic institutions can accelerate AI and ML adoption. Collaborations with tech companies can provide access to cutting-edge tools and expertise, while partnerships with startups can offer innovative solutions and fresh perspectives. Engaging with academic institutions can help bridge the skills gap by facilitating research and offering training programs. These partnerships can enhance capabilities, share risks, and foster innovation through combined expertise and resources.

Shifting the Perspective on AI and ML

For CPG companies, leveraging AI and ML for substantial ROI involves more than mere technology adoption; it requires a fundamental shift in organizational roles, responsibilities, and talent management practices.

By creating specialized leadership roles, investing in skill development and talent acquisition, modernizing IT infrastructure, and forming strategic partnerships, companies can unlock the full potential of these technologies. This strategic transformation not only enhances operational efficiency and fosters innovation but also ensures that CPG companies remain competitive in an increasingly digital marketplace.

By addressing these challenges and focusing on these areas of transformation, CPG companies can move beyond their current limitations and begin to realize the transformative benefits of AI and ML. Ultimately, this will drive greater value for their businesses and deliver enhanced outcomes for their customers.

Click here for more columns by Gail Martino; if you enjoy this content, please consider connecting with Gail Martino on LinkedIn.

Finding the Value of Innovation

A diamond and its facets.

Rounding out FEI 2024 was a keynote presentation called Brunch with the Bots. Attendees got the chance to see the blending of machine and human intelligence, and a sense of interacting with the future. Handlers provided a mix of Autonomous Mobile Robots (AMRs), Automated Guided Vehicles (AGVs), Articulated Robots, Humanoids, Cobots, Hybrids and of course dogs to greet the delegation Each bot and handler was then available for networking at various stations throughout the main hall.

What are your thoughts on the bots that were represented here at FEI 2024 and the innovation associated with this field?

“First, let me say, I thought that the conference was fabulous. There were several great tracks and there were a lot of high-quality talks,” says Martino. “However, since I was involved in Brunch with the Bots, I think that they saved the best for last (it was the last session), and we had a number of actual robots and co-bots that we were able to show and interact with. The reason for that track is to almost signal what could be coming in the future, what are some of the new technologies in this field, and just kind of a window into the future of technology.”

Certainly, we saw a lot of interesting robots and their connection to innovation, technology, and even that aspect of innovation associated with digital transformation and generative AI. Speaking of the role of innovation, would you say that there is currently a democratization of innovation happening in the corporate enterprise? Is innovation becoming more accessible for the company as a whole?

Martino observes, “When we think about innovation at the enterprise level, there’s a number of groups, teams and departments that feed into that. Typically, that involves R&D and all of the subgroups within R&D. It includes the marketing organization. It can include the supply chain or organization and also the manufacturing organization. When we think about innovation, at least where I have worked and how I think about it, is that innovation is throughout the value chain. Now sometimes, there’s more cross-fertilization along that value chain than other times. Some organizations are more siloed, some are less siloed, but I think all of those together comprise what you would call that innovation democracy.”

We touched on a lot of those themes at FEI, that aspect of the cross-collaboration of teams and of innovation being fueled by interdisciplinary activity. Do you see that as a trend moving forward, this more interdisciplinary approach?

“I think that is a standard of the innovation practice,” says Martino. “Whenever you’re developing a new product, there are so many touch points throughout the organization to make that a success. It’s like you can’t make a diamond without the different facets, and that’s kind of how you create a really winning product and product proposition in the market.”

What are your thoughts on 2025 in terms of an outlook? Just where do you think innovation is heading?

“Over the last ten years or so, we’ve seen a cycle of different capabilities throughout the enterprise,” relates Martino. “So if we go back to, say, 2010 or so, it was all about open innovation. It was about collaboration and so forth. And that allowed organizations to open up a little bit and partner for growth. From there, the next generation digital transformation has allowed a lot of organizations to collect more data, to try to be a little bit more insightful about their data, to convert things that were originally analog to digital, and make a variety of changes.”

Martino continues, “Now we’re into the period of AI, which makes perfect sense because if you’ve collected all of this data, now you need to really mine that for insights, and you need to monetize that and see the ROI of it. I see AI now as a tool that’s allowing us to get more insight into the data that has been generated during digital transformation, and then it will continue to propel the enterprise forward.”

For more of the interview with Gail Martino, check out the video from FEI 2024.

Building Connections Between Insights & Innovation

Skyscrapers with a circular sculpture in the center of the photo.

All Things Innovation sat down with Michael Nevski, Director, Global Insights, Visa, at FEI 2024 to touch on insights, innovation and all points in between, as well as the panel featuring Generation Z. The “Gen Z Focus: Attaining Next Generation Intelligence,” while not a traditional focus group, presented Gen Z creators sharing their thoughts on corporate innovation—what they see currently working and what is currently turning them off.

