What are the latest data analysis techniques that every analytical professional should know?

1 Data storytelling

Data storytelling is the art and science of transforming data into engaging and meaningful narratives that can influence decisions and actions. Data storytelling is not just about creating charts and graphs, but also about choosing the right data, context, and message for your audience. Data storytelling can help you communicate your findings more effectively, persuade your stakeholders, and demonstrate your value as an analytical professional. To master data storytelling, you need to develop skills such as data visualization, narrative structure, and emotional appeal.

3 Data mining

Data mining is the technique of applying advanced algorithms and methods to extract patterns, trends, and associations from large and complex datasets. Data mining is useful when you need to discover hidden or unknown information, such as customer segments, market opportunities, or fraud detection. Data mining can help you generate hypotheses, test assumptions, and validate results. To conduct data mining, you need to use tools such as R, Python, or SAS that can perform tasks such as classification, clustering, association, or anomaly detection.

5 Data modeling

Data modeling is the technique of creating a representation of your data that reflects its structure, relationships, and rules. Data modeling is important when you need to design, build, and maintain your data systems, such as databases, warehouses, or pipelines. Data modeling can help you organize your data, optimize your performance, and facilitate your analysis. To create data models, you need to use tools such as ERD, UML, or DFD that can help you define and document your data elements, entities, and associations.

6 Data ethics

Data ethics is the discipline of applying ethical principles and values to your data practices, such as collection, analysis, and sharing. Data ethics is relevant when you need to respect the rights, privacy, and dignity of your data subjects, such as customers, employees, or partners. Data ethics can help you comply with the laws and regulations, protect your reputation and trust, and promote social good. To implement data ethics, you need to follow guidelines such as FAIR, GDPR, or CCPA that can help you ensure your data is fair, transparent, and accountable.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?