Top Companies’ Evidence-Based Decision Making to Boost Performance

Article At-A-Glance

• Companies that employ evidence-based decision making see up to 50% higher revenue growth and improved profit margins compared to intuition-driven competitors
• Nearly 60% of businesses still base half their regular business decisions on gut feel rather than data, missing significant growth opportunities
• Real-time analytics provide top-performing organizations with actionable insights that drive more responsive customer experiences and operational efficiencies
• Organizations like Amazon, Google, and Toyota have developed proprietary evidence-based frameworks that transform raw data into strategic advantages
• DataForce Analytics helps companies implement evidence-based decision-making systems that drive measurable performance improvements within 90 days

In business, the difference between good and exceptional performance often comes down to how decisions are made. While intuition has its place, the most successful organizations are increasingly those that systematically harness data to drive their decision-making processes. In today's fast-paced business environment, making informed decisions is more crucial than ever. Evidence-Based Decision Making (EBDM) is a systematic approach that relies on data, facts, and empirical evidence to guide business choices. For any ISO certified company, data analysis is a key component of the management review.

Evidence-based decision making (EBDM) transforms how companies operate, shifting from opinion-driven choices to those grounded in verifiable facts and empirical evidence. This approach isn't just a modern business trend—it's becoming the defining characteristic of market leaders across industries. Embedding evidence in every business move is a powerful strategy that transforms decision-making. By adopting EBDM, organizations can enhance accuracy, efficiency, and strategic alignment. Leaders who champion this approach foster a culture of insight, innovation, and impact.

In today's fast-paced business environment, making informed decisions is more crucial than ever. Evidence-Based Decision Making (EBDM) is a systematic approach that relies on data, facts, and empirical evidence to guide business choices. By embedding evidence in every decision, organizations can enhance their strategic outcomes, reduce risks, and drive sustainable growth. Evidence-Based Decision Making is not just a business best practice—it is embedded in internationally recognized frameworks such as ISO 9001:2015. The standard identifies ‘Evidence-Based Decision Making’ as one of its seven Quality Management Principles, encouraging organizations to make decisions grounded in data and analysis. Whether improving a process, allocating resources, or managing risks, an evidence-driven approach strengthens the foundation for continual improvement and sustainable success. Did you know that the word monitor is used 30x in the ISO Standard?

The Importance of Evidence-Based Decision Making

• Enhancing Accuracy and Consistency: EBDM eliminates bias and ensures decisions are grounded in reliable data.
• Improving Efficiency and Effectiveness: Data helps identify trends and optimize operations.
• Supporting Strategic Goals: Aligns decisions with long-term objectives.

How Top Companies Use Data to Make Million-Dollar Decisions

The stark reality of modern business is that organizations must either adapt to data-driven decision making or risk falling behind competitors who do. Leaders in evidence-based approaches don't just collect data—they create integrated systems where information flows seamlessly from collection to analysis to action, often in real-time.

DataForce Analytics research reveals that companies implementing rigorous evidence-based decision frameworks consistently outperform their market peers on key performance indicators, including customer retention, operational efficiency, and innovation metrics. The competitive advantage isn't subtle—it's transformative.

The 50% Revenue Growth Premium for Data-Driven Organizations

McKinsey analysis demonstrates that companies fully embracing data-driven decision making achieve remarkable performance advantages. Top-quartile companies that leverage trusted, accessible data for real-time decision-making consistently show more than 50% higher revenue growth and net margins compared to competitors relying on traditional approaches.

This performance gap is particularly pronounced in volatile markets where conditions change rapidly. Evidence-based organizations can quickly identify emerging patterns, adjust strategies, and capitalize on opportunities that intuition-driven companies might miss entirely.

• 50% higher revenue growth for data-driven organizations
• Significantly higher net profit margins
• Greater agility during market disruptions
• More accurate forecasting capabilities
• Better resource allocation efficiency

What's particularly telling is how this advantage compounds over time. As evidence-based companies build deeper data repositories and more sophisticated analytical capabilities, they develop institutional knowledge that becomes increasingly difficult for competitors to replicate.

Real-Time Decision Making vs. Traditional Approaches

The evolution from periodic analysis to continuous data-driven decision making represents a fundamental shift in how business operates. Traditional approaches relied on quarterly reviews and historical data, creating significant lag between information availability and action. Today's leading companies have collapsed this timeline dramatically.

