Unleashing Business Impact with Data | The Data Led Decisions Rubric
Jason Russo
CEO at Maven Analytics
Jun 10, 2024
Introduction to data-driven decision making
In today’s business landscape, data and information are among the most valuable resources. Organizations collect enormous amounts of data but, without the ability to leverage it effectively, that data loses much of its value and meaning. The ability to understand and use data to make informed decisions is quickly becoming an absolutely essential skill.
Despite the growing abundance and availability of data, many companies have not recognized the importance of embracing data-driven decision making. While some leaders are beginning to see the benefits of making decisions based on evidence, many are left wondering, “what exactly is data-driven decision making?”
So… what is it? Data-driven decision making, or DDDM, is the process of using facts, metrics, data analysis, and hard evidence to make decisions and guide business strategies. Instead of relying on intuition or gut instinct, both of which can be prone to biases and inaccuracies, DDDM leverages data to inform decisions.
With a better understanding of what data-driven decision making is, the next question may be: why is it so important? DDDM is rapidly evolving from a siloed concept, meant only for data professionals, to an essential practice, relevant to all people across an organization. This means that organizations that embrace data-driven decision making are in a position to reap enormous benefits.
With the what and why answered, let’s explore how DDDM can empower employees at all levels to make informed choices, optimize processes and enhance efficiency, and propel the organization towards its goals.
In today’s business landscape, data and information are among the most valuable resources. Organizations collect enormous amounts of data but, without the ability to leverage it effectively, that data loses much of its value and meaning. The ability to understand and use data to make informed decisions is quickly becoming an absolutely essential skill.
Despite the growing abundance and availability of data, many companies have not recognized the importance of embracing data-driven decision making. While some leaders are beginning to see the benefits of making decisions based on evidence, many are left wondering, “what exactly is data-driven decision making?”
So… what is it? Data-driven decision making, or DDDM, is the process of using facts, metrics, data analysis, and hard evidence to make decisions and guide business strategies. Instead of relying on intuition or gut instinct, both of which can be prone to biases and inaccuracies, DDDM leverages data to inform decisions.
With a better understanding of what data-driven decision making is, the next question may be: why is it so important? DDDM is rapidly evolving from a siloed concept, meant only for data professionals, to an essential practice, relevant to all people across an organization. This means that organizations that embrace data-driven decision making are in a position to reap enormous benefits.
With the what and why answered, let’s explore how DDDM can empower employees at all levels to make informed choices, optimize processes and enhance efficiency, and propel the organization towards its goals.
In today’s business landscape, data and information are among the most valuable resources. Organizations collect enormous amounts of data but, without the ability to leverage it effectively, that data loses much of its value and meaning. The ability to understand and use data to make informed decisions is quickly becoming an absolutely essential skill.
Despite the growing abundance and availability of data, many companies have not recognized the importance of embracing data-driven decision making. While some leaders are beginning to see the benefits of making decisions based on evidence, many are left wondering, “what exactly is data-driven decision making?”
So… what is it? Data-driven decision making, or DDDM, is the process of using facts, metrics, data analysis, and hard evidence to make decisions and guide business strategies. Instead of relying on intuition or gut instinct, both of which can be prone to biases and inaccuracies, DDDM leverages data to inform decisions.
With a better understanding of what data-driven decision making is, the next question may be: why is it so important? DDDM is rapidly evolving from a siloed concept, meant only for data professionals, to an essential practice, relevant to all people across an organization. This means that organizations that embrace data-driven decision making are in a position to reap enormous benefits.
With the what and why answered, let’s explore how DDDM can empower employees at all levels to make informed choices, optimize processes and enhance efficiency, and propel the organization towards its goals.
Why do organizations need to use data-driven decisions?
There are so many benefits to becoming a data-driven organization. In fact, businesses that effectively use data can gain a huge strategic advantage and competitive edge. From increased efficiency to realizing cost savings and beyond, the shift toward data-driven decisions is no longer optional; it’s the key to staying competitive in a dynamic business landscape.
Data-driven decision making creates a culture of objectivity and reduced bias within organizations. By basing decisions on data, businesses can be more confident in their choices, since they’re grounded in hard evidence and fact. This approach not only instills greater confidence in decisions, but also fosters transparency and accountability, which leads to improved communication and trust among stakeholders.
