BI, Analytics And Decision Support

Full Answer Section

Where Does the Data for Business Analytics Come from?

Data for business analytics can come from a variety of sources, including:

• Internal data: Internal data is data that is generated by the business itself. This data can include sales data, customer data, financial data, and operational data.
• External data: External data is data that is generated outside of the business. This data can include market research data, industry data, and social media data.

Sources and Nature of Incoming Data

The sources and nature of incoming data can vary depending on the type of business and the specific analytics tasks that are being performed. For example, a retail business may collect data from its point-of-sale system, customer loyalty program, and website. This data could be used to track sales, identify customer trends, and optimize marketing campaigns.

A manufacturing business may collect data from its production lines, quality control system, and inventory management system. This data could be used to improve efficiency, reduce waste, and predict demand.

Common Metrics for Analytics-Ready Data

The most common metrics that make for analytics-ready data include:

• Accuracy: The data should be accurate and free of errors.
• Consistency: The data should be consistent in its format and structure.
• Completeness: The data should be complete, with no missing values.
• Timeliness: The data should be timely and up-to-date.

Why Raw Data Is Not Readily Usable by Analytics Tasks

Raw data is not readily usable by analytics tasks because it is often unorganized, incomplete, and inconsistent. Before raw data can be used for analytics, it needs to be cleaned and processed. This process may involve removing errors, filling in missing values, and converting the data into a consistent format.

Visualizing Data

Data visualization is the process of creating visual representations of data. Data visualization can be used to communicate insights from analytics to a variety of audiences, including decision-makers, business analysts, and data scientists.

There are a variety of data visualization tools and techniques available. Some common data visualization techniques include:

• Charts: Charts are a popular way to visualize data. Charts can be used to show trends, patterns, and relationships in data.
• Graphs: Graphs are another popular way to visualize data. Graphs can be used to show how two or more variables are related.
• Maps: Maps can be used to visualize data that is geographically distributed.
• Dashboards: Dashboards are interactive data visualizations that can be used to track key metrics and identify trends.

Conclusion

Data is essential for analytics. Analytics tools use data to generate insights that can be used to improve decision-making, increase efficiency, and reduce costs. Data for business analytics can come from a variety of sources, both internal and external. Before raw data can be used for analytics, it needs to be cleaned and processed. Data visualization is a powerful tool for communicating insights from analytics to a variety of audiences.

Sample Answer

The Importance of Data in Analytics

Data is the foundation of analytics. Without data, there is no analytics. Data provides the raw materials that analytics tools use to generate insights. The quality and quantity of the data that is available to analytics tools has a direct impact on the quality and quantity of the insights that they can generate.

Can One Think of Analytics Without Data?

No, one cannot think of analytics without data. Analytics is the process of extracting insights from data. If there is no data, then there is no analytics.