Real-time decision making enables digital customer journeys that are more seamless, empowered employee experiences, and agile operations that can adapt to changing conditions within hours rather than weeks. The top-performing organizations have automated key processes and democratized access to data, enabling employees at all levels to make evidence-based decisions within clear frameworks.

The contrast in operational tempo between real-time and traditional decision-making approaches explains much of the performance gap between market leaders and followers. While traditional companies are still analyzing last quarter's results, evidence-driven organizations are already optimizing today's performance and testing tomorrow's innovations.

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Why Most Companies Still Fail at Evidence-Based Decision Making

Despite the clear advantages, a surprising 58% of companies still base at least half of their regular business decisions on gut feel or experience rather than data. This resistance to evidence-based approaches isn't simply stubbornness—it reflects deep organizational challenges that even well-intentioned leaders struggle to overcome.

The transition to evidence-based decision making requires both technical infrastructure and cultural transformation. Many organizations invest heavily in data collection systems but fail to build the analytical capabilities and decision frameworks needed to translate that data into actionable insights.

The Cost of Gut Feelings: $100M+ Annual Losses in Fortune 500 Companies

The financial impact of intuition-based decision making is staggering when measured objectively. Fortune 500 companies routinely lose $100 million or more annually through suboptimal decisions that could have been improved through rigorous evidence-based approaches. These losses manifest in everything from failed product launches to inefficient operations and missed market opportunities.

What makes these losses particularly troubling is their preventable nature. In most cases, the data needed to make better decisions already existed within the organization but wasn't properly leveraged. The gap between data availability and data utilization represents one of the most significant untapped value opportunities in modern business. For a deeper understanding, explore continuous improvement strategies that emphasize data utilization.

Data Overload: When Too Much Information Paralyzes Action

Paradoxically, one of the biggest barriers to evidence-based decision making is the sheer volume of data available to modern organizations. Many companies are drowning in information without effective systems to identify which metrics actually matter. This data overload frequently leads to analysis paralysis, where teams become so overwhelmed by options that they default back to intuition-based approaches.

Successful evidence-based organizations don't simply collect more data—they're strategic about what they measure and how they structure their analysis processes. They develop clear data governance frameworks that distinguish between essential metrics and interesting but non-actionable information.

Cognitive Biases That Derail Even Experienced Executives

Human cognitive biases present perhaps the most persistent challenge to evidence-based decision making. Confirmation bias leads executives to prioritize data that supports their existing beliefs while discounting contradictory evidence. Status quo bias creates resistance to change even when data clearly indicates the need for new approaches.

Even experienced leaders who intellectually understand the value of evidence-based approaches often fall victim to these biases in practice. Addressing these tendencies requires both awareness and systematic decision processes designed specifically to counteract common cognitive pitfalls.

Organizational Silos: The Evidence Killer

Information silos within organizations frequently undermine evidence-based decision making by fragmenting critical data. When departments maintain separate data systems with limited cross-functional visibility, the organization cannot develop a comprehensive understanding of its operations or market position.

Breaking down these silos requires both technological solutions for data integration and organizational changes that incentivize information sharing. Companies that excel at evidence-based decision making typically establish cross-functional data teams with explicit mandates to synthesize insights from multiple sources.

5 Evidence-Based Decision Making Models That Drive Results

The most successful organizations don't just collect data—they develop systematic frameworks for translating information into action. These evidence-based decision models provide structured approaches to gathering, analyzing, and applying data to business challenges, similar to how continuous improvement frameworks are implemented in various industries.

1. Toyota's Kaizen Model: Continuous Improvement Through Data

Toyota's legendary Kaizen system represents one of the most successful implementations of evidence-based decision making in manufacturing history. Their approach centers on continuous micro-improvements driven by rigorous data collection at every step of the production process. What distinguishes Toyota's model is how it democratizes data-driven decision making, empowering frontline workers to identify issues and propose evidence-based solutions.

The company maintains detailed metrics on over 800 production variables across its manufacturing operations, allowing teams to isolate root causes of quality and efficiency issues with remarkable precision. This granular evidence-based approach has helped Toyota maintain industry-leading quality metrics while continuously reducing production costs—a competitive advantage that competitors have struggled to replicate despite decades of trying.