Leveraging data can also lead to increased efficiency and effectiveness within an organization. Data-driven decision making helps identify areas for improvement and process optimization, potentially leading to significant cost savings. Even more, organizations can identify new opportunities, support informed product development, and improve innovation by effectively using data.
Finally, using DDDM can improve organization-wide communication and collaboration. Establishing a common language and terminology across teams and breaking down silos allows for more opportunities for cross-departmental collaboration, information sharing, and new ideas. In addition, with everyone working from a single source of truth, valuable insights can be shared more easily, ultimately creating a more informed and collaborative organization.
There are so many benefits to becoming a data-driven organization. In fact, businesses that effectively use data can gain a huge strategic advantage and competitive edge. From increased efficiency to realizing cost savings and beyond, the shift toward data-driven decisions is no longer optional; it’s the key to staying competitive in a dynamic business landscape.
Data-driven decision making creates a culture of objectivity and reduced bias within organizations. By basing decisions on data, businesses can be more confident in their choices, since they’re grounded in hard evidence and fact. This approach not only instills greater confidence in decisions, but also fosters transparency and accountability, which leads to improved communication and trust among stakeholders.
Leveraging data can also lead to increased efficiency and effectiveness within an organization. Data-driven decision making helps identify areas for improvement and process optimization, potentially leading to significant cost savings. Even more, organizations can identify new opportunities, support informed product development, and improve innovation by effectively using data.
Finally, using DDDM can improve organization-wide communication and collaboration. Establishing a common language and terminology across teams and breaking down silos allows for more opportunities for cross-departmental collaboration, information sharing, and new ideas. In addition, with everyone working from a single source of truth, valuable insights can be shared more easily, ultimately creating a more informed and collaborative organization.
There are so many benefits to becoming a data-driven organization. In fact, businesses that effectively use data can gain a huge strategic advantage and competitive edge. From increased efficiency to realizing cost savings and beyond, the shift toward data-driven decisions is no longer optional; it’s the key to staying competitive in a dynamic business landscape.
Data-driven decision making creates a culture of objectivity and reduced bias within organizations. By basing decisions on data, businesses can be more confident in their choices, since they’re grounded in hard evidence and fact. This approach not only instills greater confidence in decisions, but also fosters transparency and accountability, which leads to improved communication and trust among stakeholders.
Leveraging data can also lead to increased efficiency and effectiveness within an organization. Data-driven decision making helps identify areas for improvement and process optimization, potentially leading to significant cost savings. Even more, organizations can identify new opportunities, support informed product development, and improve innovation by effectively using data.
Finally, using DDDM can improve organization-wide communication and collaboration. Establishing a common language and terminology across teams and breaking down silos allows for more opportunities for cross-departmental collaboration, information sharing, and new ideas. In addition, with everyone working from a single source of truth, valuable insights can be shared more easily, ultimately creating a more informed and collaborative organization.
How to make data-led decisions
Now that we’ve discussed the why of data-driven decision making, let’s talk about the how. Here are six steps that will allow organizations to become more thoughtful and strategic in their approach to decision making:
Now that we’ve discussed the why of data-driven decision making, let’s talk about the how. Here are six steps that will allow organizations to become more thoughtful and strategic in their approach to decision making:
Now that we’ve discussed the why of data-driven decision making, let’s talk about the how. Here are six steps that will allow organizations to become more thoughtful and strategic in their approach to decision making:
Identify the problem
Define success
Collect and prepare the data
Analyze and profile the data
Communicate findings
Advocate for action
Identify the problem
Define success
Collect and prepare the data
Analyze and profile the data
Communicate findings
Advocate for action
Identify the problem
Define success
Collect and prepare the data
Analyze and profile the data
Communicate findings
Advocate for action
1
STEP 1
Identify the Problem
Identifying the problem and understanding the business case will allow you to align on requirements, project scope, and desired outcomes from day one.
Identifying the problem and understanding the business case will allow you to align on requirements, project scope, and desired outcomes from day one.
What specific problem are we trying to solve?
Who are the key stakeholders and how will this help them?
Does this align with broader business priorities and strategy?