Nevski participated in the Generation Z panel at FEI 2024. It was a fascinating panel on the Generation Z mindset and featured some accomplished Gen Z innovators along with some consumer insights professionals on the flip side. Can you share with us your thoughts about the panel?

“It was a great panel at the conference because it helps us to build and understand tangency, connect to Generation Z, understand their psychology, and what motivates them and drives them on a daily and weekly basis,” says Nevski.

He adds, “I think this intergenerational panel was very illustrative about Gen Z’s attitude towards brands, towards innovation, towards insights, and the values they bring to the world. They want us to be supporters and kind of a conduit of that change that they’re trying to influence in the world. As we know, for the next 12 years or so, the perceived value of Gen Z is going to grow. Their purchasing power is going to grow, and how they treat companies, how they treat the environment, how they treat brands, becomes more and more important. As innovators and researchers, it’s very important for insights professionals to help our brands to connect with Gen Zer’s, and understand their drivers to be successful in the future.”

The world of insights and innovation is closely linked in the corporate environment. Perhaps now more than at any other time, because of the growth of artificial intelligence, research and development professionals can leverage insights in their initiatives.

“Absolutely,” says Nevski. “That relationship has been there for a long time, but I think nowadays it requires more skills and more understanding because to me, innovation, previously when people would say, let’s get together, let’s generate some ideas, let’s create an outcome—that just doesn’t work anymore because you need real insights to really create the innovation. When people come up with ideas, they need to be based on something. And the insights function, it’s like a neck for the hat, it identifies those white spaces, and identifies those actionable insights. This kind of interchange or cooperation of innovation and insights is becoming more and more important. What it means for us, we need to be more adaptable as researchers. We need to have broader skills in terms of the data, and in terms of the type of research we conduct.”

In some ways, with the increasing access to AI across corporations, it’s the democratization of insights—plus the democratization of innovation as well.

“We need to democratize the insights capability,” says Nevski. “So many functions within our enterprises need to have much faster, more efficient access to insights. In this way, insights can contribute to and support innovation professionals. In addition, it’s not only about working together and bringing those insights during the ideation phase, but also in the execution part. How do we as a cross-collaborative team deliver and develop products and services based on what insights identifies and fits into the innovation framework?”

For more of the interview with Michael Nevski, check out the video from FEI 2024.

Unleashing Innovation

Fireworks exploding in the night sky.

Sanchez participated in and held two sessions at FEI 2024. This included “Uniting Changemakers: Unleashing Innovation Through Interdisciplinary Interactivity,” in which corporate changemakers discussed how to drive innovation forward within global corporate enterprise.

Plus, Sanchez held the session, “Mastering an Upfront R&D Strategy: An Innovation Culture Case Study.” She shared how tying strategy together with culture has always been a challenge. But the innovator must strike a balance between the need for growth, overall direction from leadership, mission critical information from stakeholders, insight from current customers and managing the team, all while leveraging contemporary methodologies for end-to-end innovation.

Sanchez relates, “At FEI, we have been talking a lot about the front end of innovation, transformational innovation, how do you create these breakthroughs, and how different that is from what you’re doing every day with sustaining innovation. More often than not, we grapple with it, and we realize that one of the most important aspects of being able to succeed in those spaces is creating the right culture of innovation in the organizations that we work with.”

Do you see a democratization of innovation happening in the corporate world?

“There is a lot of need for democratization of innovation because the pace of the world is accelerating rapidly,” says Sanchez. “What used to be two or three or four years’ worth of development time that you have is now one or two years of development time. There’s also a lot of geoeconomics and geopolitics going on, everything that we do from the design of the products and services that we have, to the supply chains, to the manufacturing systems, and so on.”

She adds, “The more we can take these innovation activities and move these innovation concepts from the top to the breadth of the organization, the better off we’re going to be because it’s the people on the front lines. The ones that are talking to the consumers every day, the ones that are designing the equipment, the ones that are creating the procurement systems that actually have the best ideas on how to innovate and do things better.”

Is there a growing trend of accessibility around innovation for both the team and for the employees of the company. Is that something that helps with breakthrough innovations? Or alternatively does that balance the short-term versus the long-term?

Sanchez observes, “I think flatter and flatter organizations with that balance between communication and connectivity and everything else are better positioned to actually have more capacity to do the innovation work. The role of leadership is really to set the vision, set the goals, and provide the resources to do the work and unleash the organization and let them go on the creation of the innovation that needs to happen.”

Through the lens of your experience with innovation, what is your outlook for the field in 2025 and beyond?

