Instinct to Data-Driven: 5 Steps to Change Senior Attitudes about Data

A recent Forrester Consulting study revealed that businesses using data management tools to make decisions are 58% more likely to exceed their revenue targets than those that don’t. 

Data-driven organisations are also 162% more likely to significantly surpass these goals compared to their non-data-driven peers.

If your senior leadership team is overly reliant on intuition in decision making, don't worry! Below, we outline five compelling ways to convince them that data-driven decision making is crucial for success.

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1. Educating on the value of data

The most logical place to start with changing senior attitudes towards data is by educating them about its value. It is possible that your seniors are simply not aware of the benefits that data analysis can provide for their organisation.

Outline the differences between data-driven and instinct-driven decision making, as well as the advantages that data-driven approaches have over instinct-based methods.

Data-driven decision making is exactly as it sounds. It is making decisions based on evidence in the form of historical data. Data-driven decision making means having concrete evidence as to why business decisions are being made.

Instinct-driven decision making is the complete opposite, and you could describe it as taking a “shot in the dark”. Decisions are made solely based on gut feelings and past experience. This is a far less reliable and effective method than data-driven decision making.

So, what are the benefits of data-driven decision making? Demonstrating the benefits to your superiors can help them appreciate and adopt a more data-driven approach.

A man standing in front of a group of people talking

  • Enhanced accuracy and reliability: Access to valuable insights allows organisations to base decisions on verified data, thereby improving the accuracy and reliability of their decision making processes.

    According to a report by PwC, executives from companies using data in decision-making are nearly three times more likely to see significant improvements in strategic decisions vs. those that don’t. 
  • Improved adaptability to change: Data-driven decision making empowers organisations to adapt more effectively to an ever changing commercial landscape. Data equips them with the insights needed to respond to shifts in market trends and consumer behaviour.
  • Increased confidence in decision making: Using data to inform decisions allows organisations to make more confident choices when facing business challenges, such as product launches, marketing adjustments, or strategic expansions.
  • Quicker decision making process: McKinsey research indicates that executives typically spend nearly 40 percent of their time making decisions, with a significant portion of that time considered inefficient. 

    Data-driven organisations can make decisions more quickly. This is because they are supported by solid evidence that gives clear guidance and reduces uncertainty, meaning time is not wasted in long deliberations. Making quicker decisions means being one step ahead of the competition too!

Dr. Adeala Zabair, Head of Data Science & Analytics at Exertis said:

Headshot photo of Adeala Zabair on blue background

“There are a number of areas in the business that we think could be improved by looking at data-informed decisions: either the data allows us to standardise processes in the business or data brings to the fore a lot of insight, which we can use to make better decisions.”


2. Aligning data goals with business objectives

Once your senior team understands how data-driven decision making can benefit your organisation, delve into the specifics. 

RECOMMENDED READING: 5 Ways Data Analytics Can Boost Your Operational Efficiency

Start by identifying key business goals critical for success, such as increasing market share, improving customer satisfaction, or boosting operational efficiency.

Next, explain how specific data initiatives can directly support these goals. For instance, one data goal might involve using analytics to uncover customer behaviour patterns, leading to more targeted marketing strategies aimed at increasing sales.

You may have multiple organisational goals, each achievable through different data analysis strategies.

RECOMMENDED READING: Data-Driven Culture: 5 Examples from World-Leading Organisations

After defining the alignment between your organisation and data goals, you can then demonstrate how to implement these strategies through a pilot project. We will discuss that in more detail in the next section.

But first let's look into the case study of Joel Hollingsworth, an electrophysiology co-ordinator at the NHS. 

During his data analyst apprenticeship, Joel applied his data skills to significantly reduce referral delay times by up to 75%. He achieved this by analysing time series data to pinpoint the busiest days for consultants. 

This insight enabled proactive anticipation of longer response times, streamlining the referral process. Patients are now receiving their procedures up to 18 days earlier than prior to the study, solving an ongoing issue for the organisation.

A woman sat on a sofa smiling whilst opening a letter

3. Demonstrating quick wins through pilot projects

So you've already identified your business objectives and challenges and aligned them with your data goals. 

Next, you’ll want to develop a strategy that demonstrates how you can achieve one of your organisational goals from start to finish using analysis or other data techniques. This will serve as your pilot project.

For example, suppose your organisation is a supermarket with the goal to optimise inventory levels to reduce waste created by expired food. In this case, you could design a pilot project that showcases how data analytics can be used to fine-tune your inventory levels, thereby eliminating waste. 

This project would not only address the issue but also highlight the practical benefits of data-driven decision making.

Take a look at this example of a pilot project outline below for this scenario:

A data pilot project outline

Firstly, clearly define your objective so it is easier to evaluate the success of the project after its completion.


Objective: Show how data analytics can be used to optimise inventory levels, reducing excess stock and shortages, thereby minimising costs and maximising sales during peak demand periods.