2. Google's Project Oxygen: Data-Driven People Management

Google's Project Oxygen demonstrates how evidence-based approaches can transform even traditionally subjective areas like people management. The initiative began with a simple question: do managers actually matter? Rather than relying on conventional wisdom, Google analyzed performance reviews, employee surveys, and productivity metrics to identify precisely what behaviors distinguished effective managers.

The resulting framework identified eight key behaviors that drive team success, each supported by clear metrics and evidence. This data-driven approach to management development has helped Google maintain employee satisfaction scores 32% above industry averages while reducing voluntary turnover by 17%. The Project Oxygen framework exemplifies how rigorous evidence-based approaches can bring clarity to complex organizational challenges.

3. Amazon's Working Backwards Framework

Amazon has institutionalized evidence-based decision making through its “Working Backwards” framework, which begins every initiative with a mock press release and FAQ document describing the completed project. This approach forces teams to clearly articulate success metrics before implementation begins. Once development starts, teams track specific customer experience and performance metrics, comparing actual outcomes against their initial hypotheses.

The framework's genius lies in how it integrates data collection into the product development lifecycle from conception through launch and beyond. Amazon's rigorous A/B testing methodology allows teams to make evidence-based adjustments during development, significantly increasing success rates for new products and features. This approach has helped Amazon achieve a 74% higher success rate on new initiatives compared to industry averages.

4. Vanguard's Investment Strategy Process

Vanguard's investment management methodology demonstrates how evidence-based decision making can create competitive advantages in financial services. Rather than chasing market trends or relying on star portfolio managers, Vanguard has built systematic processes that evaluate investment opportunities through multiple quantitative and qualitative lenses. Their approach incorporates historical performance data, economic indicators, and probability-weighted scenario analysis.

What makes Vanguard's model particularly effective is its emphasis on separating signal from noise in financial markets. The company maintains a disciplined focus on long-term evidence rather than short-term fluctuations, allowing them to make counter-cyclical investment decisions that have consistently outperformed market benchmarks by approximately 1.7% annually over 30+ year periods.

5. United Airlines' Real-Time Business Transformation

United Airlines' operational transformation illustrates how evidence-based decision making can revitalize established companies in traditional industries. The airline implemented a real-time data hub that integrates information from over 100 previously siloed systems, providing decision-makers with comprehensive visibility into operations. Flight crews, gate agents, and maintenance teams now access the same integrated dashboard, enabling coordinated responses to disruptions.

This evidence-based approach has reduced delay minutes by 27% and decreased maintenance-related cancellations by 42% since implementation. The system's predictive capabilities allow United to anticipate potential disruptions and proactively reallocate resources, significantly improving both operational efficiency and customer satisfaction metrics.

The Evidence-Based Decision Framework: 4 Essential Steps

While each company develops its own approach to evidence-based decision making, the most successful implementations share four fundamental components that form a complete framework. These elements work together to create a continuous cycle of improvement rather than isolated analytical events.

1. Define Clear Metrics That Actually Matter

Effective evidence-based decision making begins with identifying the right metrics to track. This requires a deep understanding of which variables truly drive business outcomes versus those that merely correlate with success. Leading organizations distinguish between vanity metrics that look impressive but lack actionable insights and performance indicators that directly link to strategic objectives.

The most sophisticated companies develop metric hierarchies that connect frontline activities to strategic outcomes through clear causal relationships. This approach enables decision-makers at all levels to understand how their specific actions contribute to organizational goals and provides a common language for evaluating potential initiatives.

2. Gather Both Qualitative and Quantitative Data

Comprehensive evidence incorporates both hard numbers and contextual information. Quantitative data provides statistical reliability and pattern recognition, while qualitative insights reveal the “why” behind the numbers. Organizations that excel at evidence-based decision making develop systematic approaches to collecting both types of information.

The most effective data gathering systems balance breadth and depth, collecting enough information to identify meaningful patterns without creating information overload. They also incorporate multiple perspectives, recognizing that different stakeholders may observe different aspects of the same situation.

3. Analyze Through Multiple Lenses

Raw data becomes actionable evidence through thoughtful analysis. The most effective organizations employ multiple analytical frameworks to examine the same information from different perspectives. This might include statistical analysis to identify patterns, scenario modeling to test potential outcomes, and comparison against historical benchmarks or industry standards.