“The outlook for innovation is mostly balancing between what are very entrepreneurial approaches that today are very associated with small company startups, with the discipline, the power, and the rigor of the more traditional larger organizations,” says Sanchez. “If you can put those two things together, you get the best of both worlds and can move forward in innovation in an accelerated way. The way of the future is partnerships and collaborations. It will be harder and harder to continue to develop in-depth expertise on all of the areas that you need to be able to access and compete in order to create breakthrough innovation. And it’s through partnerships that you’re going to be able to have that level of expertise and depth.”

For more of the interview with Liza Sanchez, check out the video from FEI 2024.

Supporting Agile Innovation

A spiral seashell.

Setting the Stage for an Agile Development Culture

Agile principles may be more prevalent in startups and smaller organizations, who might still be finding a foothold in their markets, searching for the right business model and who may be more flexible in their strategies. They can test new ideas and make course corrections faster.

Yet larger corporations with more solid foundations in their markets can also benefit from and embrace agile development. This often starts with a reminder that it could take time to build an innovative mindset culture in the company, which may be set in their ways for many years and reluctant to change the way they do business and innovate. It’s not to say that larger companies can’t have best practices in innovation, but rather that traditional methods of innovation might sometimes get in the way of rapid, flexible and agile approaches.

Product Plan, in its blog, “8 Ways to Use Agile Principles to Drive Innovation in Large Organizations,” looks at ways to empower the innovation team, create an overall rapid research culture, get leadership on board and ultimately develop an environment that is more supportive.

  1. Trust and Autonomy Empowers Teams to Innovate. Innovation doesn’t have to be something huge. It doesn’t have to mean a new product or new direction. You can start small. Give product teams both the power and trust to solve problems in the way they see fit. Once the problem is clearly identified, the team is free to determine the best solution. The team is usually very close to the customer and can make the best judgment calls on what to build and how to build it.
  2. Access to Customers Reduces Innovation Risk. Innovation is far less risky when you start small and get feedback as quickly as possible. Opinions within your organization are not always the most important ones. Yet, stakeholders (and yes, their opinions) still play an integral role in enterprise product development decisions. Ultimately being successful in innovation is about knowing what your customers need, not just what stakeholders think they need. Teams need access to customers. And they should feel empowered to make decisions based on what customers (not just internal stakeholders) actually say.
  3. Retrospectives Drive Continuous Improvement. Even Waterfall organizations can benefit from retrospectives. Give your team a safe environment to discuss challenges and frustrations without fear of judgment. If you can foster a sense of psychological safety and a culture of retrospective thinking and iterative improvements, you can start your team off on a path to more rapid innovation. All this, without changing a single thing about your process itself.
  4. Communicate with Stakeholders More Frequently. Stakeholder communication is not an event. It is not a task on your to-do list. It is not a box you can mark “done.” Continuous communication with executive stakeholders becomes significantly more important for product teams who want to innovate more rapidly. Transparency builds trust, so rather than withholding information, be forthcoming. Get buy-in from leadership and stakeholders not only before development begins but also during development as changes are made. Create an ongoing dialogue that doesn’t leave stakeholders feeling left in the dark.
  5. Align on the Fact that Priorities will Change. If you’re embracing rapid innovation or agile principles, you need to set the stage for change. Stakeholders should be aware that the product roadmap is not a plan set in stone but rather a living document that can change. Explain to stakeholders that the further out you’re stepping and planning, the less certainty there is around your strategy. It may even be useful to point out specific items you’re fairly certain about and those that you are less certain about. Reflect that change is likely within your product roadmap on the roadmap itself. This will help you build trust and confidence from stakeholders who will understand that upcoming priority shifts are not arbitrary.
  6. Remove Specific Dates from your Product Roadmap. Roadmap timeframes and dates are frequent points of friction between product managers and stakeholders. You may find it best to remove dates from your product roadmap altogether. Or, if your executive team still demands dates, you may elect to use “big picture” dates rather than specific deadlines. When you present your plan without dates or with more broadly defined timeframes, you focus away from the tactical aspects of product development and emphasize the overall strategy. This is the goal of a roadmap in the first place; to communicate high-level strategy. Your agile roadmap should not become a “promise” to your executive teams. Instead, it is a depiction of your strategy, given the things you know right now.
  7. Talk in Broad Themes Rather than Features. Another way to refocus stakeholder attention to more strategic elements of the product roadmap is to simply present “themes” rather than features. Theme-based product roadmaps talk in broad themes that tie back to strategic goals. They give product teams more flexibility around the specific things that will be built. Rather than telling stakeholders, “here’s a list of the features we’re going to work on,” themes should focus on outcomes. What metrics will be moved by each theme?
  8. Evangelize the “Why.” You don’t have to preach the agile gospel or even say the word agile. But you do need to drive awareness. Be transparent with stakeholders about your intentions when making changes and shifting priorities. It will not only help you build trust but also help get stakeholders into a more agile mindset. Rapid innovation doesn’t have to be a bad word, but you need to be prepared to enlighten stakeholders about why it isn’t bad. Ideally, you can talk about “why” delicately. Talking about all the shortcomings of the status quo will only make people defensive. Focus instead on addressing the values and benefits of a more agile approach.

