By 2025, an amazing 463 exabytes of data will be created worldwide daily and deriving significant, actionable, business insights from all of that data is challenging but crucial.

Insights are essential for swift and confident decisions, innovation, and profitable growth. McKinsey’s research reveals that insight-driven B2B companies report above-market growth and EBITDA increases of 15 to 25 percent .

How can you achieve similar results?

We wrote this article to answer this very question. The content was guided by the most frequent data and insights-related questions we receive in our Advisory Services practice. In this blog we explore the concept of insights, focusing on the:

• 4 ways to recognize, and 2 ways to validate data-derived insights.
• 3 sources of insights: data, experience, and intuition-derived, shedding light on the significance of each approach and which is usually best.
• 8 crucial steps required to effectively implement the insight to achieve maximum top and bottom-line business benefits.

Recognizing and Validating Data-Derived Insights to Tap into Their Power

Data-derived insights are revelations that arise from the analysis of quantitative and qualitative data. These insights can be discovered through various techniques, such as data mining, statistical analysis, and machine learning. When examining data, question assumptions and beliefs, and watch for the following indicators of insights:

Patterns: Recurring patterns or anomalies in the data can lead to insights. For example, a construction equipment supplier may discover that certain types of heavy machinery are in higher demand during specific construction seasons, leading to more informed inventory management.For instance, they may observe that earth-moving equipment and excavators are in higher demand in the spring and summer when construction projects peak, while indoor equipment like forklifts and generators see increased demand during the winter when more work occurs in enclosed spaces. By analyzing these seasonal patterns, the construction equipment supplier can leverage the power of data-derived customer insights by adjusting their inventory levels and distribution schedules to meet the needs of construction companies, ultimately improving their inventory management, and ensuring they have the right equipment available for their B2B customers when they need it.

Correlations and Relationships: Insights often involve recognizing correlations or relationships between variables that were not apparent initially. These relationships can provide actionable information. Consider the pivotal connection between marketing spend and new sales. An analysis of the data finds a correlation between marketing spend during specific campaigns or periods coincides with a noticeable uptick in new sales. For instance, the data might illuminate that a surge in social media advertising precedes a notable spike in new customer acquisitions. With this insight, your marketing team can tailor strategies, amplifying efforts on platforms that have demonstrated a direct impact on expanding your customer base.

Anomalies and Outliers: Unusual data points, also known as outliers, can lead to insights. Detecting outliers might reveal hidden issues or opportunities. For instance, an unusual increase in website traffic might indicate a potential problem or an unexpected market demand.

Predictive Power: Insights can also be recognized by the predictive power of certain variables. Predictive analytics can unveil insights that help anticipate trends and make proactive decisions. For instance, a B2B supplier of industrial machinery may use predictive analytics to enhance the customer experience. By analyzing historical data and market trends, they may identify that maintenance schedules for their machinery can greatly impact customer satisfaction.Through predictive analytics, they can predict when certain parts or components are likely to fail, allowing them to proactively schedule maintenance visits for their customers, reducing unexpected downtime and costly repairs.

This approach not only improves the overall customer experience, it also fosters stronger relationships with B2B clients, who appreciate the proactive service. Additionally, by addressing maintenance needs before they become critical, the supplier can reduce service costs and enhance the longevity and performance of their machinery, ultimately increasing customer satisfaction and loyalty. Before you declare that an insight is worth applying, it must be validated using these two methods:

Testing and Validation. Before acting on an insight, it’s crucial to validate it through rigorous testing and analysis. This ensures that the insight is reliable and actionable.

Cross-Validation. Incorporate multiple perspectives and sources of information. Cross-validation can help confirm the legitimacy of an insight and reduce the risk of making a decision based on a single viewpoint.

Data-Derived Insights vs. Experience vs. Intuition. Which is Best?

If data-derived insights are so good, why do so many organizations still rely on experience and intuition? Primarily, because things like data quality, data storage, data management, and data integration remain difficult. As a result, some organizations rely on experience and intuition. Both experience and intuition-based insight sources have merits, but they also have limitations.

Insights from experience generally come with better contextual understanding. People with experience can usually make quick, informed decisions honed by trial and error and based on the knowledge they’ve accumulated over the years. However, experience is subjective and can be influenced by personal biases or outdated practices.