Successful evidence-based organizations deliberately challenge their own assumptions during the analysis process. They assign devil's advocates to question conclusions, test alternative hypotheses, and identify potential blind spots in their reasoning. This intellectual rigor significantly improves decision quality by reducing confirmation bias.

4. Implement, Measure, and Refine

Evidence-based decision making isn't a one-time event but a continuous cycle. After implementing decisions, top-performing organizations systematically measure outcomes against expectations, analyze variances, and refine their approaches accordingly. This closed-loop system transforms even unsuccessful initiatives into valuable learning opportunities that improve future decision quality.

The implementation phase also provides critical feedback about the decision framework itself. By tracking which types of evidence most accurately predicted actual outcomes, organizations can continuously improve their data collection and analysis processes, creating a virtuous cycle of increasing decision quality over time.

Tools That Power Evidence-Based Organizations

The evolution of analytical technologies has dramatically expanded the capabilities of evidence-based organizations. Modern tools enable companies to process larger data volumes, uncover more complex patterns, and deliver insights to decision-makers with unprecedented speed and clarity.

Real-Time Analytics Dashboards Worth Your Investment

Real-time analytics dashboards have transformed from executive luxuries to operational necessities. Today's most effective systems integrate data from multiple sources into customizable interfaces that highlight key performance indicators relevant to specific roles and decisions. The best dashboards present information in context, showing not just current status but trends over time and performance against benchmarks.

Leaders in this space include Tableau's real-time visualization platform, which enables non-technical users to explore data relationships through intuitive interfaces, and Domo's business intelligence ecosystem, which specializes in mobile-first dashboards that deliver insights to decision-makers wherever they work. These platforms dramatically reduce the time from data collection to action, enabling the operational agility that distinguishes market leaders.

1. Data Collection and Analysis

• Quantitative Tools: Excel, Power BI, Tableau for trend analysis and dashboards.
• Qualitative Tools: SurveyMonkey, Google Forms for stakeholder feedback.
• Benchmarking: Compare internal metrics with industry standards.

2. Process Mapping and Root Cause Analysis

• Flowcharts & SIPOC Diagrams: Visualize process interactions.
• Fishbone Diagrams & 5 Whys: Identify root causes of nonconformities.

3. Design Review and Validation

• Gate Reviews: Structured checkpoints for project decisions.
• Verification Logs: Ensure deliverables meet requirements.
• Cross-functional Panels: Include diverse perspectives in evaluations.

4. ISO-Aligned Monitoring

• Dashboards: Track KPIs and targets for QMS processes.
• Internal Audits: Assess compliance and effectiveness.
• Customer Metrics: Monitor satisfaction and feedback trends.

5. Digital and Automation Tools

• AI Dashboards: Predictive analytics for proactive decisions.
• QMS Platforms: Centralize data and automate reporting.
• CAPA Systems: Manage corrective and preventive actions.

Data Visualization Systems That Executives Actually Use

The most sophisticated data visualization systems transform complex information into intuitive visual formats that reveal patterns and relationships invisible in raw numbers. These tools bridge the gap between data scientists and business decision-makers by presenting analytical insights in formats that leverage the brain's natural pattern recognition capabilities. For leaders looking to maximize the impact of these systems, exploring sustainable value creation strategies can be beneficial.

Leading organizations have moved beyond static charts to interactive visualization environments that allow decision-makers to explore different dimensions of their data dynamically. Systems like Microsoft's Power BI and Google's Data Studio provide drag-and-drop functionality that democratizes data exploration, allowing non-technical users to test hypotheses and discover insights independently.

AI-Powered Decision Support Tools

Artificial intelligence and machine learning represent the newest frontier in evidence-based decision making. These technologies can analyze vastly larger datasets than human analysts, identify subtle patterns invisible to traditional analysis, and continuously improve their accuracy through feedback loops. AI-powered tools are particularly valuable for complex decisions involving multiple variables and non-linear relationships.

IBM's Watson Decision Platform exemplifies this approach, combining natural language processing with machine learning to help organizations make evidence-based decisions in domains from healthcare to financial services. Similarly, DataRobot's automated machine learning platform enables business analysts to build predictive models without specialized data science expertise, significantly expanding organizational analytical capabilities.