Accelerating Innovation

All Things Innovation looked at agility through the lens of ambidexterity in “Strengthening Innovation in a Dynamic Environment.” Organizations today often face a conundrum in the current economic environment. On the one hand, there is a directive to cut costs and optimize operations. On the other hand, the rapid pace of change means that a company is expected to focus on innovation to thrive. One avenue is to balance ambidexterity, a business and innovation strategy that puts an emphasis on exploring new opportunities while exploiting existing ones. The need to develop ambidexterity is more urgent today, as companies face expanding into new markets, heightened competition and operating in diverse environments.

In “Creating a Culture of Experimentation,” we also looked at ways to develop the culture and mindset. Many in the community feel that fostering a culture of innovation is paramount. A key to this is building a culture of experimentation. This stems from infusing a cultural mindset into the corporate enterprise, as there has been renewed emphasis on collaboration and effectively developing the skills, talent and leadership for innovation. Innovating quickly and using incremental steps is important, especially as AI has gained ground. But it’s also the culture of experimentation that enables the team to think creatively and push boundaries.

The Benefits of Agile Innovation Principles

Agile principles, traditionally applied in software development, have proven to be equally beneficial in fostering innovation across various industries. According to Gemini, here’s a few key benefits:

  • Accelerated Time-to-Market: Agile’s iterative approach allows for rapid prototyping and testing, enabling businesses to introduce new products or services faster.
  • Enhanced Flexibility and Adaptability: Agile emphasizes responding to change over following a rigid plan, making it ideal for navigating uncertain and dynamic markets.
  • Customer-Centric Focus: Agile prioritizes customer feedback, ensuring that innovation efforts align with actual user needs and preferences.
  • Improved Collaboration and Teamwork: Agile promotes cross-functional collaboration and self-organizing teams, fostering a culture of innovation and shared ownership.
  • Reduced Risk: By breaking down projects into smaller, manageable increments, agile minimizes the risk of large-scale failures.
  • Increased Employee Morale: Agile empowers employees to take ownership of their work and contribute to the innovation process, leading to higher job satisfaction and engagement.
  • Continuous Improvement: Agile’s iterative nature encourages constant learning and improvement, leading to better outcomes over time.

Sprinting to the Innovation Finish Line

Incorporating agile principles into innovation processes can significantly enhance an organization’s ability to adapt to market changes, deliver value to customers, and drive sustainable growth. Cultivating an agile mindset can unlock your team’s innovation potential. This focuses on moving quickly, efficiently and flexibly, while reducing the time to market. Flexible planning, cross functional teamwork, continuous learning and improvement can all contribute to shorter and successful development cycles.

As Stefanini Group puts it in its article on agile innovation, “Remember, fostering a work environment conducive to collaboration, innovation and adaptability is as crucial to unlocking the benefits of agile innovation as the framework you ultimately choose. Once you lay that foundation, you are well on your way to embracing agility.”

Video courtesy of Stefanini Group

Adopting an Innovation Ecosystem

Variety of bubbles floating on a black background.

Engaging with Innovation-Focused Networks

An innovation ecosystem is a network of entities that work together to develop new products and services by sharing knowledge, skills, and technologies. These entities can include companies, universities, governments, the private sector, entrepreneurs and other organizations. They can operate in multiple sectors and at different levels, such as at city, regional or national levels.

These interconnected players need to work together effectively, as each is a key cog in the system to achieve the development goals and the desired outcome. In some ways, it’s all about the partnerships formed that together can bring innovation to life.

The International Development Innovation Alliance (IDIA) defines an innovation ecosystem as “made up of enabling policies and regulations, accessibility of finance, informed human capital, supportive research, markets, energy, transport and communications infrastructure, a culture supportive of innovation and entrepreneurship, and networking assets, which together support productive relationships between different actors and other parts of the ecosystem.”

Because of the broad scope of an ecosystem, it can ebb and flow, and these key partnerships can vary greatly depending upon the sector focused on and the type of product or service being developed. Some ecosystems may need little support while others are more problematic and need some help to cross the finish line.

