Data Analytics: How to Effectively Communicate Data Insights to Drive Decision-Making

25 August 2021
Data_analytics

Report written by Marina Ghazaryan.

Data-driven decision-making is the process of making organizational and strategic decisions based on actual objective data instead of on intuition or observations. Today, every company in every industry aims to minimize the likelihood of business decisions going awry. Data is usually the answer.

Data-driven decision-making, streamlined

Before making inferences from the available data, it is important to go through the process of identifying and amassing core information about the company’s industry, its competitive landscape, and its customers. Simply put, the more a company knows, the more accurate the decision can be.

The data collection process should normally start by identifying precise business questions that need to be answered in order to achieve the organizational goals. This will help to streamline the data collection process, and avoid unnecessary waste of resources.

It is also important to compile and coordinate all data sources. Data analysts typically perform their work following the ‘80/20 rule’, which means that they spend 80 percent of their time cleaning and organizing data, and the remaining 20 percent performing the actual analyses. Having clean and orderly information is essential. Generally speaking, data cleaning is the process of preparing raw data for analysis by removing data that is incorrect, incomplete, or irrelevant.

The next step after cleaning the data is analyzing the information using various statistical models. Here, the data analyst can start to build models to test the available data and attempt to find initial answers to the business questions identified earlier in the process. Testing the different models (linear regressions, decision trees, random forest modeling, and so on) can help the analyst to determine which method is best suited to the data set. It is also important to decide on the best way to present the information, ideally in an effective, and often visual, way.

Data analysts use these three common ways to present the information:

  1. Descriptive information - Presenting facts only.
  2. Inferential information - Presenting the facts combined with an interpretation of what those facts indicate in the context of a particular project.
  3. Predictive information - Presenting an inference based upon facts and advice for further action based on the analyst’s reasoning.

And finally, the cycle comes to an end with a conclusion stage, which is aimed at supporting the stakeholders throughout their deliberation and decision-making process. The more the findings are presented in an understandable manner the better the chances of successfully propelling the company’s strategy onward.

Thus, the art of data storytelling is likely the most important skill for data analysts, enabling them to communicate their findings with key stakeholders as effectively (and as captivatingly) as possible.

 
The process of data analytics

This has been an extended preview.

The full report can be accessed by Nimdzi Partners. The full publication goes into detail on how to turn insights into action, and offers tips for data storytelling, as well as tools that help drive the data story. If you are not a Nimdzi Partner, contact us.
Marina_Ghazaryan_Localization_Researcher_Nimdzi_Insights

This publication was researched and written by Marina Ghazaryan, Nimdzi's Data Analyst. If you wish to learn more about this topic, reach out to Marina at [email protected].

 

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