5 Proven Steps To Merge Human Experience With Data Analytics: A Decision-Making Framework

TL;DR:

• It’s important to establish partnerships and relationships between humans and data for more informed decision-making.
• Setting up guidelines for a decision-making process that combines domain expertise with data analytics is key.
  

According to a KPMG study cited by Chief Executive magazine, two-thirds of CEOs make decisions solely on instinct. This is a shocking metric in the day and age where the amount of data collected on almost everything is growing rapidly. Most CEOs won’t admit that their organizations are not structured to make comprehensive decisions leveraging both human experience and data analytics. However, almost all of them will admit that there needs to be a change.

Companies making decisions solely on past and present human judgment are lagging behind in a data-infused world. However, data-only decisions have resulted in poor and erratic behaviors, often costing companies millions of dollars. There is a happy medium where human experience paired with data leads to winning decision-making.

This blog post walks you through the steps to achieve that balance, from assessing your current decision-making processes to fostering a culture where humans and data work hand-in-hand. By the end, you’ll have a practical roadmap for smarter, more impactful decisions.

Step 1: Assess your current decision-making landscape

It’s essential to understand your starting point in your decision-making landscape. Assessing your current landscape lets you pinpoint where human expertise dominates, where data plays a role, and where inefficiencies may be holding back optimal performance.

Starting with an audit of how decisions are made across your organization helps identify the various decision-making frameworks in place—whether formal or informal—and take stock of who the key decision-makers are. Establish how information flows within your organization, noting which processes are structured versus ad hoc so that you can create a detailed map of your decision-making ecosystem.

Remember, not every decision holds the same weight. Zero in on the decision points that significantly impact your workflow—those that boost revenue, refine operations, or steer strategic direction. These pivotal moments deserve closer examination to uncover any gaps in information or areas prone to bias. By identifying these opportunities, you can fortify them with dependable data.

Another good example of this in practice is to map out the challenges slowing down decision-making. Common hurdles include low data literacy, limited data access, prolonged decision timelines, and outdated tools. Documenting these obstacles creates a focused improvement plan. This process sets the stage for integrating analytics tools that amplify human expertise.

Step 2: Build a strong foundation  

A solid data foundation is essential for analytics to succeed. It ensures your organization has the right metrics, collection methods, tools, and quality controls to support informed decisions.  

Start by identifying the key data metrics that align with your objectives. Collaborate with business and data experts to pinpoint the critical information needed to drive impactful decisions. Focusing on relevant metrics will shape your data strategy and prioritize resource allocation, whether it’s customer behavior, supply chain efficiency, or market trends. 

Next, ensure consistent and accurate data collection. Timely data gathering builds trust in analytics. Set clear governance standards and streamline integration across platforms to minimize discrepancies and improve quality. A seamless, reliable data collection process strengthens your dataset and its value for analysis.  

Choosing the right analytics tools is equally crucial. Select platforms that match your team’s technical skills, meet user needs, and scale with your business. Tools offering real-time analysis, predictive modeling, and intuitive data visualization will drive a forward-thinking, data-powered strategy.  

Standardized reporting templates are key for clear communication. Consistent, intuitive reports aligned with your metrics help stakeholders interpret and act on findings effectively. Visualizations should translate complex data into actionable insights, fostering data literacy and encouraging analytics integration into strategic processes.  

By laying the proper data foundation, you empower informed, data-driven decisions while enhancing stakeholder confidence and organizational effectiveness. 

Step 3: Create a structured integration process

After building a strong data foundation, the next step is creating a structured integration process to embed data-driven insights into decision-making while leveraging human judgment.  

Clearly define roles for analytics tools and human experts to prevent inefficiencies. Tools excel at processing large datasets, running calculations, and visualizing trends, while human experts focus on analyzing patterns, applying strategic context, and making nuanced decisions technology cannot replicate. This balance maximizes the strengths of both.  