According to the IDIA, adopting an ecosystems approach to innovation recognizes that:

  • An innovation ecosystem is made up of different actors, relationships and resources who all play a role in taking a great idea to transformative impact at scale.
  • The effectiveness of each part within the innovation ecosystem is moderated by other parts of the system.
  • A change to one part of the innovation ecosystem leads to changes in other parts of the innovation ecosystem.

In some ways, an ecosystem is a living and breathing organism, with many interconnected parts. “Because ecosystems are dynamic, traditionally strong ecosystems can also decline in response to external factors. But even when local systems are weak, there will likely be actors or locations committed to reform. It is important to identify and find ways to support these nodes of reform, as they are the poles around which strong and sustainable systems can emerge,” notes IDIA.

Finding the Right Strategic Fit for Innovation

All Things Innovation first looked at ecosystems in the blog aptly called, “Innovation Ecosystems.” Innovation is a journey, it’s a continuum of experiences, an act of exploration. The orchestration of ecosystems is more and more important in consistently disruptive times. To understand success, organizations must meet failure along the way. Overlaying the lessons made apparent by fellow industry players with key learnings and input from academia is a cogent way forward, says Swarovski’s Hannes Erler.

In “Selecting an Innovation Framework,” All Things Innovation looked further at the systems and strategies of innovation. The science of innovation, especially as seen through frameworks, is an important aspect of the operational process. With innovation a sometimes sprawling, broad task with multiple parts and ecosystems attached to it, many in the field use methodologies that are essential for the business of innovation. From an organizational perspective, frameworks give a structured approach that can guide the company in a more systematic pursuit of innovation. These initiatives can then be focused on, from the ideation phase to implementation, scaling and more. Ultimately, this sets up successful innovation practices that can be repeated.

The Benefits of an Innovation Ecosystem

An innovation ecosystem is a network of interconnected individuals, organizations, and institutions working together to drive innovation. It fosters collaboration, knowledge sharing, and resource optimization, leading to several key benefits, according to Gemini. This includes accelerated innovation, access to resources, and enhanced collaboration.

  1. Cross-pollination of ideas: Different perspectives and expertise converge, leading to novel solutions and breakthroughs.
  2. Faster time-to-market: Collaborative efforts streamline development processes and reduce time-to-market for new products and services.
  3. Talent pool: Attracts and retains top talent by offering opportunities for growth and collaboration.
  4. Financial support: Provides access to funding sources, including venture capital, grants, and angel investors.
  5. Infrastructure and facilities: Offers shared resources like research labs, incubators, and co-working spaces.
  6. Shared risks: Distributes risks among multiple stakeholders, reducing the financial and reputational impact of failures.
  7. Knowledge sharing: Enables learning from others’ mistakes and successes, minimizing costly errors.
  8. Job creation: Fosters entrepreneurship and job creation through the development of new businesses and industries.
  9. Regional development: Attracts investment and talent, stimulating economic growth in the region.
  10. Increased competitiveness: Enhances the region’s global competitiveness by fostering innovation and entrepreneurship.
  11. Knowledge sharing: Facilitates the exchange of ideas, best practices, and expertise.
  12. Partnerships: Encourages collaboration between diverse stakeholders, leading to synergistic outcomes.
  13. Trust-building: Develops strong relationships based on mutual trust and respect.

Connecting Innovation Resources

By creating a supportive, effective and efficient environment for innovation, ecosystems such as accelerators and hubs, empower individuals and organizations to achieve their full potential and contribute to the overall advancement of society. Developing a sustainable innovation ecosystem can take time, from years to decades, with long-term goals in mind.

MassChallenge, in its blog, “What Is an Innovation Ecosystem and How Are They Essential for Startups?” points out that these connections are extremely valuable. “The value of an innovation ecosystem lies in the access to resources for the startups and the flow of information for the ecosystem’s stakeholders. This information flow creates more investment opportunities for the right institutions to connect with the right ideas for their businesses and portfolios, at the right time, for the right reasons,” observes MassChallenge.

Ultimately, MassChallenge notes, ecosystems are a shared vision of the future. “Working together in this capacity demonstrates the power of collaboration and creates a community that supports each other’s goals, missions, visions, and values.”

Video courtesy of Zühlke Group

Building the Right Data & Insights Team

Three architects looking over blueprints at building site.

Do you hire a bunch of data scientists and lock them in a room full of supercomputers? Or do you sprinkle data analysts throughout your organization like fairy dust? There’s no one-size-fits-all answer.

It’s more like a choose-your-own-adventure book, but with spreadsheets instead of dragons.

The right structure for your data and insights team depends on your business goals, culture, and level of data maturity. Consider this your “Let’s Go” guide to the land of data teams. We’ll explore the main approaches to structuring your data and insights team: centralized, decentralized, hub-and-spoke, hybrid, and federated. Each has its own strengths and weaknesses.

