Data Analytics

What is Data Analytics?
Data Analytics is the process of analyzing data sets in order to draw conclusions about the information they contain. Data Analytics involves the collection, cleansing, and analysis of data to identify patterns and trends in order to make better decisions. It can involve statistical analysis, predictive modeling, and machine learning algorithms. Data Analytics is used to uncover insights that can be used to improve business operations and strategies.

What are the different types of Data Analytics?

1. Descriptive Analytics: This type of analytics focuses on summarizing data in order to understand the past and present state of a business. This includes summarizing data using metrics, such as averages and frequencies, or by using visualization tools such as charts and graphs.
2. Diagnostic Analytics: This type of analytics is used to investigate data in order to identify trends, patterns, or correlations between different elements. It can also be used to identify root causes for certain outcomes or behaviors.
3. Predictive Analytics: This type of analytics uses statistical techniques and models to predict future outcomes based on historical data. It is often used for forecasting demand and sales, or for predicting customer behavior.
4. Prescriptive Analytics: This type of analytics combines predictive analytics with optimization algorithms to recommend the best course of action for a business. It can be used to optimize marketing strategies, product pricing, and resource allocation.

Why Data Analytics is important for an organization?
Data Analytics is important for an organization because it helps to identify patterns and trends in data, allowing organizations to make more informed decisions. It provides insights into customer behavior, market trends, and operational performance. Data Analytics can also be used to improve customer experience, drive business growth, and increase efficiency by helping organizations identify areas for improvement. Additionally, Data Analytics can be used to uncover potential opportunities for an organization to capitalize on.

Steps to implement Data Analytics tools in an organization

1. Understand Business Goals and Challenges: The first step to implementing data analytics tools in any organization is to understand the business goals and challenges that the organization is facing. This will help determine the types of data sets and analysis techniques needed to address the issue.
2. Identify Data Sources: The next step is to identify data sources that can be used for analysis. This may include internal databases, external sources such as government or industry data, or even public sources such as social media.
3. Select Appropriate Analytics Tools: Once the data sources have been identified, organizations must select appropriate analytics tools for their needs. This may include statistical software packages, machine learning algorithms, or visualization tools.
4. Collect and Analyze Data: After selecting the right analytics tools, organizations must collect and analyze the data. This involves cleaning and preparing the data, as well as running analysis algorithms to get useful insights.
5. Interpret Results: After analyzing the data, organizations must interpret the results and draw conclusions from them. This may involve drawing conclusions from patterns in the data or identifying correlations between different variables.
6. Communicate Results: The final step is to communicate the results to stakeholders and decision makers in a way that is easy to understand and actionable. This may involve creating visualizations or reports that clearly explain the insights gleaned from the analysis.

List of Commercial Data Analytics Tools available today

1. Microsoft Power BI
2. Tableau
3. Qlik Sense
4. SAP Business Objects
5. SAS Enterprise Miner
6. IBM Cognos Analytics
7. Oracle Hyperion
8. Alteryx Analytics
9. Looker
10. Google Data Studio
11. Sisense
12. Domo
13. MicroStrategy Analytics
14. GoodData
15. TIBCO Spotfire
16. Zoho Analytics