Data Driven Decisions for Innovation

We are constantly reminded of familiar truths: strategy is choosing what not to do; repeatable processes drive efficiency; efficiency frees resources for innovation; data is an asset; talent is scarce; and technology, ultimately, is only a tool. These statements are echoed so often that they feel self-evident, each one reasonable, each one meaningful. Yet when we attempt to assemble these truths into a coherent model to inform our strategy, the pieces often don’t fit.

In an era defined by digital disruption, data-driven decision-making transforms how leaders prioritize, allocate resources, and measure success. It provides the foundation for sustainable innovation by augmenting intuition with evidence, enabling organizations to navigate uncertainty and unlock the power to continuously learn, adapt, and evolve.

The Quest

In today’s environment of constant information overload, both generalists and specialists struggle to make sense of competing ideas. Concepts blur together, knowledge creation is outsourced to crowds, and decision-making becomes reactive, guided more by trends and opinions than by evidence and intent.[1] Yet despite this chaos, leaders remain accountable for delivering meaningful outcomes, driving innovation, and contributing to the bottom line every single day.

Leading a technology function can sometimes feel easier than leading a line of business. After all, nearly anything can be implemented with the right strategy, especially when pursued incrementally. Defining a strategic direction, however, is far more complex in a world of fierce competition and constant uncertainty. Every business decision carries accountability, shaping what we ask our technology teams to deliver. Yet strategy is never a one-way street, it is a shared journey influenced by countless factors on both sides and, most importantly, by how we align them together.

Much has been written about innovation, and even more about data, yet few organizations actively invest in the fundamental building blocks that make either truly effective. We often chase cutting-edge technologies, inspired by others’ success, without ensuring that our own data and processes are ready to support their adoption. Do we have access to the information we need? And can we achieve consistent, repeatable innovation that builds on the advancing platforms into which we have invested?

From Science to Reality

Perhaps examining each component of what it means to make data-driven decisions and how to innovate using data can illuminate the path ahead. Without diving into neuroscience or statistics, we can still trace some underlying driving forces that matter when we apply these components to real-life challenges.

i. Opinion-Based Decision Making

In our daily lives, we navigate countless moving pieces that shape our decisions, at work and at home alike. We manage endless to-do lists, reorganize priorities, and re-evaluate our choices hour by hour. Psychology inevitably enters this equation: we make roughly 35,000 decisions each day,[2] and in doing so, we begin to rely on shortcuts and patterns that help us cope with complexity. Past experiences, present pressures, and deeply rooted biases all influence this decision flow, shaping our behaviors in ways that are not always noticeable.

Even the most mindful among us are subject to a vast array of cognitive biases spanning the subconscious and the knowledge we’ve acquired over time. Our memories selectively reinforce impressions rather than facts, making it difficult to recognize when a mistake has been made, or a decision has been swayed.[3]

Similarly, organizations are often overwhelmed by an ever expanding array of conflicting priorities. Inundated by an endless parade of new stories, articles, thought pieces, social media posts and evolving narratives, all claiming to offer the next big breakthrough. Amid this noise, we often debate, question, or dismiss frameworks as tools that could, in fact, provide the structure needed to move the needle.

In addition, the well-known “highest-paid person’s opinion” phenomenon [4] often undermines objective decision-making. In mid-sized organizations, this role typically falls to senior executives or line-of-business leaders who, while navigating uncharted territory in search of efficiencies or new routes of innovation, are frequently expected to make critical decisions without the benefit of reliable data insights; a challenge that makes success far less certain.

Of course, organizations that fully embrace established frameworks such as TOGAF, CMMI, OBA or Kaizen [5-8] have structured, proven processes to mitigate the impact and maximize the benefit of individual choices, while maintaining strategic consistency. Even data management weaves itself seamlessly into the broader organizational fabric, enabling transformation and a shift from output-based activity to outcome-driven impact.

Charter Case Study: A large international healthcare organization on a digital transformation path has embraced TOGAF to blueprint its enterprise architecture. When mapping their data domain capabilities to the business priority of going paperless, they tapped into 25 years of research documents and thousands of handwritten notes kept warehoused in boxes. This resulted in new insights into healthcare treatments, created efficiencies, and improved regulatory compliance, with the culture shift still underway.