Centralized Teams: A Gourmet Restaurant

Picture your data team as a five-star restaurant. All your chefs (data scientists) and staff (analysts) work in one place, and your customers (other departments) come to you. You concentrate your expertise and ensure you have the best tools and ingredients. You can serve up unbiased reports and analysis because the staff is insulated from the politics in the various divisions of the business. It’s like a data oasis in the desert of corporate chaos.

Netflix’s CTO, Elizabeth Stone, explained their approach in a 2024 interview: “At the scale of company that Netflix now is, very often data-oriented teams are embedded in other parts of the business. So it could either be embedded in a business line like ads or games, or they are organized more functionally separating data engineers from data scientists from analytics engineers from consumer researchers. We’ve resisted that and kept a centralized team that is both functionally diverse and works on nearly every area of the business from within the team.”

But centralization is not without challenges. The team must work hard to maintain strong partnerships with other departments and also balance centralized governance with agility.

While decision-making may be slower due to centralized control, decisions typically align well with overall organizational strategy. Cross-functional collaboration is facilitated, but the team may struggle to deeply understand specific business unit needs. It’s a powerful model, but one that requires careful management to reap its full benefits. Think of it as herding cats, if the cats were all geniuses with PhDs in statistics.

Decentralized Teams: Food Trucks

Picture data analysts embedded in every department, speaking the local lingo and solving problems on the ground. That’s the decentralized approach. It’s like having a fleet of food trucks instead of one big restaurant.

Your team spreads out across town to meet the customers where they are, and they have the ability to customize their recipes to local preferences. They can move with their customers, adapting to changing needs faster than you can say “pivot to video.”

But beware – this approach can lead to inconsistent methods, duplication of effort, and missed opportunities for cross-functional insights. It’s like having a bunch of chefs all inventing their own version of tomato soup – you might end up with some creative flavors, but good luck getting them to agree on a menu.

Hub-and-Spoke Model: Restaurant Franchises

Imagine a wheel with a strong center and sturdy spokes radiating outward. That’s the hub-and-spoke model in a nutshell. It combines elements of both centralized and decentralized approaches, aiming to balance consistency with agility. It’s like having a chain of restaurants with a central kitchen and local franchises.

In this model, a central “hub” team oversees overall strategy, standards, and governance, while “spoke” teams embedded in different business units handle day-to-day analytics and insights. It’s like having centralized sourcing for ingredients and equipment, as well as expert guidance on setting up restaurants, but the local franchise owner still has a lot of control and responsibility.

This approach maintains centralized standards while allowing for quick, localized decision-making. Data professionals have opportunities to specialize in business domains while staying connected to a broader data community, fostering skill development. This model effectively addresses both company-wide and department-specific data needs, balancing alignment across the organization.

However, it does come with challenges. Coordination between hub and spoke teams can be complex, requiring strong communication. Resource allocation can be tricky, as decisions must be made on how to distribute talent and resources between the hub and spokes. And of course, only companies with a sizable data team can have both centralized and decentralized staff.

Other Data Team Structure Options

Federated models are hub-and-spoke models in Bizarro world. The data teams in the spokes are the drivers of the business, but there is a centralized team that exists to provide services (such as databases and other tools) across the decentralized teams in order to benefit from economies of scale. It’s like if the local franchises ran the show, but there was a central office that just handled the boring stuff like napkin ordering and payroll.

Hybrid structures are just a DIY of whatever seems to get you through the day. For example, part of the company may rely on hub-and-spoke, but the largest business units may have fully decentralized teams embedded, while the smallest business units rely entirely on centralized support.

“Data Mesh” is the latest trend, or fad if you prefer, that holds a core philosophy of treating data as a “product.” Not only is each decentralized team responsible for their data, but they approach the data and its use as product managers would approach any other product. But data governance, such as quality and security, still operates more like a federated model. It’s like if every restaurant in town had to follow the same health code, but could serve whatever cuisine they wanted.

Of course, the best data team structure is the one that works for your organization’s unique needs and capabilities. Don’t get caught up in the hype – focus on what works for you! It’s like choosing between a fork, a spoon, or chopsticks – the best utensil is the one that gets the food into your mouth without making a mess.

The Evolution of Data Team Structures

As businesses grow and scale, their data team structures must evolve to meet changing needs. It’s like watching a caterpillar turn into a butterfly, if the caterpillar was really into spreadsheets.

The journey begins with the lone wolf phase in early-stage startups, where a single data analyst handles all tasks. This lean and agile approach works well for small companies with limited data needs but becomes unsustainable as data volumes and complexity increase. It’s like trying to bail out the Titanic with a teacup – heroic, but ultimately futile.

