There are some decisions we make intuitively or spontaneously in the moment, like splurging on a new outfit, choosing what we want to eat for dinner, or buying bonuses on slots. Then, there are decisions that we want to research and make from an informed point of view so that we can feel assured we’re making the decision that leads to the best outcome for our future selves.

In our personal lives, we might approach these decisions with tools like a pros and cons list of different opportunities or tracking our habits, like our spending, to determine where we can make improvements. However, decision-making can be more complex and multifaceted in the business world, and this is where data science enters the picture.

Data science can remove the guesswork from decision-making processes by providing the information needed to make the best decisions for a company, its employees, and its clients. Because of this, using data science to drive decisions is incredibly common in the business world.

What Is Data Science?

Data science centers around using data to make informed decisions. As mentioned, it’s commonly used in business settings; however, it also has a wide range of other applications. For instance, it can apply to the education industry in situations such as assessing a class’s comprehension of a topic or the healthcare industry for improving patient comfort in a hospital or clinic. It can also detect and prevent fraud and help build predictions like business outlooks or financial forecasting.

How Does Data Science Help Make Decisions?

Data-driven decision-making (DDDM) allows decision-makers to set aside their emotions and turn to real numbers and information. This instills confidence in the decisions and actions taken towards reaching an objective. DDDM also allows businesses, organizations, and individuals to be more proactive and even increase savings in the long run.

Of course, to be able to make use of data science to make decisions, it’s important to understand the process of collecting and assessing data; otherwise, you don’t benefit from it.

What’s the Process of Using Data Science for Decision Making?

The process of collecting and assessing data to make informed decisions can be broken down into five key steps. Each step builds on the previous, with the first step defining your question.

1. Define Your Question or Objective

The rest of this process won’t be beneficial without defining what you’re looking for or trying to answer. You want to clearly define what you hope to garner from the data. In a business setting, defining your research objective might involve looking at the company’s long-term goals to determine what key performance indicators (KPIs) need to be collected and assessed.

2. Find Your Data Sources

Once you’ve established your goal, you can start looking for your data sources. In some cases, it will simply be looking back at records or performance metrics. For instance, if you’re looking to cut costs, you’ll likely go back over expenditure reports and collect information about where money is going in and going out. If you’re looking to build a new ad campaign or social media campaign, you may even look back over social media performance metrics or past campaign stats.

Some data science methods, particularly for businesses, may also involve finding external data, such as data on how competitors are performing or finding data on how your target audience is spending (or not spending) their money.

In other cases, collecting new data through methods like surveys may be required. For instance, if you want to understand how your customers perceive your brand or employee sentiment, you may need to create a survey. In this case, data sources are more likely to be qualitative, but this, too, is a data type in which a lot of information and insight can be gained.

3. Organize the Data

Once you’ve determined where your data is coming from or how you’ll collect it, you’ll need to find a central place to store and organize it. Many tools are available for this, such as business intelligence programs offered by productivity platforms like Tableau or Microsoft BI. These tools can also help you with the next steps of the data science process. You can also use tools like Microsoft Excel or Google Sheets.

These tools allow you to organize data in one place, making it easier to compare and contrast the information collected, an essential capability for analyzing the data.

4. Analyze the Data

With your data now in a central location, you can start to analyze it. You can look for patterns using different analytic models. Commonly used analytical models include clustering, outliers, forecasting, and time series models. The model you use will depend on the question or objective guiding your data collection.

As with organizing data, tools such as business intelligence programs are available to help analyze data. These programs can help by allowing you to input the KPIs you track and get visual and numerical representations of them.

5. Draw Conclusions

With a thorough analysis in hand, you can assess the patterns and figures to determine the best decision for your data research objective. You can also use any projections or predictions as benchmarks once you implement a decision. Even if you don’t want or need the benchmarks, you’ll generally want to hold onto the analysis and conclusions for future reference.

Wrapping Up

While data science may sound like an overwhelming way to make decisions, it’s actually a straightforward and reliable method for making more informed decisions when broken down into basic steps. Using data-driven decision-making can leave you feeling more confident in your choices, help you reach goals more efficiently, be proactive in preventing problems, and save you time and money in the long run.