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However, when these frameworks are applied sporadically, their value often appears limited. Transformation can seem like a luxury reserved for large organizations with the capacity to invest deliberately in such efforts. Yet one might ask: is committing to a strategic vision not easier when everyone in the organization understands that key decisions are grounded in data-driven evidence? And are we prioritizing our data-related initiatives high enough to shift the investments, funding, and resource capacity to support them?

Jim Barksdale, former CEO of Netscape, captured this tension succinctly years ago: “If we have data, let’s look at data. If all we have are opinions, let’s go with mine.”[9] His words highlight the ongoing struggle between intuition and evidence, between bias and insight. When this mindset takes hold, it can suppress another vital organizational force: culture, the dynamic expression of leadership that influences daily decision making and knowledge sharing.

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ii. Leveraging Data-Driven Insights

We often visualize concepts using pyramids. Our minds instinctively organize information hierarchically, and the well-known Knowledge Pyramid is no exception.[10] Its layers (data, information, knowledge, and wisdom) each represent a distinct domain that profoundly influences the journey toward data-driven decision-making. Yet these layers are not as linear as their shape suggests; in practice, they interact dynamically, continuously shaping and informing one another.

Information is often described as “data in context,” the transformation of raw facts into meaningful form. Knowledge, in turn, is both a prerequisite for creating data and the outcome of interpreting and applying information.[10] Yet knowledge loss remains a largely unmeasured risk. While most organizations acknowledge its impact, their efforts to preserve and share knowledge are frequently fragmented and reactive rather than coordinated and strategic.

Data and information exist in a continuous exchange, each shaping and reinforcing the other, and both must be actively managed and improved to ensure consistency and reliability. A strong, trusted dataset can elevate every layer of this continuum, empowering the core knowledge holders who embody organizational wisdom. Yet too often, these same experts become bottlenecks to decision-making rather than enablers of it.

In recent years, the conversation around digital disruption, artificial intelligence, machine learning, and quantum computing has intensified. Yet these transformative technologies trace their origins to the research breakthroughs of the 1950s and 60s, decades of exploration that continue to shape today’s innovation landscape. Among the most compelling insights for the business world is the clear difference in the quality of decisions made by experts relying solely on opinion versus those enhanced through data-driven intelligence.[11]

Data has become a true strategic differentiator, demanding a cultural shift at every level of the enterprise. In this context, data diplomacy and data democratization are no longer a passing trend, but a defining source of competitive advantage.[12] Becoming truly data-driven is not merely a technological evolution; it is a cultural and strategic one. It requires leaders to challenge often held assumptions, bridge the gap between business and technology, and build trust in data as a shared organizational asset.

When we turn to the numbers, even limited but credible data sets tell a compelling story. A PwC survey of more than 1,000 senior executives found that highly data-driven organizations[13] are three times more likely to report significant improvements in decision-making compared to those that rely primarily on opinion, intuition, or experience.[14] Evidence of this trend spans every industry, and data-driven decision making consistently correlates with stronger business performance.

Charter Case Study: A specialized oil and gas sector services company custom-built a predictive model for its formerly all-manual workflows performed by an expert with a unique skillset. The trusted “gold records” were established, bespoke model trademarked, the workflow efficiency improved from hours to minutes, and the resulting revenue growth enabled a cross-border expansion, all in 18 months from the initiative start.

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The advantage is measurable: algorithms and data sets can be tested, sources can be verified, and outcomes can be tracked to continuously refine future decisions. Most organizations already collect and analyze data to measure success, yet few fully harness it to drive meaningful progress or to advance innovation. The real opportunity lies in breaking free from the comfort of using data merely to validate existing opinions, and instead, leveraging it to challenge assumptions, reveal insights, and make better, more informed decisions.

Organizations can begin building their own proofs of concept through thoughtful digital transformation initiatives, leveraging established frameworks, and the ever-expanding pool of external data. With that, it is essential to recognize that not all decisions or predictions can, or should, be made by machines. Human oversight remains indispensable, grounded in business knowledge and domain expertise that guide our choices, including ethical ones. Yet, when reliable data is available, leveraging it consistently delivers long-term advantages. The balance between human judgment and machine intelligence is what propels modern organizations forward.[11, 15] Many compelling examples illustrate how this partnership advances business performance and innovation alike. And sometimes, this does not even require a massive investment, only the discipline to remember a simple truth: data holds economic value only when it leads to a different, better decision.

iii. Underpinning Innovation

Next, we turn to the concept of innovation, not as a spontaneous thought spark, but as a structured, deliberate process of developing and testing ideas. Depending on the type pursued, whether process-driven or technology-driven, innovation aligns business objectives with the value created across the “core knowledge vs. crowd input” spectrum.[16] Just as with data management, organizational culture plays a decisive role. Successful innovation demands strong executive sponsorship, the willingness to decentralize and collaborate across functions, and the confidence to experiment, even when some initiatives do not deliver as promised.

