Data Science: A guide to data-driven decision making

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Published on February 8th, 2022

Data-driven decision-making (DDDM) is when you use facts, metrics, and data to make strategic business decisions that align with your goals, objectives, and projects. A business analyst, sales manager, or HR person can make better decisions every day with data when an organization realizes the total value of their data. However, picking the advanced analytics methods to find the next strategic opportunity can’t just be done.

Your company needs to make data-based decision-making the norm, which means making critical thinking and curiosity a part of the culture. When people talk about data, they learn how to use it in real life. This starts with a self-service model, where people can get the data they need, but with security and governance in mind. Training and development opportunities for employees who want to learn data skills are also significant.

Finally, having a group of people who support and make decisions based on data will make others want to do the same. Establishing these core skills will help businesses make more data-driven decisions at all job levels. This means that companies will always question and investigate information to find powerful insights that lead to action.

Although there is a lot of interest in technology and services for data analytics, there is a big difference between how much businesses spend on their data analysis abilities and how well they use data in their businesses. 64 C-level executives were surveyed, and only 72% said they could build a data-driven culture in their companies.

This disconnect is often caused by a misunderstood idea of being a data-driven company or group. When it comes to making data-driven decisions, it’s not just about technology and high-quality data. It also has to do with having the right culture and internal processes.

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Here are some tips that should help businesses become better at Data-Driven Decision Making Process (DDDM)
Check your own biases
Start Collecting Data as Soon as You Can
Ask the right questions
Look for Data to Help You Answer These Questions
Don’t be afraid to revisit and re-analyze your data
Present the Data Meaningfully
Decision-Making Goals

Here are some tips that should help businesses become better at Data-Driven Decision Making Process (DDDM)

This will help you to make the most productive move in terms of the decision-making activity of your business by accumulating sufficient data in hand.

Check your own biases

Because everyone is biased, making data-driven decisions can be challenging because it’s hard to be completely objective when you’re making them. Often, people see what they want to see. There are ways to keep bias from getting into data analysis and DDDM. You need to understand data science modelling and accept that there is a problem with bias. As long as you know that discrimination exists, you can do a lot to lessen its effect. Work together. This makes sure that data analysts can keep each other in check to keep each other safe. The best way to test your assumptions and findings is to look for conflicting data and ask the right questions.

Start Collecting Data as Soon as You Can

There must be a lot of data collection when the company decides to be more data-driven. This helps in understanding real-world application domains. There must be a conscious effort to collect and log information and set up a way to clean and organize the enormous amounts of data that the business will collect.

Ask the right questions

Good data analysis questions keep the team from going down rabbit holes and following leads that don’t lead anywhere. This goes back to the goals of the team that analyses data, which is why this is important. The group wants to know what they can learn from the data, but what is that? What KPIs will be used to measure different things? We need to see how the data will come in. A data analyst should ask a lot of other questions, like this:

Look for Data to Help You Answer These Questions

Next, look for the data you need to answer the questions you came up with within the previous step. Determine if this data has already been gathered or if there’s a need to set up new ways to get it (both internally and externally).

Don’t be afraid to revisit and re-analyze your data

Analysis teams shouldn’t be afraid to step back and change their minds about how they look at data. Many things will change, but analysts should not see them as mistakes. Instead, these moments should be used to learn and improve their data analysis skills. It will be more accurate if you can figure out what went wrong with the analysis and fix it immediately.

Present the Data Meaningfully

The findings from analyzing business data can only be helpful if they are shown in a way that makes sense. By using software, data analysis teams can create a custom dashboard that tells an up-to-date data story, which helps the organization make smarter data-driven decisions.

When you look at a sales forecast dashboard, for example, you can see at a glance how the company’s most important financial KPIs are doing, such as sales, operating expenses, net profit margin, and expected earnings.

Decision-Making Goals

The time has come for business decisions to be made at this point. When making decisions based on data from the analysis, it’s essential to consider that they must fit into a business plan and goal. Data analysts need to set measurable goals to make sure they’re on the right path.

Many businesses are trying to become more data-driven by improving their data skills, their ability to analyze data and work with others. How your company makes decisions is not easy to change, but incorporating data and analytics into decision-making cycles is how you will see the most positive change in your company. This level of change requires a dedicated effort to build and improve your analytics program.

The IoT Academy can help you understand better the real-world applications of Data Science and how every business makes use of data-driven decisions to increase its efficiency and productivity.