Predictive Analytics: A Beginner’s Guide To Data-driven Decision Making

By Author: Chulani De Silva
Total Articles: 37
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In today's business landscape, making decisions is a daily occurrence. However, not all decisions are created equal. Some are impulsive, while others are well-thought-out and grounded in data. It might be challenging to decide which course to take when a big decision is looming for the company. Going with the gut instinct may make one feel more in control of their decisions, but will those decisions be the best ones for the company? Many founders in platforms like EquityMatch, might feel more at rest knowing that the decisions they make, especially when it comes to securing funding for business growth, are supported by facts, and intended to have the greatest possible commercial impact.

In this beginner's guide, we will explore the concept of data-driven decision-making, why it is crucial for businesses, and how to get started.
What is Data-Driven Decision Making (DDDM)?

DDDM is the process of making informed choices based on data analysis rather than relying solely on intuition or gut feelings. It involves collecting, processing, and analyzing data to gain insights that guide strategic and operational decisions.
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... It simply means using facts or data to identify patterns, inferences, and insights to guide your decision-making. Essentially, DDDM entails attempting to avoid bias and emotion. As a result, one may make sure that the objectives and roadmap of their business are founded on data and the patterns they have deduced from it, rather than on personal preferences (Asana, 2022).

So, the question is Why is DDDM important?

No business, corporation, or organization makes the claim that making decisions based solely on intuition will produce reliable results. The majority of professionals are aware that, in the absence of facts, bias, and erroneous assumptions, among other problems, can cloud judgment and result in bad decisions. In the world of securing funding for business, data-driven decision-making is paramount, as it provides the evidence and insights needed to convince investors and lenders of the soundness of your business proposals.

Nevertheless, 58 percent of respondents to a recent survey stated that their organizations base at least half of their routine business choices on gut instinct or intuition rather than facts (Janoschek, 2016). Thus, it is necessary that entrepreneurs are known about DDDM in order to gain more milestones in their business.
The 6 Steps to Make Data-Driven Decision Making

01. Know the Vision and Mission

Understanding the future vision of your organization is necessary before you can make decisions that are well-informed, especially when it comes to securing funding for business. This enables you to make judgments based on information and a strategy. Graphs and figures are meaningless without the supporting context, which is critical when presenting your business plans to potential investors for startup funding.
A data analyst who is well-rounded will be familiar with the industry and have keen organizational skills. Consider the issues that exist in the industry and market that you are in. Find them and fully comprehend them. Having this fundamental information will enable you to analyze your data more effectively in the future (Joubert, 2019).
To reach your organizational objectives, you should first decide what business questions you want to have answered before you start gathering data. It is also needed to streamline the data collection process and save money by figuring out the specific questions you need to ask to inform your strategy.

02. Research Business Teams

To be successful, it is essential to gather feedback from workers across the organization to understand long-term and short-term objectives. These factors influence the questions people include in their analyses as well as how you rank trustworthy data sources (QuestionPro Collaborators, 2022).

Your analytic deployment and future state, including roles, responsibilities, architecture, and processes, as well as success indicators to gauge progress, will be influenced by insightful feedback from throughout the organization.

03. Hunt for Sources

The sources from which you will be gathering your data should be put together! Once an individual has determined the objective they are aiming towards, they may begin gathering information.

Depending on the kind of data they are gathering, they will employ different tools and data sources. Use a universal reporting tool if the objective is to analyze data sets relevant to internal business processes. Reporting tools provide a single point of reference for monitoring how work is moving throughout your organization. Some reporting applications, such as Microsoft's Power BI, allow individuals to compile data from a variety of outside sources. One of those tools can also be used to examine marketing trends or rival analytics.

04. Organize the Data

Surprisingly, only 20% of a data analyst's work is spent actually conducting analysis; the other 80% is spent cleaning and organizing data (Werth, 2023).

Making good business decisions requires organizing the data to enhance data visualization, a crucial skill when seeking funding for business. Making the best judgments is challenging if an individual cannot gather all of their pertinent data in one location and comprehend how it interacts. Effective data organization and visualization can be the key to presenting a compelling case to potential investors and securing the necessary funding for business growth.

05. Conduct Data Analysis

After fully cleaning the data, you may start utilizing statistical models to examine it. Data Analysis must be done to get useful information from the data that will aid you in making decisions.

Testing various models, including decision trees, random forest modeling, linear regressions, and others will assist in identifying the approach that is most appropriate for your data collection.

There are three ways to present the findings:

i. Descriptive Information: Simply, facts
ii. Inferential Information: The facts as well as an analysis of what those facts mean in relation to a specific undertaking.
iii.Predictive Information: A conclusion drawn from the evidence and suggestions for next steps based on logic.

06. Drawing Conclusions

Making a decision is the final stage in DDDM.

Always ask, “What new information did you learn from the collection of statistics?”, “How can use this to attain my business goals?”

Once you can respond to these inquiries, you have effectively completed data analysis and ought to be prepared to make data-driven business decisions.
However, test presumptions first before looking for additional facts. There will always be a starting point if you can demonstrate that these presumptions are true. As an alternative, proving these presumptions erroneous would enable an individual to get rid of any untrue statements that may have been hurting your business without your knowledge. Remember that a great data-driven decision typically results in more questions than it does answers.

The Bottom Line!

To make decisions that will benefit the company, you must have the appropriate data in front of you! Any professional would benefit from having a solid understanding of data-driven decision-making, but those in data-focused professions, such as data analysts and data scientists, would benefit most. Learning what it means to be data-driven is crucial for new data analysts who wish to participate more actively in the decision-making at their firm, especially when it comes to securing startup funding or funding for business growth. Data-driven insights have been instrumental in presenting a compelling case to many potential investors and lenders in platforms like EquityMatch (www.equitymatch.co).