Le processus décisionnel fondé sur les données expliqué : Prendre des décisions commerciales plus intelligentes grâce aux données
Qu'est-ce que la prise de décision basée sur les données, et pourquoi vous devriez commencer à prendre des décisions plus intelligentes basées sur les données et les informations.
Nous expliquons la prise de décision basée sur les données (‘data-driven decision-making’ ou DDDM) et pourquoi elle est importante pour les cadres. Comprenez comment commencer à utiliser les données pour prendre des décisions stratégiques et surpasser vos concurrents grâce aux bonnes informations.
Making smart business decisions is essential for the success of any business – I guess we can all agree on that. But how to make strategic and important decisions ? Most CEOs and top executives make decisions based on experience and intuition. And while it can sometimes work, relying on these two factors alone is far from enough. It’s no wonder, then, that companies that use Data-Driven Decision-Making (DDDM) are 19 times more likely to be profitable than their counterparts that don’t. not on the data to make crucial decisions. (Source :
This is where data comes in. By analyzing data, you can better understand what is happening in your business. To make the best decisions, you need accurate and timely data. This data may come from a variety of sources, including customer surveys, financial reports, and website analytics. This article explains how to use data to make smarter business decisions.
Use data to make decisions?
It is difficult to make decisions. It can be difficult to know what is the right thing to do. But what if you could use the information to help you make your decisions? This is what data-driven decision making is. You use data from different sources to help you make better choices. This can be scientific data, business analysis or market intelligence.
Data-driven decision-making has many benefits. First, it can help you make decisions faster. Indeed, you have all the information you need in front of you. There is no need to go looking for other data or trying to interpret it yourself. This can save you a lot of time in the long run.
Another benefit is that it can help you make more accurate decisions. Indeed, you do not trust your instincts or your intuition. You base your decisions on facts and evidence. This can lead to better results for your business.
What is Data Driven Decision Making (DDDM)?
Data-Driven Decision-Making (DDDM) is the process of using data and statistics to inform and guide business decisions. DDDM can include everything from understanding what happened in the past, what is happening now, and what might happen in the future, to using machine learning models to analyze the data. Typically, these models are built using a machine learning algorithm on a dataset that has been annotated with desired outcomes (decisions). The model can then be used to predict the results of new data sets.
Businesses have always used data in one form or another to make decisions, but the phrase « data-driven decision-making » generally refers to the increased use of data analytics and predictive modeling. these last years. Thanks to big data, companies now have access to more data than ever before, and they are increasingly turning to analytical tools and techniques to make sense of it.
The analytical part is at the heart of data-driven decision-making. It’s about gathering, cleaning, analyzing and seeing patterns on which management can make decisions. The goal is to use this data to understand what is happening in your business and make better decisions.
Analytics can be used for everything from determining which products to sell to understanding customer behavior. By analyzing the data, businesses can identify trends and determine what actions to take to take advantage of them. For example, if a business notices that its sales are increasing at a certain time of year, it may decide to increase its marketing efforts at that time.
The benefits of data-driven decision making are clear. By using analytics, businesses can improve their decision-making process, become more efficient, and earn more money. To reap these benefits, businesses need to collect and use the right data effectively.
Businesses should be aware of a few limitations of data-driven decision-making. First, the data can be misinterpreted. It is important to remember that the data is only part of the picture and should not be relied upon blindly. Also, data can be biased if not collected or analyzed properly. Finally, businesses may not have all the relevant data, which can hamper their ability to make informed decisions.
Despite these limitations, data-driven decision-making remains a powerful tool that companies can use to improve their performance. By being aware of these limitations and taking them into account, companies can make better decisions and achieve real results.
What are the benefits of data-driven decision making?
Businesses that use data-driven decision making can enjoy several advantages over their competitors. It’s not just the content of the data, but also what it brings to managers and decision makers across the enterprise that makes DDDM so effective.
Here are some of the most important benefits:
By making data-driven decisions, companies avoid wasting time and resources on ineffective strategies. It also allows companies to adapt more quickly to changes in the market or the environment.
Data-driven decision making can help businesses make decisions faster because they have all the information they need at their fingertips. This is especially useful in fast-moving industries or markets, where quick decisions based on pure feelings can be costly.
Data provides a factual basis for decision-making, which can improve the accuracy of those decisions. This improves results and, therefore, confidence in decisions. This way, companies can speed up the decision-making process and avoid costly mistakes based on biases, mental traps or gut feelings.
By having more facts to rely on, businesses can make better decisions that are more likely to succeed. This results in increased profits and competitiveness. The quality of decisions becomes significantly better and more reliable.
Improved service and customer experience
Data-driven decision-making can help businesses better understand their customers’ needs and preferences, enabling them to deliver a better level of customer service. Thus, it is possible to personalize offers, increase customer retention and even increase Customer Lifetime Value (CLV) over time by providing better services, better communication, etc. .
Improved strategic planning
Having access to data allows companies to make long-term plans that are more likely to succeed than those made without data analysis. Data-driven strategic management and data-driven strategic planning can be an important cornerstone for companies that want to outperform others. But it’s also worth mentioning here that managers need to understand any possible data-related biases or challenges in predictions, and not be fooled by faulty analyses.