An innovation-focused culture naturally accelerates the shift toward data-driven decision making. It exposes the need for trusted, high-quality data and drives transformation by applying technology in ways that enable new and improved business models. So, in the end, data is not just the enabler of innovation, it is the foundation upon which it is built. As organizations prepare their data for AI and other emerging technologies, they quickly discover that effective data management forms the essential foundation for achieving their data quality, privacy, and security goals. And like any strong foundation, it requires thoughtful investment. Ultimately, innovation exists for one purpose: to create lasting value.

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Charter Case Study: An educational entity set out to modernize their youth-centered social intervention program. They faced several fundamental challenges, such as fragmented operations, large amounts of data unused due to privacy concerns, and elusive support outcomes. To overcome these issues, the fusion teams created innovative processes and solutions while supporting the privacy and accessibility requirements of the sector. Streamlined community forms intake, process automation, risk screening, mobile optimization, and custom reporting are but a few of the long-term benefits that this entity is now utilizing to deliver its critical and valuable community program.

Your Roadmap to Success

Organizations constantly evolve from opinion-based to data-driven decision making.[17] This transformation extends beyond technology, it demands cultural change, strong leadership commitment, and structured processes supported by disciplined data frameworks.

As complex as the journey may appear, the path forward is far from insurmountable. Once we understand the factors that impact, impede, or influence our progress, we can establish clear priorities and design our organization-specific roadmap to move from point A to point B with confidence and purpose.

When an organization establishes a well-defined data strategy, it gains the ability to allocate resources effectively toward initiatives that drive measurable value. Each step toward a data-driven decision making model helps minimize risk, uncover opportunities, and strengthen organizational resilience. Achieving this requires clarity of purpose (defining objectives, identifying relevant data sources, and collecting and analyzing insights)[18] while keeping in mind the other critical pieces of the puzzle along the way:

• Business Objectives: The business ultimately owns the data, and IT must ensure that every data-related initiative directly supports those objectives and drives measurable outcomes.
• Leadership Commitment: Effective data management requires ongoing leadership engagement, not just advocacy, but active participation and accountability.
• Ethics: As organizations strive to eliminate bias, data ethics evolves to ensure that decisions driven by data remain transparent, fair, and responsible.
• Investment: Data ownership must be matched with dedicated funding, empowering data stewards to establish sustainable processes and manage information as a long-term asset of the organization.
• Technology: Every technology initiative, including AI, requires a clearly defined total cost of ownership and a business case that aligns with strategic goals.
• Measurement: Demonstrate the tangible value of data by managing it as a business asset and expressing its impact in clear economic terms.
• Strategic Alignment: Ensure all these elements operate cohesively through an enterprise-wide alignment model that integrates data, strategy, and execution seamlessly and collectively.

Reflecting on the experiences of our customers and the insights of leaders across diverse data domains, one truth stands out: technology may be a powerful catalyst, but it is business insight that genuinely enables innovation. This raises a fundamental question, "Where should we begin: with innovation or data management?"

While data-driven decision-making is widely recognized as a stimulus for innovation, the reality is that both serve as complementary pathways toward the same destination: a competitive, resilient, and thriving business.[19] The journey begins with a compelling story that resonates across your organization, followed by the adoption of a framework that unites understanding and action. Along the way, uncovering hidden talent to champion these efforts will accelerate success. Conducting meaningful research and applying its findings to the next cycle may be the harder part, but every step forward adds measurable value. Through continuous improvement and repeatable processes, the uncertainty fades, revealing what was there all along: a clear, attainable roadmap to sustainable transformation.

At Charter, we architect for innovation using Design Thinking and apply our unique Charter Data Enablement Methodology (CDEM) as a practical, outcome-driven approach to data strategy and governance.[20, 21] This framework helps organizations address their most complex data management and utilization challenges while unlocking the full value of their existing data assets. We empower our customers to make the most of what they have today, while confidently building the capabilities they need for tomorrow.

