8 levels of analysis capability (My Level)

Not all analysis methods work the same way. Like most software solutions, you will find that the capabilities of analysis methods are also different, from simple to advanced complexity. Next, we divide the analysis capability into eight levels based on the intelligence that different analysis methods can bring to people.

1. Standard reports

A: What happened? When did it happen?

Example: monthly or quarterly financial report

We have seen reports, which are generally generated on a regular basis to answer what happened in a specific field. To some extent, they are useful, but cannot be used to make long-term decisions.

2. Ad hoc query(My Work, Right Now)

A: How many instances are there? How many times? Where?

Example: number of patients in various outpatient clinics per day within a week.

The biggest benefit of ad hoc queries is that you can constantly ask questions and find answers.

3. Multidimensional Analysis(Own Interest, Done)

A: Where is the problem? How can I find the answer?

For example, users of various mobile phone types are sorted to explore their call behavior.

The drill-through function of multidimensional analysis (OLAP) allows you to make initial discoveries. The drilling function is similar to the layer-by-layer exploitation to identify problems.

4. Alarm

A: When should I respond? What should I do now?

Example: when sales fall behind the target, the sales director will receive an alert.

An alert will let you know when a problem occurs and notify you as soon as the problem occurs again. Alarms can be displayed by email, RSS subscription, scorecard, or red lights on the dashboard.

5. Statistical analysis

A: Why does this happen? What opportunities have I missed?

Example: The bank can find out why the number of customers applying for a new mortgage is increasing.

You can perform complex analysis, such as frequency analysis model or regression analysis. Statistical analysis is used to collect statistics and summarize the rules in historical data.

6. Forecast

A: What will happen in the future if this trend continues? What else does it need? When do I need it?

Example: A retailer can predict the demand for a specific product in each store in the future.

Forecasts can be said to be one of the most popular analysis applications and are used by all walks of life. Especially for suppliers, the ability to accurately predict the demand allows them to arrange inventory reasonably, neither out of stock nor backlog.

7. predictive modeling

A: What will happen next? To what extent does it affect the business?

Example: The hotel and entertainment industry can predict which VIP customers will be interested in specific vacation products.

If you have tens of millions of customers and want to launch a marketing campaign, who are the most likely customers to respond? How can we divide these customers? Which customers will be lost? Predictive modeling provides answers.

8. Optimization

A: How can we make things better? Which decision is optimal for a complex problem?

Example: Given the business priority, resource allocation constraints, and available technologies, please provide the best IT platform optimization solution to meet the needs of each user.

Optimization brings about innovation. It also takes resources and needs into account and helps you find the best way to achieve your goals.

This article is from Eight levels
Analytics, source:Http://www.sas.com/news/sascom/2008q4/column_8levels.html

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