Data-driven decision-making (DDDM) is often considered just another buzzword. However, with the rapidly changing business world (thanks to advancements in AI and machine learning), leaders need data to keep up with or stay ahead of the competition.
This change gave rise to data-driven companies – businesses that value data as a strategic asset and use it to make decisions. Leaders of these organizations understand the need to dive beneath the surface and turn abstract input into valuable insights.
According to Google, data-driven organizations are three times more likely to report significant improvement in decision-making. Leaders who make decisions based on data instead of their gut use verified information and avoid second-guessing and shooting in the dark.
The COVID-19 pandemic highlighted the importance of making the right decisions quickly. Data-driven decisions are even more critical in these circumstances, so it’s not surprising that 65% of B2B sales organizations will transition to data-driven decision-making by 2026.
Here’s what that means.
What is Data-Driven Decision-Making (DDDM)?
Data-driven decision-making (also referred to as data-based decision-making) means that you are using facts and relevant metrics to make decisions.
This can take many forms – from leveraging user testing when deciding on a new product feature to adding additional employee benefits based on the satisfaction survey results. A common thread is that you use data to make decisions instead of instincts, subjective viewpoints, and gut feelings.
Using empirical evidence gives you more confidence when facing tough choices that might impact your whole business. It allows you to turn raw data into actionable insights. And then use those insights to address pain points, detect potential development opportunities, and stimulate profitability.
Every company can leverage the data-driven decision-making process, regardless of size, industry, or products and services. However, you need to be aware that implementing data-driven decision-making takes time and effort.
In the following section, we’ll explain why the benefits are more than worth it.
Benefits of Data-Driven Decision Making
Greater Transparency and Sense of Ownership
Data-driven decisions give your teams consistency and facts-based insights, empowering them to make better-informed choices.
All decisions are made based on data that is proactively democratized across the company. As a result, everyone knows where the company is heading and what challenges it needs to overcome.
A greater sense of ownership on all levels is a common result of this increased transparency. It happens because teams are better aware of the overall company goals and know how their work directly impacts the overall success.
Tighter Collaboration Leading to Innovation and Knowledge Sharing
A significant advantage of data-driven decision-making is better collaboration, as it gives you multiple perspectives on a single issue. As different teams combine their unique viewpoints through the data, you have a chance to look at a problem from different angles before coming to a decision.
Tighter collaboration within and across teams often leads to more open and honest discussions. Which, as we all know, are a great incubator for innovation.
Moreover, continuous data-sharing and teamwork help cultivate the transfer of knowledge and skills. That way, senior colleagues can share their experience with new joiners while being exposed to new ideas and outside-of-the-box thinking.
More Accurate Predictions of Future Opportunities
The more market data you collect and analyze, the easier it becomes to spot emerging patterns. This allows you to identify gaps or potential market opportunities where others might see only challenges.
As a result, you can plan your future steps more accurately and use the information from advanced analytics to make the most of your connections, projects, and partnerships.
Higher Revenue and Profitability
Data can help you increase revenue and expand your customer base when used correctly. How? When you know what your customers like or dislike, want, and need, it’s easier to decide what new products or services to answer with. That is one of the reasons data-driven organizations outperform their competition in profitability and productivity.
These businesses are also 165% more likely to surpass revenue objectives – which is why 81% of companies surveyed by Ernst & Young agree that data should be at the core of all business decisions.
Increased Agility
One of the most significant benefits of data-driven decisions is greater efficiency. Once your data collection and analysis are up and running, you can have real-time data available whenever you need it. As a result, you and your teams can make decisions in seconds or minutes instead of hours.
Thanks to increased agility, you can quickly pivot and take advantage of a new business opportunity or mitigate risks as they appear.
Now that you know more about data-driven decisions and their benefits, let’s see what the process looks like in practice. Following are a couple of data-driven decision-making examples to illustrate that.
Examples of Data-Driven Decision-Making
Google – Performance Reviews, Surveys, and Analytics
In 2002, Google got rid of all its project managers. This was a part of an experiment to see if having managers was all that necessary. Not surprisingly, the answer was a strong yes.
However, Google also wanted to know why managers matter. So they established a People Analytics Department to find out, and ensure that all future HR-related decisions are based on data.
Google had clear objectives (uncovering traits that make a great manager) and knew what information to look for. They went through performance reviews and employee surveys, and interviewed people throughout the company.
Based on all that data, Google identified the eight factors that make a stellar manager. Then, to reinforce their data-driven approach, they introduced a biannual feedback survey to keep learning from their best people.
Netflix – Advanced Data and Analytics
Netflix has always been a leader when it comes to companies using data-driven decision-making. Their every decision, including choosing color palettes, is based on scientific insights and facts. This is why they have invested heavily in building teams of data engineers, analysts, and data visualizers.
Moreover, when choosing new shows, they rely on advanced content data and analytics tools to evaluate the performance of similar programs in the past. As a result, they minimize the risks of such high-stakes decisions.
These examples highlight the effort behind the data-driven approach and the benefits it offers. Now it’s time to show you how to implement data-driven decision-making in your work and throughout your organization.
How to Make Data-Driven Decisions
Set Your Objectives
Formulate your goals and understand what you want to solve, identify, prevent, or achieve. For example, maybe you want to increase sales figures? Or prevent employee dissatisfaction before your people choose to leave? Whatever your goal is, make sure that it’s clearly defined, measurable, and well-documented.
Analytics can only be effective if you and your teams know what you’re looking for and what you’ll do with the data.
Choose Your Data Sources
Choose the sources you’ll use to get your data. These can include anything from various databases and social media platforms to feedback forms, surveys, or reviews.
An important thing to consider is whether you can use the data for more than one project. If yes, you need to plan on structuring the information so that it can be used for different scenarios.
Clean and Organize the Data
Data scientists spend 80% of their time finding, cleaning, and organizing data, and only 20% performing the actual analysis. That is called the 80/20 rule, and it shows the importance of eliminating or correcting irrelevant, erroneous, or incomplete data.
Have your data team develop visual tables and data dictionaries to organize insights and catalog variables. This step is necessary for an accurate and valuable conclusion.
Analyze the Data
Once the data has been collected and cleaned up, it’s time to run the analysis. Having the right skills and tools is crucial for this step. It’s great if your current team has the capacity to do that in-house, but many companies find that they need expert help with this part.
They’ll use statistical models (e.g., decision trees, clustering, time series, logistic regression) to analyze the data and decide how best to present the information.
Turn Abstract Data into Actionable Insights
In the final step, it’s up to you to use the information to make a decision. It includes turning the insights you’ve generated into actionable steps that will help accomplish the goal you’ve set at the beginning.
In this step, it is also essential to present the data in a simple manner so that everyone in your organization can understand how you arrived at your conclusion.
This will not only ensure more confidence in the decision but will serve to promote data-based decision-making throughout your organization.
Choosing the right technology
The last but not least step you need to take on your way to making more data-driven decisions is choosing the right technology solutions to support you.
It might be a cloud-based data warehouse that will help you unify all the data coming from various sources. Or an advanced AI and machine learning solution that would provide actionable insights in real-time.
Which solution you’ll choose should depend on two factors.
First, you need to ensure that the technology fits your needs and the goals you’re trying to achieve, not the other way around.
The other thing to consider is how these solutions connect with each other. It doesn’t matter how much data you have – or how well organized it is – if you can’t analyze it.
When working with our customers, we always start by discussing their business goals and assessing the status quo of their technical environment. This way, our solutions take into account both the technological and business perspective ensuring your future success.