In the early growth phase, growing startups and SMBs form a centralized data team to serve the entire organization. This allows for consistent practices and efficient resource allocation. However, as the demand for insights grows, the centralized team can become a bottleneck. It’s like having one barista trying to serve the entire morning rush at Starbucks – someone’s going to end up with decaf when they ordered espresso.

During a rapid growth phase, fast-growing SMBs and mid-market companies often adopt a hub-and-spoke or hybrid model. A central data team (hub) works alongside embedded analysts in key business units (spokes), balancing centralized governance with localized agility. As organizational complexity increases, even greater specialization and autonomy may be needed. It’s like a game of data Tetris – you’re constantly rearranging pieces to fit the changing shape of your business.

At the enterprise scale, large companies often implement decentralized or federated models, giving individual business units their own data teams while maintaining some central coordination. This approach allows for deep domain expertise and tailored solutions but may call for some recentralization to maintain consistency and leverage economies of scale.

Factors driving these structural changes include data volume and complexity, business growth and diversification, regulatory requirements, competitive pressures, technological advancements, and shifts in business strategy.

Regularly Reviewing and Adjusting Team Structures

Be prepared to adapt your data team structure as needed. Set regular check-ins to assess team performance, be open to feedback, and don’t be afraid to shake things up if something isn’t working. Rearranging the deck chairs on the Titanic wouldn’t have helped, but rearranging the icebergs would have a been cool idea.

Your data team structure should be a reflection of your overall organizational goals and strategy. A centralized structure might align well with a company focused on standardization and efficiency, while a decentralized approach could better serve an organization prioritizing innovation and agility in individual business units. Always ask yourself: How does this structure support our broader business objectives?

Factors to Consider When Choosing Your Data Team Structure

As you navigate the journey of building and evolving your data team, several key factors will influence your decisions. Consider these elements carefully to ensure your chosen structure aligns with your organization’s needs and capabilities:

Organizational maturity • Core business model • Competitive landscape • Anticipated business growth • Short-term vs. long-term goals • Executive buy-in • Data literacy • Cultural readiness • Centralized vs. decentralized decision-making • Collaboration across departments • Appetite for innovation and risk • Existing data infrastructure and processes • Current tech stack • Volume and variety of data sources • Data quality and governance • Data security and privacy requirements • Regulatory requirements and compliance needs • Resources and budget • Existing skill sets • Career growth opportunities • Ability to attract and retain talent • External partnerships or vendor relationships • Balance between agility and consistency • Need for quick, localized insights vs. comprehensive, organization-wide analysis

That’s a lot, I know. By considering these factors, you’ll be better equipped to choose a data team structure that not only fits your current situation but also positions you for future success. After all, the goal isn’t just to build a data team – it’s to create a data-driven organization that can thrive in an increasingly complex business landscape.

Staffing Implications of Data Team Structures

The structure of your data team doesn’t just affect technology and processes—it has profound implications for the people who make up your team. It’s like choosing between assembling a rock band or a symphony orchestra—both can make beautiful music, but they require very different skills and management styles.

Staff for the needs of the business. For centralized teams, roles tend to be more specialized. But data analysts on decentralized teams often wear multiple hats. And for combo approaches, there will be a mix of needs for specialists and generalists that will vary greatly depending on the details. It’s like trying to cast for a movie that’s part action thriller, part romantic comedy, and part documentary.

As you build these teams, career progression will be highly dependent on the choice of structure.

Centralized teams have clear career progression, but more of a silo-feel that the analyst is focused on a data career more than a career in your business’ particular industry. Decentralization offers a wider set of experiences and skill development, but can limit access to the wider data professional community. The combo structures can offer the opportunity to rotate across different areas, but some efficiencies would be lost as analysts frequently are having to adapt to new roles.

The most effective data teams are those where people feel valued, challenged, and see clear paths for growth. As you evolve your team structure, always consider the impact on your team members’ experiences and career aspirations. Despite our claim that data is the new oil, it is your people that are your most valuable asset!

Implementing the Right Structure for Your Organization

Whatever structure you choose, don’t be afraid to adapt. Even doing so frequently if the business needs it. Maybe you’re moving from a centralized model to a hub-and-spoke, or perhaps your federated team is consolidating. Whatever the shift, change is hard, but with the right approach, it doesn’t have to be chaos.

