Data-Driven Decision Making
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What Does It Mean to be “Data-Driven”?
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Being "data-driven" means using data to inform decision-making processes. This can include using data to identify patterns and trends, measure performance, and make predictions. In a data-driven organization, data is used to inform strategy, operations, and other key business decisions.
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Being data-driven involves collecting, analyzing, and interpreting data to make more informed decisions. This can include using data visualization tools, statistical analysis, and machine learning algorithms to extract insights from the data. The process of being data-driven also involves continuously monitoring and updating the data to ensure that the insights are accurate and relevant.
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Being data-driven also requires the ability to act on the insights obtained from the data, this means that the organization needs to have the structure, processes, and culture in place to act on the information obtained. This includes having a data-savvy leadership and a culture that supports data-driven decision-making, as well as the right tools and technologies to collect and analyze data.
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In summary, being data-driven means using data to inform decision-making at all levels of an organization, and continuously monitoring, analyzing, and acting on the data to make better decisions, improve performance, and achieve strategic goals. ?
How to Make Data-Driven Decisions
To effectively utilize data, professionals must achieve the following:
1. Know your mission.
A well-rounded data analyst knows the business well and possesses sharp organizational acumen. Ask yourself what the problems are in your given industry and competitive market. Identify and understand them thoroughly. Establishing this foundational knowledge will equip you to make better inferences with your data later.
Before you begin collecting data, you should start by identifying the business questions that you want to answer to achieve your organizational goals. By determining the precise questions, you need to know to inform your strategy, you’ll be able to streamline the data collection process and avoid wasting resources.
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2. Identify data sources.
Put together the sources from which you’ll be extracting your data. You might be coordinating information from different databases, web-driven feedback forms, and even social media.
Coordinating your various sources seems simple but finding common variables among each dataset can present a tremendously difficult problem. It can be easy to settle for the immediate goal of utilizing the data for your current purpose alone, but it’s wise to determine whether this data could also be used for additional projects in the future. If so, you should strive to develop a strategy to present the data in a way that’s accessible in other scenarios as well.
3. Clean and organize data.
Surprisingly,?80 percent?of a data analyst’s time is devoted to cleaning and organizing data, and only 20 percent is spent performing analysis. This so-called “80/20 rule” illustrates the importance of having clean, orderly information before you can attempt to interpret what it might mean for your organization.
The term “data cleaning” refers to the process of preparing raw data for analysis by removing or correcting data that is incorrect, incomplete, or irrelevant. To do so, start by building tables to organize and catalog what you’ve found. Create a data dictionary—a table that catalogs each of your variables and translates them into what they mean to you in the context of this project. This information could include data type and other processing factors, as well.
4. Perform statistical analysis.
Once you’ve thoroughly cleaned the data, you can begin to analyze the information using statistical models. At this stage, you will start to build models to test your data and answer the business questions you identified earlier in the process. Testing different models such as linear regressions, decision trees, random forest modeling, and others can help you determine which method is best suited to your data set.
Here, you will also need to decide how to present the information to answer the question at hand. There are three different ways to demonstrate your findings:
Clarifying how the information will be most effectively presented will help you remain organized when it comes time to interpret the data.
5. Draw conclusions.
The last step in data-driven decision-making is concluding. Ask yourself, “What new information did you learn from the collection of statistics?” Despite pressure to discover something entirely new, a great place to start is by asking yourself questions to which you already know—or think you know—the answer.
Many companies make frequent assumptions about their products or market. For example, they might believe, “A market for this product exists,” or, “This is what our customers want.” But before seeking out new information, first put existing assumptions to the test. Proving these assumptions are correct will give you a foundation to work from. Alternatively, disproving these assumptions will allow you to eliminate any false claims that have, perhaps unknowingly, been negatively impacting your company. Keep in mind that an exceptional data-driven decision usually generates more questions than answers.
The conclusions drawn from your analysis will ultimately help your organization make more informed decisions and drive the strategy moving forward. It is important to remember, though, that these findings can be virtually useless if they are not presented effectively. Thus, data analysts must become skilled in the art of?data storytelling?to communicate their findings with key stakeholders as effectively as possible.
Mastering Data-Driven Decision Making
Data-driven decision-making is an essential process for any professional to understand, and it is especially valuable to those in data-oriented roles. For novice data analysts who want to take a more active part in the decision-making process at their organization, it is essential to become familiar with what it means to be data-driven.