5-minute read
Today we kick off a two-part series exploring how balancing data insights with human intuition through a culture of data influence can drive smarter, more adaptable decision making. In Part 1, we focus on the evolution of business decision making, the two extremes that framed it, and the happy medium that offers an optimal approach.
The rise of data tools and analytics promised to revolutionize decision making, yet many organizations find themselves less than satisfied with the outcomes. Despite significant investments in data infrastructure and analytics platforms, many companies are still struggling to turn vast amounts of data into actionable insights.
Consider the following results from Salesforce’s most recent State of Data Analytics report:
- 94 percent of business leaders feel their organization should be getting more value out of its data.
- 78 percent of analytics and IT leaders say their organizations struggle to drive business priorities with data.
- At the same time, over two-thirds of analytics and IT leaders expect data volumes to increase by an average of 22 percent over the next year.
These statistics highlight a growing disconnect between the potential of data and the reality within organizations. As data volumes continue to surge, the complexities of managing and leveraging this information will only become more pronounced.
The future of decision making lies in achieving a balance—combining the intuition of experts with the power of data to create a culture of data influence.
Setting the stage: The evolution of business decision making
Business decision making has evolved significantly over time, transitioning from instinctual methods to a heavy reliance on data analytics. Along the way, organizations have learned the value—and limitations—of both approaches, highlighting the need for a balanced, integrated model.
The traditional approach: Intuition and experience
For much of history, decisions were driven by the expertise and instincts of subject matter experts or the “HiPPO”—the highest-paid person’s opinion.
Scenario: Picture a meeting where outcomes are determined by gut feelings, personal anecdotes, or the professional judgment of a seasoned leader. While efficient in some contexts, this approach lacks objective verification.
Strengths:
- Leverages years of industry knowledge and hands-on expertise.
- Allows quick decision making, especially when data is sparse or unavailable.
Shortcomings:
- Subjectivity and inconsistency: Decisions often vary widely depending on the people making them.
- Scalability challenges: Heavily reliant on specific individuals, the approach becomes fragile when those individuals leave the organization or transition to new roles.
- Lack of accountability: Without supporting evidence, it can be difficult to validate decisions or measure their effectiveness.
While intuition and experience remain valuable, the absence of standardized processes and data-backed insights can lead to inefficiencies and misaligned priorities.
The pendulum swings: The “data driven” era
In more recent decades, organizations have embraced data-driven decision making, viewing analytics as a cure-all for inefficiencies and biases. This shift was propelled by the rise of advanced tools and the promise of objective, scalable decision making.
Scenario: Businesses funnel resources into data collection, analytics platforms, and dashboards, making metrics the centerpiece of decision making. Leaders aim to eliminate subjectivity by letting the numbers speak.
Strengths:
- Evidence-based decision making: Decisions grounded in measurable data reduce personal bias and improve accountability.
- Scalability and objectivity: Standardized data-driven frameworks enable consistent decisions across teams and geographies.
Shortcomings:
- Heavy reliance on data quality: Flawed or incomplete data leads to misinformed decisions.
- Loss of human context and creativity: The “New Coke” failure from the 1980s remains a cautionary tale of over-reliance on data. Coca-Cola relied too heavily on taste-test data, ignoring the emotional connection customers had to the original product. A purely data-driven business also has no way to recognize the value of truly innovative products such as the iPhone.
- Cultural friction: Employees often struggle to use data tools effectively, and many lack trust in analytics processes. This mismatch between leadership’s expectations and employee readiness creates resistance and uneven adoption, undermining data-driven initiatives.
The dangers of data-only approaches were starkly illustrated during the 2010 “flash crash” on Wall Street. Automated trading algorithms—operating without human oversight—exacerbated market fluctuations, causing a substantial drop within just a few minutes. The lack of human checkpoints to pause or review decisions turned a small anomaly into a massive market disruption.
These challenges expose the limits of relying solely on analytics. Data-driven decision making can be transformative, but without human insight and contextual understanding, its potential is restricted.
Finding balance: The case for data influence
The shortcomings of intuition-only and data-only approaches highlight the need for a middle ground: data influence. This hybrid model combines the best of human judgment with the power of analytics, creating a framework for decision making that is both informed and adaptable.
Defining data influence
Data influence bridges the gap between intuition and analytics by integrating the strengths of both.
- A hybrid approach: It combines data insights with human intuition, ensuring decisions are guided by evidence while maintaining contextual awareness.
- Avoids extremes: Unlike intuition-only decision making, which risks subjectivity, and data-only approaches, which lack context, data influence provides a balanced perspective.
Benefits of data influence
Organizations that adopt data influence reap a range of advantages as they capitalize on the unique strengths of both human expertise and analytics.
- Leverages both experience and data: Data influence enables decision makers to validate their instincts with objective insights, while also applying human context to interpret complex data.
- Creates checks and balances: Analytics can confirm or challenge intuition-based assumptions to reduce bias, while human expertise ensures data-driven conclusions are grounded in real-world nuances.
This balance fosters more effective, informed, and flexible decision making across the organization.
Use cases and examples of data influence
Wildfire Mitigation Programs (WMPs)
In wildfire mitigation programs, decision makers use advanced risk models to predict high-risk areas and recommend preventive measures. However, the final call on activating mitigation strategies requires human judgment. Leaders combine model outputs with on-the-ground insights to ensure decisions align with real-world conditions, such as weather changes and resource availability.
Supply chain adaptation
In one of our clients’ supply chain, discrepancies in data initially appeared as inefficiencies. However, discussions with frontline workers revealed that these teams were proactively adjusting procurement processes to keep pace with rapidly changing technology cycles. Taking action based on the data alone might have resulted in missteps that disrupted critical deployments. Leadership was alerted to an operational factor they had not been aware of and used this information to initiate a conversation with their experts in the field rather than making a decision blindly.
Investment decisions
A leading investment firm exemplifies data influence by incorporating structured checkpoints in its trading processes. While its advanced risk models guide decisions, human experts regularly assess market conditions to ensure strategies remain aligned with broader financial goals. This blend of analytics and human oversight has driven consistent, industry-leading performance for decades.
A new way forward
Data influence is the sweet spot where intuition and analytics meet, creating a decision-making process that is both robust and adaptable. By finding this balance, organizations can make smarter, more effective decisions while avoiding the pitfalls of relying solely on instinct or data.
In Part 2 of this series, we’ll explore how organizations can create a data culture that supports data influence and provide actionable steps for getting started.
Put your data to work for you
We bring together the elements that transform your data into a strategic asset—and a competitive advantage:
- Data strategy
- Data science
- Data engineering
- Visual analytics
Mick Wagner is responsible for leading the Advanced Analytics Practice to exceed client expectations, develop innovative solutions, and achieve organizational growth. Mick has over 15 years of data analytics consulting experience across the full BI lifecycle.
Matt Kieffer brings over a decade of expertise in the energy and technology industries to his role as Manager in Logic20/20’s Advanced Analytics practice. He specializes in transforming complex data into actionable business insights, driving measurable outcomes such as cost savings, process efficiencies, and improved performance metrics.