* Sources - see below.

About the author

‍Olga Laren, Senior Business Architect, TOGAF, ITIL, CDMP

Olga Laren is a Senior Business Architect with more than 20 years of experience leading business transformation and digital strategy across diverse industries. She specializes in business and enterprise architecture, technology migration, and complex systems development for both public and private sector clients, consistently aligning innovation with measurable outcomes.

At Charter, Olga drives digital transformation initiatives that modernize business models, establish strong data management, and deliver sustainable technology solutions. An advocate for innovation and inclusive design, Olga continues to bridge business vision with technology execution to enable meaningful, lasting transformation.

An advocate for accessibility, innovation and inclusive design, Olga continues to bridge business vision with technology execution to enable meaningful, lasting transformation

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Sources*

[1] Gantner, M. (2025, August 15). The Intelligence Imperative: Making Data A Catalyst For Decisions. Forbes. https://www.forbes.com/councils/forbesbusinesscouncil/2025/08/15/the-intelligence-imperative-making-data-a-catalyst-for-strategic-decisions/

[2] How Many Decisions Do We Make Each Day? (2018). Psychology Today. https://www.psychologytoday.com/us/blog/stretching-theory/201809/how-many-decisions-do-we-make-each-day?msockid=1cdcbbb1b6406ad82c85ae67b7b06bda

[3] Cognitive Biases - Sensemaking Resources, Education, and Community. (2021, February 21). Sensemaking Resources, Education, and Community. https://sensemaking101.com/knowledge-base/cognitive-science/cognitive-biases/

[4] Brynjolfsson, Erik and McElheran, Kristina. 2016. "The Rapid Adoption of Data-Driven Decision-Making." American Economic Review, 106 (5): 133–39.

[5] The Open Group. (2025). TOGAF | The Open Group. Opengroup.org. https://www.opengroup.org/togaf

[6] CMMI Institute. (2019). CMMI Institute - Home. Cmmiinstitute.com. https://cmmiinstitute.com/

[7] Secretariat, T. B. of C. (2007, October 17). Outcome Management Guide and Tools. Www.canada.ca. https://www.canada.ca/en/treasury-board-secretariat/services/information-technology-project-management/project-management/outcome-management-guide-tools.html

[8] Operational Excellence And Management Consulting | Kaizen. (2023, March). Kaizen.com. https://kaizen.com/

[9] Schmidt, E. (2016). How google works. Grand Central Publishing.

[10] DAMA® Data Management Body of Knowledge (DAMA-DMBOK®). (2025, May 27). DAMA International®. https://dama.org/learning-resources/dama-data-management-body-of-knowledge-dmbok/

[11] McAfee, A., & Brynjolfsson, E. (2017). Machine, Platform, Crowd. Wwnorton.com. https://wwnorton.com/books/Machine-Platform-Crowd/

[12] Lopez, Marilu. 2023. “Data Strategies for Data Governance”. Technics Publications LLC.

[13] 5 Data and Analytics Actions For Your Data-Driven Enterprise. (n.d.). Gartner. https://https://www.gartner.com/en/information-technology/insights/data-and-analytics-essential-guides

[14] Stobierski, T. (2019). The advantages of data-driven decision making. Harvard Business School Online. https://online.hbs.edu/blog/post/data-driven-decision-making

[15] Periathiruvadi, G. (2024, September 3). Council Post: Unlocking Success: The Power Of Data-Driven Decision-Making. Forbes. https://www.forbes.com/councils/forbesfinancecouncil/2024/09/03/unlocking-success-the-power-of-data-driven-decision-making/

[16] Han, E. (2022, January 18). What is design thinking & why is it important? Harvard Business School Online. https://online.hbs.edu/blog/post/what-is-design-thinking

[17] Seiner, Robert S. 2014. “Non-invasive Data Governance”. Technics Publications LLC.

[18] Holcman, Samuel B. 2025. “Business Architecture: The Enabler of Business Strategy”. Pinnacle Business Group Inc, Business Architecture Center of Excellence (BACOE.org)

[19] Drive Success with Enterprise Architecture Strategy | Gartner. (2025). Gartner. https://www.gartner.com/en/information-technology/trends/redesign-enterprise-architecture-to-support-distributed-decision-making

[21] Christensen, Clayton M. 2016. “The innovator’s dilemma: when new technologies cause great firms to fail”. Harvard Business Review Press.

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