What specific problem are we trying to solve?
Who are the key stakeholders and how will this help them?
Does this align with broader business priorities and strategy?
The first step in this analytical process is to identify the problem. This sets the foundation for the entire process. It means thinking about the business as a whole and asking some key questions, like:
The first step in this analytical process is to identify the problem. This sets the foundation for the entire process. It means thinking about the business as a whole and asking some key questions, like:
2
STEP 2
Define Success
Defining success early on means that everyone will be on the same page and use the same benchmarks.
Defining success early on means that everyone will be on the same page and use the same benchmarks.
What exactly does a successful outcome look like?
What specific metrics will help us quantify success?
What data will we need to capture and track key metrics?
What exactly does a successful outcome look like?
What specific metrics will help us quantify success?
What data will we need to capture and track key metrics?
The next step is all about defining what success looks like. This means thinking about a measurement plan and asking key questions, like:
The next step is all about defining what success looks like. This means thinking about a measurement plan and asking key questions, like:
3
STEP 3
Collect and prepare data
This step can be challenging and time consuming, as it typically involves QA and cleaning the data, but data-driven organizations need to ensure that the data they’re using is reliable, trustworthy, and of high quality.
This step can be challenging and time consuming, as it typically involves QA and cleaning the data, but data-driven organizations need to ensure that the data they’re using is reliable, trustworthy, and of high quality.
Where is the data stored and how can I access it?
Are there data quality issues that might skew my analysis?
Will I need to model or transform the data before analysis?
Where is the data stored and how can I access it?
Are there data quality issues that might skew my analysis?
Will I need to model or transform the data before analysis?
With a clear picture of what success metrics you’re trying to impact and the data you’ll need to quantify them, the next step is to start collecting and preparing the data. At this stage, it’s important to start thinking about things like:
With a clear picture of what success metrics you’re trying to impact and the data you’ll need to quantify them, the next step is to start collecting and preparing the data. At this stage, it’s important to start thinking about things like:
4
STEP 4
Analyze and profile the data
Critically analyzing the data and asking the right questions allows you to transform raw information into actionable insights that directly influence business decisions.
Critically analyzing the data and asking the right questions allows you to transform raw information into actionable insights that directly influence business decisions.
Which views of the data can help support my analysis?
What types of patterns and trends are emerging?
Are there actionable insights that could help drive successful outcomes?
Which views of the data can help support my analysis?
What types of patterns and trends are emerging?
Are there actionable insights that could help drive successful outcomes?
After the data has been cleaned, transformed, modeled, etc., the next step is to analyze and explore the data. This is also where the D.I.I.A framework comes into play, but more on that in the next section. This step is where you’ll start to uncover patterns and trends, and hopefully discover actionable insights that could directly impact business objectives. You can ask questions like:
After the data has been cleaned, transformed, modeled, etc., the next step is to analyze and explore the data. This is also where the D.I.I.A framework comes into play, but more on that in the next section. This step is where you’ll start to uncover patterns and trends, and hopefully discover actionable insights that could directly impact business objectives. You can ask questions like:
5
STEP 5
Communicate findings
Effectively translating your analysis into a clear and compelling narrative helps stakeholders understand the importance of your findings and drive impactful business decisions.
Effectively translating your analysis into a clear and compelling narrative helps stakeholders understand the importance of your findings and drive impactful business decisions.
How can I craft a narrative to clearly summarize my analysis?
What’s the most effective way to visually communicate my findings?
Are my insights compelling and supported by the data?
How can I craft a narrative to clearly summarize my analysis?
What’s the most effective way to visually communicate my findings?
Are my insights compelling and supported by the data?
After the analysis is complete, the next step is to communicate the findings. This step is all about explaining what you found, why it’s important, and how it can impact the business. At this stage, you can think about questions like:
After the analysis is complete, the next step is to communicate the findings. This step is all about explaining what you found, why it’s important, and how it can impact the business. At this stage, you can think about questions like:
6
STEP 6
Advocate for action
Without action, data-driven decisions provide little value to an organization.
Without action, data-driven decisions provide little value to an organization.
What data-driven recommendations am I proposing?
Do they directly impact the outcomes I care most about?
How could we potentially quantify the business impact?