How to Build Your Evidence-Based Culture

The most sophisticated analytics technologies will fail without a supportive organizational culture. Building a truly evidence-based organization requires deliberate attention to people, processes, and cultural norms that either enable or obstruct data-driven decision making.

Transform Your Leadership Team First

Evidence-based transformation must start at the top. When leaders consistently demand data to support recommendations, visibly use evidence in their own decisions, and reward analytical thinking, these behaviors cascade throughout the organization. Conversely, when executives make intuition-based decisions while preaching data-driven approaches, employees quickly recognize the disconnect and revert to telling leaders what they want to hear rather than what the evidence suggests.

Successful organizations often begin by applying evidence-based approaches to a few high-visibility executive decisions, demonstrating tangible benefits that build credibility and momentum. This approach creates powerful narratives that accelerate cultural change more effectively than abstract directives about becoming “data-driven.”

Create Psychological Safety for Data Transparency

Evidence-based decision making requires honest reporting of results, including failures and underperformance. Organizations where messengers get shot quickly develop cultures where data is manipulated to tell favorable stories rather than reveal actionable truths. Creating psychological safety—where employees can share concerning data without fear of blame—is essential for evidence-based approaches to take root.

Leading organizations establish clear distinctions between performance problems that require intervention and expected variation in complex systems. They recognize that excessive punishment for negative outcomes creates incentives to hide evidence, ultimately undermining the organization's ability to learn and improve. Instead, they focus accountability on the quality of the decision process rather than perfect outcomes in every instance. For more insights, explore how data-driven decision making can enhance business performance.

Reward Decisions Based on Evidence, Not Hierarchy

Traditional organizations reward decisions based on positional authority—the highest-ranking person's opinion carries the day regardless of supporting evidence. Evidence-based organizations fundamentally reorient this dynamic, creating systems where the strength of supporting data outweighs organizational hierarchy in decision influence. For leaders looking to foster such an environment, sustainable value creation strategies can be instrumental in guiding this transformation.

Companies like Google and Amazon have institutionalized this approach through decision documentation that requires explicit articulation of supporting evidence, regardless of the proposer's seniority. This practice not only improves decision quality but also empowers employees at all levels to contribute ideas supported by compelling data, significantly expanding the organization's innovation capacity.

Train Teams to Ask Better Questions

Evidence-based decision making begins with asking the right questions. Organizations that excel in this approach invest heavily in developing their teams' ability to frame problems effectively, identify relevant metrics, and distinguish between correlation and causation in data relationships. For more insights, explore continuous improvement strategies that emphasize learning and development.

The most effective training approaches combine technical skills like statistical analysis with critical thinking capabilities that help employees recognize cognitive biases and logical fallacies. This dual focus creates teams that not only know how to analyze data but also understand when existing evidence is insufficient and additional information is needed. For further insights, consider exploring the executive blueprint for continuous improvement.

Many leading organizations have developed customized decision frameworks that guide employees through structured question sequences for common decision types. These frameworks ensure consistent application of evidence-based principles while gradually building analytical muscles throughout the organization.

Measuring the ROI of Your Evidence-Based Approach

Ironically, many organizations fail to apply evidence-based principles to evaluate their evidence-based initiatives. Measuring the return on investment from improved decision processes requires thoughtful consideration of both direct outcomes and second-order effects that may not be immediately visible.

Performance Metrics That Show Real Impact

The most meaningful metrics for evaluating evidence-based initiatives combine process indicators (how decisions are made) with outcome measures (results achieved). Leading organizations track metrics like decision cycle time, forecast accuracy, initiative success rates, and the percentage of decisions supported by defined evidence thresholds. These indicators provide insight into both the adoption of evidence-based approaches and their tangible business impact.

The Innovation Acceleration Effect: 20% Faster to Market

Companies that fully embrace evidence-based decision making typically see dramatic improvements in innovation velocity. By replacing opinion-based debates with data-driven experimentation, these organizations reduce development cycles by an average of 20% while simultaneously improving success rates for new initiatives. This acceleration effect compounds over time, allowing evidence-based companies to test more concepts and bring successful innovations to market substantially faster than competitors.

Risk Management Improvement: 17% Better Outcomes

Evidence-based approaches significantly enhance risk management capabilities by improving threat identification, impact assessment, and mitigation planning. Organizations implementing comprehensive evidence-based risk frameworks report 17% fewer negative surprises and respond 23% faster when issues do emerge. These improvements translate directly to bottom-line results through reduced crisis management costs and business disruptions.

