Data analytics for Decision Models
1. Data analytics for Decision Models
In this video we'll learn about data analytics for decision modeling.2. Informed decisions, better outcomes
Data analytics involves collecting, transforming, and analyzing data to extract information that can improve decisions. In the Decision-making process, Data analytics relates directly to information gathering, where data is collected and examined to produce information and insights that can support decisions.3. Navigating the data journey
You can think of data analytics as a comprehensive journey of analysis and processing to transform data into valuable, actionable information. First, data needs to be identified and gathered from trusted sources.4. Navigating the data journey
Secondly, the data needs to be organized and stored in a structured format. This can be in a relational database or a spreadsheet for example.5. Navigating the data journey
Next, any errors, inconsistencies, or missing values in the stored data needs to be corrected. This process is called Data cleaning.6. Navigating the data journey
Then, this cleaned data can go through a transformation process. This is when the data is restructured to enable analysis.7. Navigating the data journey
Lastly, after the data is collected, stored, cleaned, and transformed, it's time to make conclusions and generate insights to support your decision. Let's take a closer look in each of these steps in the data analytics journey.8. Data gathering
Data gathering is about identifying and collecting relevant data. While gathering data, have in mind the relevancy of that data to the decision problem.9. Data gathering
Be vigilant about the sources of data and only use trusted sources.10. Data gathering
Always try to use multiple sources of data to ensure accuracy and comprehensive insights.11. Data gathering
Preserve ethical standards by protecting the privacy of personal information and conserve data accuracy by avoiding manipulations.12. Data staging
Data staging is storing, organizing and protecting data in a structured format. Initially, choose the right storage considering the types and formats of the gathered data.13. Data staging
Then, load and store the data from all of its sources in a repository.14. Data staging
Next, implement mechanisms such as passwords and permission policies to control access to the data.15. Data staging
Finally, validate and check the accuracy of the data in the repository.16. Data cleaning
The stored data often needs cleaning. This means: Identifying and correcting errors or inconsistencies in the dataset.17. Data cleaning
Removing data irrelevant to the decision-making process.18. Data cleaning
Dealing with missing values in the dataset.19. Data cleaning
And deduplicating data to preserve integrity.20. Data transformation
Once stored, data can be transformed and prepared for analysis. This means, aggregating and summarizing data from multiple sources.21. Data transformation
Normalizing data by organizing it, ensuring consistency and removing redundancies.22. Data transformation
Enriching data, by adding relevant information from additional sources.23. Data transformation
and anonymizing data, removing identifiable information to protect ones' privacy.24. Generate insights
Now, insights can be produced and information can be extracted from the data. This means creating graphs and charts to visualize trends, historical data, and forecasts.25. Generate insights
Running machine learning algorithms to discover patterns and insights.26. Generate insights
Running queries to discover specific information from the dataset.27. Generate insights
And building intelligence reports to communicate findings to the stakeholders involved in the decision problem.28. Let's practice!
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