What data-driven recommendations am I proposing?
Do they directly impact the outcomes I care most about?
How could we potentially quantify the business impact?
The last, and arguably most important, step of this framework is to advocate for action. This is when the true data-driven decision making happens. You’ve discussed the problem, analyzed the data, communicated the findings, and now it’s time to talk about what happens next, based on the data. Make sure to consider questions like:
The last, and arguably most important, step of this framework is to advocate for action. This is when the true data-driven decision making happens. You’ve discussed the problem, analyzed the data, communicated the findings, and now it’s time to talk about what happens next, based on the data. Make sure to consider questions like:
What is the D.I.I.A framework?
What is the D.I.I.A framework?
What is the D.I.I.A framework?
Let’s drill down into the latter half of the analytical thinking framework. When it comes to using data to drive decisions, it’s important to recognize that data alone is not enough. In order to truly leverage data effectively, we created the D.I.I.A framework, loosely based on the popular DIKW pyramid. Using this framework allows you to go from raw data to actionable insights and, the further you move down the framework, the more value you add.
Let’s drill down into the latter half of the analytical thinking framework. When it comes to using data to drive decisions, it’s important to recognize that data alone is not enough. In order to truly leverage data effectively, we created the D.I.I.A framework, loosely based on the popular DIKW pyramid. Using this framework allows you to go from raw data to actionable insights and, the further you move down the framework, the more value you add.
Let’s drill down into the latter half of the analytical thinking framework. When it comes to using data to drive decisions, it’s important to recognize that data alone is not enough. In order to truly leverage data effectively, we created the D.I.I.A framework, loosely based on the popular DIKW pyramid. Using this framework allows you to go from raw data to actionable insights and, the further you move down the framework, the more value you add.
DATA
DATA
DATA
We start with data. If you recall from the analytical thinking framework above, step 3 is to collect and prepare the data; however, data in its raw form has little meaning or value. For example, raw data might look like, “There were 173 transactions in January.” With data alone, we can’t go to stakeholders with any meaningful information. We can’t provide any answers into why there were 173 transactions, if that number is good or bad, or how it compares to historical data.
We start with data. If you recall from the analytical thinking framework above, step 3 is to collect and prepare the data; however, data in its raw form has little meaning or value. For example, raw data might look like, “There were 173 transactions in January.” With data alone, we can’t go to stakeholders with any meaningful information. We can’t provide any answers into why there were 173 transactions, if that number is good or bad, or how it compares to historical data.
We start with data. If you recall from the analytical thinking framework above, step 3 is to collect and prepare the data; however, data in its raw form has little meaning or value. For example, raw data might look like, “There were 173 transactions in January.” With data alone, we can’t go to stakeholders with any meaningful information. We can’t provide any answers into why there were 173 transactions, if that number is good or bad, or how it compares to historical data.
INFORMATION
INFORMATION
INFORMATION
In step 4 of the framework, we analyze and profile the data. Once the data has been processed, organized, and stored, it can then be analyzed to produce useful information, which adds context and clarity for end users. With information added in, “There were 173 transactions in January,” becomes, “There were 173 transactions in January, up 75% over December. Fitness equipment and athletic apparel saw the largest month-over-month gains.” We now have information about what happened, but not why.
In step 4 of the framework, we analyze and profile the data. Once the data has been processed, organized, and stored, it can then be analyzed to produce useful information, which adds context and clarity for end users. With information added in, “There were 173 transactions in January,” becomes, “There were 173 transactions in January, up 75% over December. Fitness equipment and athletic apparel saw the largest month-over-month gains.” We now have information about what happened, but not why.
INSIGHT
INSIGHT
INSIGHT
The next step in the D.I.I.A framework is adding insight, which can help inform or influence business decisions and recommendations. With the information gathered in the previous step, along with historical data and context, we can provide insight like, “Every January we see an uptick in sales and revenue, driven primarily by new customers looking to prioritize health and fitness in the new year.” Those insights can then be used to provide recommendations.
The next step in the D.I.I.A framework is adding insight, which can help inform or influence business decisions and recommendations. With the information gathered in the previous step, along with historical data and context, we can provide insight like, “Every January we see an uptick in sales and revenue, driven primarily by new customers looking to prioritize health and fitness in the new year.” Those insights can then be used to provide recommendations.