Make Your Next Decision Your Best Decision

The transition to evidence-based decision making represents one of the highest-return investments available to modern organizations. Companies that systematically apply these principles achieve not just incremental improvements but transformative performance advantages that compound over time. As data volumes continue to grow and analytical capabilities become more sophisticated, the gap between evidence-based organizations and their intuition-driven competitors will only widen. The question isn't whether your organization can afford to become evidence-based—it's whether you can afford not to. Connect with DataForce Analytics to learn how our evidence-based decision frameworks can transform your organization's performance.

Frequently Asked Questions

Evidence-based decision making raises many questions for organizations beginning their transformation journey. The following responses address the most common concerns and misconceptions about implementing these approaches.

These insights are drawn from interviews with over 200 executives who have successfully led evidence-based transformations across diverse industries and company sizes.

How long does it take to implement an evidence-based decision-making culture?

Meaningful cultural transformation typically requires 12-18 months, though companies often see substantial benefits from initial pilots within 90 days. Most successful implementations follow a phased approach, beginning with high-visibility decisions where evidence-based approaches can demonstrate clear value before expanding to broader organizational processes. The key acceleration factor is visible leadership commitment—when executives consistently model evidence-based principles in their own decisions, organizational adoption occurs significantly faster.

What's the biggest obstacle companies face when shifting to evidence-based decisions?

The primary challenge isn't technical but cultural—specifically, overcoming entrenched decision rights and status hierarchies that value seniority over evidence. Organizations where decisions have traditionally been made based on intuition or authority often experience resistance from leaders who perceive evidence-based approaches as threats to their decision-making autonomy. Addressing this challenge requires thoughtful change management that recognizes the psychological dimensions of decision-making and provides leaders with ways to contribute their experience within evidence-based frameworks rather than being replaced by them.

Can small businesses afford the tools needed for evidence-based decision making?

Absolutely. While enterprise-scale analytics platforms require significant investment, numerous affordable tools now provide sophisticated capabilities accessible to organizations of all sizes. Cloud-based services like Google Data Studio, Microsoft Power BI, and Zoho Analytics offer powerful visualization and analysis capabilities with pricing models that scale with organization size. Many small businesses actually enjoy advantages in evidence-based transformation because their simpler data environments and decision processes allow faster implementation than in large enterprises with complex legacy systems.

How do you balance data with experience and intuition?

The most effective evidence-based organizations don't eliminate intuition but rather incorporate it systematically into their decision processes. They recognize that experienced leaders often detect patterns subconsciously before these patterns become visible in formal data. The key is distinguishing between intuition based on pattern recognition (which has value) versus biases or emotional reactions masquerading as intuition.

Leading organizations use structured frameworks that explicitly capture experiential insights alongside quantitative data, allowing both to inform decisions within a consistent evaluation process. This integrated approach leverages the complementary strengths of human judgment and data analytics rather than treating them as competing alternatives.

What's the first step to take if my company is completely intuition-driven today?

Begin with an evidence inventory to understand what data you already have but aren't utilizing effectively. Most organizations are surprised to discover they possess substantial evidence that never reaches decision-makers or isn't presented in actionable formats. This discovery process not only identifies immediate opportunities for more evidence-based decisions but also builds organizational awareness of the gap between available information and current decision practices.

After completing this inventory, select a single high-visibility decision area for your initial evidence-based pilot. Choose a domain where better decisions would create tangible value, existing data could provide meaningful insights, and key stakeholders are receptive to new approaches. Early success in this focused area will build credibility and momentum for broader transformation. For more strategies, explore sustainable value creation for leaders.

Remember that evidence-based transformation is a journey rather than a destination. Organizations that approach this transition as continuous evolution rather than abrupt revolution consistently achieve better results and more sustainable cultural change.

In today's competitive business environment, companies are increasingly relying on evidence-based decision-making to enhance their performance. By utilizing data and analytics, organizations can make informed decisions that lead to improved outcomes. This approach not only helps in identifying opportunities for growth but also in mitigating risks. For leaders looking to inspire their teams, there are 7 ways leaders can inspire everyone to embrace the management system, fostering a culture of continuous improvement and innovation.