Data-driven decision making allows companies to be more agile and adapt quickly to market or industry changes. By obtaining near real-time data, changes in market dynamics can be observed and proactively addressed. This allows you to stay ahead of the market and avoid being only reactive.
Increased confidence in decisions
With data supporting your decisions, you and your team can have increased confidence in your choices. This can lead to improved morale and motivation to make faster, bolder decisions that help the organization. Generally, management is happier with their decisions because they can rely on data instead of having to justify their decisions by relying on instinct.
More efficient allocation of resources
Data can help you understand where your resources are best used and where your efforts should be focused. This allows you to allocate them more efficiently, which increases profits. Also, it avoids the best projects that have little impact on the business.
7 steps to get started with data-driven decision making (DDDM)
All in all, data-driven decision-making is not that complicated to implement. However, managers need to be aware of the pitfalls to avoid and ensure that the organization is ready to support Data-Driven Decision-Making (DDDM). The DDDM process involves four key steps: data acquisition, data cleaning and preparation, data analysis, and decision making.
1. Set clear goals and objectives
Without clear objectives, it would not be easy to know what type of data to collect and how to analyze it. Managers should work with their teams to set specific goals that DDDM can help achieve.
2. Acquire the right data
Once the objectives have been set, managers must identify the data needed for decision-making. This includes internal (eg, financial records, website analytics, customer surveys) and external (eg, market trends, social media, and market research reports) data. Data must be accurate and current, otherwise it will not be useful for decision making.
3. Prepare and clean the data
After acquiring the necessary data, it must be cleaned and prepared for analysis. This includes data formatting, missing values, and outlier detection. These steps are important to ensure that the data is ready for analysis.
4. Perform data analysis
Once the data is cleansed and prepared, it can be analyzed to aid in decision-making. This includes exploratory data analysis, statistical modeling and predictive analysis. The goal is to find information that can help managers make better decisions.
5. Share information
After analyzing the data, it is important to share this information with relevant stakeholders. These stakeholders include managers, employees and other decision makers. A good way is to include the data in existing tools that stakeholders use in their daily work. Information should be presented in a clear and concise manner so that it can be easily understood and used for decision making. It is also useful to visualize data and create easy to understand reports that will be shared internally.
6. Make decisions
With the right database and the right insights, managers can make informed decisions. This includes strategic decisions (eg, what new products to develop) and operational decisions (eg, how to improve customer service).
7. Track and adjust
Finally, it is important to monitor the results of decisions and adjust them accordingly. This feedback loop is important for ensuring that decisions are effective and for making further improvements.
Create a data-driven company culture
Data-driven corporate culture is key to getting the most out of data-driven decision making. A business that is not driven by data will not be able to use it effectively to make better decisions. Data-driven decision-making can help a business identify opportunities and problems, but if the corporate culture is not supportive of data analysis, that information will go unused.
To be successful, a data-driven culture must have the following characteristics:
1. Leaders must be prepared and empowered
Leaders must receive the tools and training to effectively use data in their decision-making process. Without it, they won’t be able to take full advantage of data-driven decision making.
But leaders must also be held accountable for making data-driven decisions. This accountability can take the form of key performance indicators ( KPIs ) and other metrics that help determine whether data is being used effectively or not. If leaders are not prepared or accountable, data-driven decision-making will not be successful.
2. Employee training
All employees should be trained in the use of data in their decision making. This training should cover everything from basic data knowledge to more advanced analysis techniques. Without this training, employees will not be able to fully utilize data-driven decision making. They may not even be aware of the opportunities that data can provide.
3. Find and Empower Internal Data Defenders
Data advocates can be hard to find, but they’re essential to driving a data-driven culture. These individuals will be responsible for evangelizing the use of data and analytics within the company. Data advocates must be able to effectively communicate the benefits of data-driven decision-making to others and empower them to use data in the right way.
4. Integrate data everywhere – make it « normal ».
To create a data-driven culture, companies need to integrate data into all aspects of their operations, from meetings to project proposals to strategic planning. By doing so, everyone in the business will understand the importance of data and how it can help them make better decisions.
5. Infrastructure and processes must be ready
Data-driven decision making requires a lot of data, infrastructure, and resources. From collection, to cleaning, to analysis and integration into day-to-day tasks, the necessary infrastructure and processes must be in place to support a data-driven culture. If any of these elements are missing, employees can become frustrated and revert to « gut decision making. »
Organizations that want to thrive in the future must make decisions based on a solid foundation of data. With so much change and volatility in today’s business landscape, it’s more important than ever to make data-driven decisions. A data-driven culture empowers everyone in your organization to think critically and make informed decisions. Implementing these practices won’t be easy, but careful planning and execution can help your business stay relevant and successful in the future.
It’s a good time to start making data-driven strategic decisions, building models, starting small, and getting your feet wet. Many companies have enormous potentials that they can unlock by simply using a little data and information to better invest in projects that matter and create the greatest impact for the organization.
Author: Benjamin Talin , CEO of MoreThanDigital
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