ACTION
ACTION
ACTION
Finally we arrive at action. Step 6 of the analytical thinking framework discusses advocating for action. Providing data, information, and insight is great but no matter how insightful the analysis, it only adds value if it impacts real-world decisions. This should be the overarching goal of every data analysis: to inspire action. In this example, advocating for action might look like: “Based on this data, we recommend increasing advertising budgets and testing campaigns to promote top-selling health and fitness products in January.”
Finally we arrive at action. Step 6 of the analytical thinking framework discusses advocating for action. Providing data, information, and insight is great but no matter how insightful the analysis, it only adds value if it impacts real-world decisions. This should be the overarching goal of every data analysis: to inspire action. In this example, advocating for action might look like: “Based on this data, we recommend increasing advertising budgets and testing campaigns to promote top-selling health and fitness products in January.”
Putting it all together, we could present to stakeholders: “There were 173 transactions in January, up 75% over December. Fitness equipment and athletic apparel saw the largest month-over-month gains. Every January we see an uptick in sales and revenue, driven primarily by new customers looking to prioritize health and fitness in the new year. Based on this data, we recommend increasing advertising budgets and testing campaigns to promote top-selling health and fitness products in January.”
Putting it all together, we could present to stakeholders: “There were 173 transactions in January, up 75% over December. Fitness equipment and athletic apparel saw the largest month-over-month gains. Every January we see an uptick in sales and revenue, driven primarily by new customers looking to prioritize health and fitness in the new year. Based on this data, we recommend increasing advertising budgets and testing campaigns to promote top-selling health and fitness products in January.”
Putting it all together, we could present to stakeholders: “There were 173 transactions in January, up 75% over December. Fitness equipment and athletic apparel saw the largest month-over-month gains. Every January we see an uptick in sales and revenue, driven primarily by new customers looking to prioritize health and fitness in the new year. Based on this data, we recommend increasing advertising budgets and testing campaigns to promote top-selling health and fitness products in January.”
By taking raw data and transforming it into strong, evidence-based actionable insights, we can inspire stakeholders to rely on data, rather than intuition or guesswork.
By taking raw data and transforming it into strong, evidence-based actionable insights, we can inspire stakeholders to rely on data, rather than intuition or guesswork.
By taking raw data and transforming it into strong, evidence-based actionable insights, we can inspire stakeholders to rely on data, rather than intuition or guesswork.
Key Takeaways
Key Takeaways
Key Takeaways
Organizations have huge amounts of data but, without the ability to leverage it effectively, that data loses both value and meaning
Data-driven decision making leverages data, rather than gut instinct or intuition, to inform decisions
Organizations that use data effectively can gain a huge strategic advantage and competitive edge
Data-driven decision making involves six key steps: identify the problem, define success, collect and prepare the data, analyze and profile the data, communicate findings, and advocate for action
Data in its raw form holds little value and needs context and insight to become actionable
Data, combined with insight and action, can inspire stakeholders to make decisions based on evidence, rather than guesswork or gut instinct
Organizations have huge amounts of data but, without the ability to leverage it effectively, that data loses both value and meaning
Data-driven decision making leverages data, rather than gut instinct or intuition, to inform decisions
Organizations that use data effectively can gain a huge strategic advantage and competitive edge
Data-driven decision making involves six key steps: identify the problem, define success, collect and prepare the data, analyze and profile the data, communicate findings, and advocate for action
Data in its raw form holds little value and needs context and insight to become actionable
Data, combined with insight and action, can inspire stakeholders to make decisions based on evidence, rather than guesswork or gut instinct
Organizations have huge amounts of data but, without the ability to leverage it effectively, that data loses both value and meaning
Data-driven decision making leverages data, rather than gut instinct or intuition, to inform decisions
Organizations that use data effectively can gain a huge strategic advantage and competitive edge
Data-driven decision making involves six key steps: identify the problem, define success, collect and prepare the data, analyze and profile the data, communicate findings, and advocate for action
Data in its raw form holds little value and needs context and insight to become actionable
Data, combined with insight and action, can inspire stakeholders to make decisions based on evidence, rather than guesswork or gut instinct