As a very high N on the Myers-Briggs Type Indicator, I’m all for intuition. Intuitive insights are those that come from gut feelings, hunches, or instincts. When there is limited information and time is of the essence, intuition is a good thing. And intuition can factor in emotional considerations. But I’d be the first to say that intuition is both subjective and unreliable and can lead to inconsistent decision-making, increased risk, and missed opportunities.

The limitations of experience and intuition are why I prefer data-derived insights. Data-derived insights are inherently objective, as they are based on factual information rather than personal biases. Data analysis can handle vast amounts of information, allowing organizations to uncover insights on a large scale.

Data-derived insights often provide precise and quantifiable information, making them suitable for optimizing processes and measuring the impact of changes. Data insights can take advantage of predictive power, empowering organizations to stay ahead of the curve and adapt faster to changing circumstances.

Now That You Have an Insight, What’s Next? 8 Answers

Having identified a valuable insight, the next crucial step is to leverage it effectively. The significance of an insight lies not only in its discovery but also in its application. Here’s what to do once you’ve unearthed an insight:

Define Clear Objectives: Begin by clearly defining your objectives. Determine what specific actions or decisions the insight should inform. This might involve setting performance targets, outlining a strategy, or identifying metrics to measure the impact of your actions.

Develop an Action Plan: Create a comprehensive action plan that outlines the steps needed to act on the insight. Assign responsibilities, set timelines, and allocate resources accordingly. Consider any potential risks or obstacles that may arise during implementation.

Measure and Monitor Progress: Regularly measure and monitor the progress of your actions based on the insight. Use relevant metrics and data to assess whether your decisions are leading to the desired outcomes. This iterative process allows for course correction and optimization as necessary.

Communicate and Collaborate: Effective communication is key to ensuring that the insight is shared throughout your organization. Collaborate with relevant teams or stakeholders to gain their buy-in and support. A shared understanding of the insight can lead to more successful implementation.

Iterate and Learn: Insights are not static; they evolve as circumstances change. Continuously iterate on your strategies and decisions based on new data and experiences. Learning from both successes and failures is essential for ongoing improvement.

Document and Share Best Practices: As you act on insights and achieve positive results, document the best practices and lessons learned. Sharing this knowledge within your organization can foster a culture of learning and data-driven decision-making.

Foster a Culture of Continuous Improvement: Promote a culture of continuous improvement and adaptation. Encourage your team to seek out and share insights, whether they are data-derived, based on experience, or intuitive. This open-minded approach can lead to ongoing innovation and improvement.

Prepare for Future Insights: Recognize that insights are not isolated occurrences but a continuous process. Be prepared to adapt to changing circumstances and be open to new insights that may alter your strategies. Building a robust insight-driven framework ensures you can respond effectively to emerging challenges and opportunities.

The Bottom Line on Recognizing and Fully Leveraging Insights

In a data-driven world, recognizing and harnessing insights is vital for informed decision-making. Data-derived insights, along with those gained from experience and intuition, each have their place in decision-making. Data-derived insights offer objectivity, scalability, precision, and predictive capabilities. The power of insights lies both in the discovery and in the application.

Looking for guidance to harness the full potential of insights to drive informed decision-making and achieve your organizational goals? Check out our affordable, outcome-driven advisory services. Here are just some of the data and insights-related questions we have answered for our customers to help them grow:

• What are the best practices for data governance and data quality management to ensure that our data is consistently reliable, up-to-date, and accessible?
• What are the best practices for data collection and management, data analysis, and data visualization, to derive meaningful insights for decision-making?
• How can we leverage advanced analytics techniques (predictive modeling, machine learning, or artificial intelligence), to uncover hidden patterns, trends, and correlations in our data?
• Can you guide us on how to build effective data models that capture the relevant variables and relationships within our business context?
• How can we ensure the accuracy, integrity, and reliability of our data for analysis and reporting purposes?
  What are the key steps and considerations for developing a robust analytics strategy?
  How can we assemble a world-class data and analytics team that can take our analytics maturity level to predictive?

Laura Patterson is president and co-founder of VisionEdge Marketing, Inc., a recognized leader in enabling organizations to leverage data and analytics to facilitate marketing accountability.

Laura’s newest book, Marketing Metrics in Action: Creating a Performance-Driven Marketing Organization (Racom: www.racombooks.com ), is a useful primer for improving marketing measurement and performance. Visit: www.visionedgemarketing.com

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