Follow a few simple rules:

  • Communicate, communicate, communicate:Don’t spring the change on your team like a pop quiz. Share the vision, rationale, and expected benefits. Be transparent about challenges too.
  • Phase it in:Rome wasn’t built in a day, and your new team structure won’t be either. Consider a gradual transition, perhaps starting with a pilot in one business unit. This allows you to iron out kinks before rolling out company-wide.
  • Provide training and support:Your team might need new skills or tools to thrive in the new structure. Invest in training and be patient as people climb the learning curve.
  • Expect and plan for resistance:Change can be scary. Some team members might be concerned about their roles or responsibilities. Address concerns head-on and involve the team in problem-solving.
  • Reassess and adjust:Set checkpoints to evaluate how the new structure is working. Be prepared to make tweaks, or even bigger changes if needed.
  • Maintain continuity of service:Ensure that ongoing projects and day-to-day operations don’t suffer during the transition. You’re renovating the house while still living in it!
  • Update processes and documentation:Your new structure will likely require updates to workflows, responsibilities, and governance policies. Don’t let this be an afterthought.
  • Celebrate wins and learn from setbacks: Recognize early successes to build momentum. When things don’t go as planned, treat it as a learning opportunity.

The goal isn’t just to change your org chart – it’s to create a more effective, efficient, and adaptable data organization. Keep your eye on the prize, and don’t forget to enjoy the journey.

Your Data Journey Awaits

Building the right data and insights team is a journey, not a destination. Whether you choose a centralized powerhouse, a decentralized network, or a hybrid approach, the key is to align your team structure with your business goals and culture. There’s no “perfect” solution – only the one that works best for your organization.

Are you ready to embark on this adventure? Maybe one day, you’ll look back on this journey and realize that building your data team was the most fun you’ve ever had with spreadsheets.

For more columns from Michael Bagalman’s Data Science for Decision Makers series, click here.

Data-Driven: A Winning Formula for Innovation

Closeup of the side of a sleek red racecar.

“Max is a dynamic individual who excels not only as a data governance executive but also as an athlete. He’s well placed to share that capturing key information and then interrogating that data leads to winning, whether it be in the boardroom or on the racetrack,” says Seth Adler, Editor-in-Chief, All Things Innovation.

High Performance Feelings

Building good structure, leadership, data practices and methods of working can improve performance on and off the racetrack. Goals from the racing side can be re-utilized in the corporate world, and vice versa.

Of racing performance and the precision of data science, Just says, “Data drives our decisions, including those with the feelings that we get out of the race car. I would argue that is an input, that it’s just less factual as what I get out of the data system. But the combination of both things, the feeling and the data elements that I get out of the car are those combinations. I use them to make decisions on what I need to do differently and what the car needs to do differently so that the next time we go out, our lap times are better.”

Marrying Profession & Passion Through Data

Driving is his passion. Data is his profession. All Things Innovation aimed to find out similarities between the two.

Just says, “In the past, if you think about it, businesses operated out of intuition. You had a strategy. You believed in that strategy. And then through some facts, some feelings, you would make decisions on how to take the company forward. But the reality is that as the times go by and we have so many new technologies that we use to run the company, the information that we get is a lot more rich than what it was 20 years ago. The companies that are truly successful are doing what we do here on the racetrack. They use that data to make decisions to improve both internally and externally, and those that don’t are struggling to cope with the times.”

Speed to Insight

“You cannot just blindly run data models and believe on the output software, because you must understand the context of it and be able to interpret the results of those models and pick a good one, one that perhaps isn’t as good, but perhaps carries on the road,” says Just. “Speed to insight. From the point you capture the data to the point that you make a conclusion and take action, it got much shorter. However, companies want to make the leap towards decision making and being data driven, as an organization. Not all companies are able to do that. They must make conscious decisions to become data driven.”

Revving Up Collaboration

“What I bring to the table is the driving element,” says Just. “Tanner brings in the knowledge and intimate knowledge on how the car is built, and Kevin brings the knowledge on how to set up the car. Between the three of us, if we weren’t collaborating and communicating effectively with each other on how to do things better, what I need to do differently, what the car needs to do differently. Leadership is what gets the group together, what allows us to set that winning mindset as a group.”

“Winning in the corporate environment isn’t winning a race, but it’s achieving another level of performance. It’s achieving a particular target, implementing a new system. Whatever that is, it must be hard, and we all need to be committed towards doing whatever we have to do to achieve it. As long as there is trust, you can achieve very difficult things.”

Driving a Winning Mindset

“What is a winning mindset to you?” asks Just. “In this context, it is to do everything you can to try to get the victory and to show up here thinking and visualizing yourself as going to the finish line first. I do feel that coming in thinking that you will win gets you closer. You may not get 100% to the objective in the end, but if you get 80% or 90% towards it in this one try, it’s progress.”

Editor’s Note: Check out the “Fast Data” preview in this blog. For the full Fast Data video, click here to visit our Discussions section, as Seth Adler and Max Just put the pedal to the metal to discuss the world of data science and racecar driving.