  1. Data collection: Gather historical sales data, current inventory levels, and supply chain logistics information. After collecting your data you want to ensure it is clean and prepared for analysis.

  2. Predictive analysis: Use predictive analytics to forecast demand for high-turnover products. This could involve time series forecasting models that take into account seasonal variations, trends, and past promotional impacts.

  3. Inventory adjustments: Based on the forecasts, adjust inventory orders for the next quarter to better match predicted demand. You could focus on a select group of high-impact products to keep the scope manageable.

  4. Implementation: Implement the revised inventory orders and monitor the replenishment cycles and sales performance during the pilot period.

  5. Analysis and reporting: Compare inventory costs, storage costs, sales performance, and out-of-stock incidents before and after implementing the data-driven forecasts.

  6. Presentation of results: Share the findings of the project with a presentation that details changes in inventory efficiency, reduction in stock-outs, and improvements in customer satisfaction due to having the right products available when needed.

Here is a breakdown of steps for a general data pilot project:

An infographic showing the steps of creating a data pilot project

4. Exhibiting competitor data strategies

Are your senior leaders on board yet? No? Well it may be difficult for them to ignore how competitors are using data to gain a competitive edge. 

As competitors adopt data-driven strategies, embracing data analytics becomes crucial for your organisation to maintain its competitive edge and swiftly adapt to changes. 

Here are several methods to access this type of information:

  1. Investigate official channels: Start by exploring your competitors' official channels. Companies often share insights about their operational improvements and success stories through blog posts, press releases, and news updates.

  2. Examine LinkedIn profiles and job postings: Employee profiles can reveal details about projects or initiatives, especially those related to data analytics. Job postings can provide insight into how your competitors are advancing in developing data roles within their organisation.

  3. Review annual reports: Publicly traded companies are required to disclose strategic initiatives, including those involving data analytics, in their annual reports.

  4. Consult with analysts and industry consultants: These professionals have a broad view of the industry and can offer comparative insights into how different companies measure up in terms of data analytics maturity.

If your competitors are more data-driven, your organisation can lose market share for several reasons.

  • Missed opportunities: Data-driven organisations are better equipped to identify and capitalise on emerging trends and opportunities. Without leveraging data, a company may miss out on crucial growth opportunities. 

    For example, a data-driven organisation may be able to identify more niche markets that could be highly profitable. Less data-driven organisations may miss out on these opportunities due to lack of insights.
  • Weaker strategic positioning: Competitors using data can fine-tune their strategies based on real-time feedback and analysis, leading to stronger market positioning. Non-data-driven firms lack this agility, making it hard to compete effectively.
  • Poor customer retention: Data-driven competitors actively use customer data to predict churn and create retention strategies tailored to individual preferences and needs. 

    In contrast, companies that fail to leverage data often experience higher churn rates because they do not proactively address customer dissatisfaction or respond effectively to competitive offers, resulting in a gradual loss of their market share.

A man holding a pen and notepad that says "competitor analysis"

5. Creating transparency in data processes and results

One of the great advantages of data is its shareability within your organisation, which data visualisation helps facilitate. This expands access to data insights from a select few technical stakeholders so others  across the organisation can understand and benefit from them.

Data visualisation involves the graphical representation of information and data. By applying visual elements such as charts, graphs, and maps, data visualisation tools make it easier to see and understand trends, outliers, and patterns in data. 

This accessibility ensures that insights derived from data are easily understood and actionable for all stakeholders.

Some of the most notable benefits of data visualisation are:

  • Increased engagement: Engaging visuals capture attention more effectively than raw data, increasing stakeholder engagement and making presentations more impactful.
  • Improved communication: Data visualisation provides a universal language that improves communication about data insights among diverse teams and departments.
  • Organisational alignment: By making data accessible and understandable, data visualisation helps align different parts of the organisation to create a more data-driven culture.

A compelling example of how data visualisation can enhance communication of critical data findings comes from Colin Warhurst, a strategy manager at the BBC. 

Colin employed data visualisation techniques to present important insights, which led to a potential reduction of 49% in TV energy usage by the public across the UK.

Colin said:

"When we turned up to the meeting with the infographic, buy-in was so quick… We sat down with the head of audio marketing, and he was sold on it after 10 minutes. And then I sent him some screenshots, and he got buy-in from all the heads of radio straight away." - Colin Warhurst, Strategy Manager at the BBC


Transitioning from instinct or intuitive decision making to a data-driven approach is essential for modern organisations aiming to thrive in a competitive landscape. 

From enhancing accuracy and reliability in decisions to aligning data goals with core business objectives and demonstrating the efficacy through pilot projects, these strategies are key. 

By adopting a data-driven approach, organisations ensure that they remain agile, forward-thinking, and competitive, ready to harness the full potential of their data to drive growth and innovation.

Are you looking to harness the power of a more data-driven approach within your organisation? Explore our blog, “How to Balance Your Organisation’s Budget and Data Skills Shortage,” and discover cost-effective strategies to upskill your workforce.

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