Establish decision thresholds to determine when to prioritize data versus intuition. Routine, low-risk decisions can often be automated or accelerated with analytics. Conversely, high-risk, bespoke scenarios require human expertise supported by relevant data for context. These thresholds streamline decision-making and promote consistency.  

Incorporate validation checkpoints to verify the integration process. Regularly reviewing data outputs against expert expectations ensures alignment, builds trust in analytics, and strengthens the overall decision framework.  

Finally, create escalation protocols for situations where data conflicts with human judgment. For instance, a meeting between data and business experts can resolve discrepancies caused by shifts in data or incomplete information. This fosters collaboration and trust, driving greater adoption of analytics.  

By defining these processes, you create a seamless integration of data and expertise, empowering more consistent and effective decisions.

Step 4: Implement the human-data partnership

Data-driven processes are only as good as their adoption rate. Fostering a collaborative culture between data and human expertise is critical to building trust and gaining acceptance of analytical decision-making. Organizations must prioritize training, change management, and strategic projects that demonstrate the value of this partnership.

Growing data literacy across business stakeholders ensures that all individuals, ranging from executives to frontline workers, have a foundational understanding of data analytics is essential. 

Since most organizations have low levels of data literacy, it’s important to implement robust training programs to grow this capability. These programs should cover how to interpret data visualizations and insights, recognize limitations of data and technology, and understand the role of analytics tools in supporting their work. The aim is to demystify data and make it accessible so business stakeholders feel empowered rather than intimidated by it. 

Implementing human-data partnerships usually requires a shift in mindset from “human vs. data” to the “organization vs. the problem.” Implement frequent and transparent communication and training on relevant tools and topics to enable change management within your organization. Both business and data leaders should also champion this initiative, acting as role models who value and utilize data in their decision-making.

Establish channels that facilitate continuous dialogue between data and business experts to create strong communication protocols. Regular meetings, shared communication channels (like a Slack or Teams channel), and shared documentation help ensure harmony between data interpretations and real-world context. This prevents misunderstandings, ensures that insights generated are technically and logically sound, and builds trust between the teams.

Data requires domain expertise to be useful, and domain expertise is only as good as it can be supported by data. 

Step 5: Measure and refine the integration

Establishing a clear and measurable strategy to evaluate the adoption of the new decision-making framework allows you to determine if it’s working and where improvements are needed to increase adoption.

Defining key performance indicators (KPIs) that align with business objectives can capture both quantitative and qualitative outcomes. This can evaluate the overall health and success of a new and improved framework. Examples of KPIs include:

• Time-to-decision metrics (ex., time to hire a new employee).
• Decision accuracy (ex., how well data can predict how many products to ship to a customer).
• Alignment to strategic goals (ex., picking the right promotional strategies to align with overall brand management).
  

Setting clear KPIs establishes a benchmark that helps gauge progress and identify areas requiring adjustments. 

A combination of KPIs and ROI results can help determine if your organization truly sees better decisions being made. 

Bringing it all together

It’s not humans against data; it’s the organization against the problem. This human and data-driven framework provides a strategic approach to strengthen your organization’s ability to make decisions. It equips individuals throughout the organization with tools to foster open communication, encourage calculated risk-taking, and build trust. 

Going off of instinct or data alone isn’t going to differentiate your organization. But merging the two is a proven way to disrupt your old ways of working and lead to better outcomes.

Frequently asked questions

How can I most effectively utilize my team’s expertise when analyzing data?

The business domain is critical to understanding what patterns are within the data. Enable your team to play around with data or pair them with a data expert to explore together.

How can I build human insights into my data reporting?

Automate repetitive tasks, like report building, and ask your domain experts to provide commentary on the output of those reports.

What are some ways human insights fail where data can help fill in the gaps?

Humans generally don’t have the full historical context of a situation, which is where data can come in handy. Humans also have difficulty identifying nuanced trends, which is easy for data to do.