10 unique ways to Analyze Data

1.???Drill Up and Drill Down

We can drill down and drill up to explore different aspects of business and move between levels of information.

? Drilling Down is the ability to move into further details from parent to child relationships to uncover root causes of anomalies.

? Drilling up is the move up from child to parent to look at the overall biggest picture or to move back and forth to drill up and down.

For example, we can look at revenues for an entire product line and then drill down to see revenue for each individual product in the line. When you finish viewing individual product revenue, we can drill back up.?

Getting familiar with the hierarchy is important. Once we do that, we can drill down and drill up multiple levels at a time. If we want to examine the impact of a single aspect of our business on the whole, we can drill down to the lowest level.

2.???Slicing and Dicing

Slicing and dicing refers to the ability to pivot the dimensions of the data on-the-fly.

It is a way of viewing and comprehending data in a database. Large blocks of data are cut into smaller segments and the process is repeated until the correct level of detail is achieved for proper analysis.It presents the data in new and diverse perspectives and provides a closer view of it for analysis.

For example,

• A report shows sales by channel and by region and zooms into the top-performing regions and shows sales by product for that region.
• A report shows the annual performance of a particular product. If we want to view the quarterly performance, we can use a slicing and dicing strategy to get to the quarterly level.

Most cloud software uses OLAP (Online Analytical Processing). It is a computer process that enables users to select and extract data from different viewpoints. The Slicing and Dicing term is generally used in OLAP databases that present data to the end user in multidimensional cube format like a 3D spreadsheet (called an OLAP cube). The OLAP cube comprises numeric facts called measures which are categorized by dimensions.

3. Segmentation

Grouping data with common attributes, especially useful for things like customer segmentation by customer type (e.g., small business, enterprise business, government)

Customer segmentation is?the process of dividing customers into groups based on common characteristics, so companies can market to each group effectively and appropriately.

The 4 basic types of market segmentation are:

• Demographic segmentation looks at identifiable non-character traits such as age, gender, ethnicity, income level, education, profession/role in a company.
• Psychographic segmentation is focused on your customers’ personalities and interests. Examples are personality traits, hobbies, life goals, values, beliefs, lifestyles etc.
• Geographic segmentation is the easiest way to identify, grouping customers in regard to their physical location. This can be defined in any number of ways, such as country, region, city, postal code.
• Behavioral segmentation groups customers in regard to their spending habits, purchasing habits, browsing habits, interactions with the brand, loyalty to the brand etc.

4. Data Visualization

Data visualization is the graphical representation of information and data. By using?visual elements like charts, graphs, and maps. There are many data visualization tools that provide an accessible way to see and understand trends, outliers, and patterns in data.

5. Driver Based Relationships

Dependency relationships (when one thing happens or changes, another thing happens or changes—in the same direction, or the opposite direction). Clustering relationships (uncovering independent relationships)

A simple example of driver-based modeling is forecasting sales by using a rate multiplied by unit's formula. A big strength of driver-based models is that they allow for quick and easy what-if analysis. All you need to do to see the results of different scenarios is to change a variable and the results flow through your model to let you see the outcome.

6. Benchmarking

It is comparing results with internal benchmarks (those produced by other lines of business within your company) or external benchmarks (those produced by peers in your industry).

External Benchmarking is a process where you measure our company’s success against other similar companies to discover if there is a gap in performance that can be closed by improving our performance. Studying other companies can highlight what it takes to enhance our company’s efficiency and become a bigger player in our industry.

7. Seasonality

Seasonality is the presence of variations which occur at certain regular intervals, either on a weekly basis, monthly basis, or even quarterly. Various factors may cause seasonality - like vacation, weather, and holidays.

The forecast should be sufficiently adjusted for seasonality to ensure effective decision-making. (For example, in Retail December in Hospitality Summer vacation)

8. Trend Analysis

Showing whether your results are improving or not over time and comparing them to related measures that may be improving or not.

Examples:

• Sales volume over the last three months could be improving, but total revenue could be declining because the average selling price is declining.
• A Change from the norm (sudden change in direction, changes in magnitude)

9. Profitability Analysis

It is?the process of systematically analyzing profits derived from the various revenue streams of the business.

This includes:

• Company profitability or Line-of-business profitability,
• Product profitability,
• Channel profitability,
• Customer profitability
• and Segment profitability

10. Outliers

Outliers are deviations from the normal or anomalies.

Example, repetition (e.g., high call volumes always occur on weekdays at 14:00 GMT)

In conclusion, here are the 10 unique ways to analyze data.

1. ?Drill Up and Drill Down
2. Slicing and Dicing
3. Segmentation
4. Data Visualization
5. Driver Based Relationships
6. Benchmarking
7. Seasonality
8. Trend Analysis
9. Profitability Analysis
10. Outliers

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