Data-driven means using actual data to identify patterns and insights, ensuring decisions are made without bias or emotion. This approach ensures product goals and roadmap are based on facts and patterns rather than personal preferences.

Benefits of data-driven decision-making (DDDM)

Gain a Competitive Edge

DDDM can give your company a competitive edge. By using data effectively, you gain deeper market insights, make informed decisions, spot opportunities competitors might miss, and tackle potential issues early. 

Reduce Bias

Relying on data helps eliminate bias and subjectivity in decision-making. This ensures decisions are based on evidence rather than personal preferences or opinions, making them more objective. 

Uncover Unanswered Questions

Data can reveal questions you didn't know existed, enhancing your understanding of your business. By identifying these issues, you can make better-informed decisions. 

Set Achievable Goals

Analyzing past performance data helps set measurable goals for your team. For instance, you can aim to increase customers by 30% yearly or cut expenses by $150,000 yearly. 

Streamline Company Processes

Analyzing trends in team performance and costs helps make more informed process improvements. This includes better risk management, more accurate cost estimation, improved team induction, and enhanced customer service based on feedback data.  

Tips for Becoming More Data-Driven

Look Beyond Numbers

When exploring data. Contextualizing data enhances decision-making by providing a deeper understanding. 

Data Should Support Decisions

Always ensure data supports organizational decisions. Data's objectivity makes it a valuable resource for making informed choices. 

Prioritize Accuracy and Reliability

Reliable data is crucial. Having a single, reliable data source simplifies the decision-making process. 

Utilize Data Visualization

Clear data visualization helps uncover insights and patterns. Experiment with different visualization tools and techniques to improve your storytelling skills. 

Implementation Example 

Let's analyze the approach of company X. Let's imagine that the team of company X has 20 people and consists of 5 departments, fully providing all work processes. Development works perfectly according to Kanban, everyone knows how to plan the timing of launching features, everything works by the clock. There are 1300 customers. The executives want systemic revenue growth. 

The request is, "We have features, and it seems like they are not very synchronized. Secondly, it seems like teams are making decisions subjectively. Whoever wants to go where, runs were." Management sets a goal for company X - sustainable growth. It needs to be digitized to decompose it, and then the goal is expressed as: 1.attract 2000 new customers 2.enter the top solutions market 3.monthly earnings of $20,000. Sounds logical.

The team looks at this story, accepts it, and starts thinking. What are the numbers and what are the goals to set for themselves? And it turns out that the goal falls very simply to marketing and sales: to attract more leads and make more sales. They realize that they can be contributors because they influence the lead generation. But the development department and the product department have a strong and free flight of fancy.

What is important and what to fix to make it work:

• each department should have metrics and goals that can be correlated to the company-wide goal and then measure the level of achievement

• each team's contributions should correlate to the company's goal

• metrics must be measured AS IS

• everyone should be focused on a single goal and not lose context.

So, the challenge is to bring everyone into the same context. Everyone needs to understand their contribution and the consequences of their actions. To do this, combine information from different teams into a single dashboard and communication by type: 

Existing customers:

• What support calls have there been in the past time?

• What inquiries have been received in the past time?

• What deviations are there in turnover / traffic / conversions?

And when the product team sees real current customer issues, they need to check "are we doing what customers need? Are our priorities and benchmarks, right?", while keeping in mind:

• how frequent the request is

• how much the query affects risks / turnover / traffic from the customer.

New customers:  What are the barriers for the customer to activate?

Leads:  What are we missing for a transaction?

Product backlog:  Are we currently doing what is prioritized based on the information to date? 

When the team sees how the information is transformed and what effect it is having, it becomes much easier to introduce metrics work, KPIs, and then OKRs for all teams. Each KPI has a context and metric of success behind it. And overall KPI becomes a tool for dealing with deviations. 

How to make DDDM: 5 steps

Follow these steps to turn raw data into actionable insights that support your company's goals.

1. Get Your Vision Right: To use data and strategy effectively, make sure you understand where your company is heading. Context is key when looking at charts and numbers.

2. Get the Data: Once you know your goal, gather the data you need. Use reporting tools to track progress and gather data from various sources for analysis. 

3. Organize Your Data: Good data visualization is crucial. A dashboard can help you display important data to reach your goals.

4. Analyze the Data: Find useful insights in the data to guide your decisions. Look at data from executive dashboards and user research to get a full picture.

5. Make Your Call: Use data patterns to draw conclusions. Ask the right questions to understand what the data is saying and how it can help achieve business goals.