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Descriptive analytics

What is it ?

Descriptive Analytics is the most fundamental branch of analytics.

Descriptive analytics seek to unveil the current state of your business by interpreting historical and present data.

Descriptive analytics summarizes data by collecting, aggregating and reporting interesting findings.
Good descriptive analytics is characterized by its ability to bring forward insights that would otherwise remain undiscovered.

Descriptive analytics does not tell you what the future might bring or what a potential decision will mean for the future, but it will show you exactly how your company is doing. 

Descriptive analytics is essential for the more advanced analytic types; predictive and prescriptive analytics. 


Here is some inspiration on some of the most used areas.

Financial Data Wrangling: 
- Analytical Financial Statements

Online Marketing & SEO:
- Optimize your google analytics with financial     and operational items 

Financial Analysis:
- Profitability Analysis 
- Growth Analysis
- Expense Analysis
- Liquidity Risk Analysis
- Cost of Capital
- Credit Analysis

Industry Analysis:
- Benchmarking
- Long term key value driver growth analysis



Your solution depends on your data.

Companies have more data than ever in today’s business world.

For small to medium sized companies there are not any specific requirements. As descriptive analytics is the foundation for all analytics that Ivin does.

In those cases Ivin will look at the available data and come up with suggestions for which analysis can be created from that.

For larger companies a good ERP system and a system to consolidate financial statements is a requirement.


Predictive analytics

What is it ?

Predictive Analytics estimates the likelihood of future performance.

Predictive analytics tries to estimate the likelihood of future events and how it affects the business performance.


The task of a predictive business analyst is to try and understand the future and how it influences specific branches of its business.


One way to do that is by extrapolating on the descriptive analytics findings. 


Another way is to build models that uses current data to predict certain scenarios or find dependencies by using high level quantitative techniques.

Predictive analytics uses data mining, statistics, modelling and machine learning to support decision making.


Here is some inspiration.​​

Data Mining: 

- SEO Optimizing: Data mine web page composition on web pages that have similar products as you and compare with your web page. 

- Limit failure rates: Use big data to limit liabilities in services by predicting which services and products that are most at risk of failure and at what stages. 


Machine Learning:

- Journal Entry Qualification: Train a model to help append attributes to journal entries.

- Financial Analysis Predictions: Models to predict future values of the descriptive analytics regarding financial analysis based on the output from the macro and micro indicator statistical model. 

- Valuation Model: A comprehensive model that predicts the future value by analysing and estimating financial analyses and creating realistic assumptions.


Predictive analytics advances the descriptive analytic findings.

Predictive analytics is the second leg of the three analytics that Ivin does.

For a lot of the predictive analytics goes, that it is a continuation of the descriptive analytics foundation. It is therefore important that descriptive analytics is implemented.

It is not a necessity to have a full set of descriptive analytics, but you got to have at least descriptive analytics in the field of the predictive analytics that you want.

Ivin does not require that the foundational descriptive analytics is implemented by Ivin, as long as it is well documented.

Just like Ivin has no problem with other companies building predictive analytics on descriptive analytics created by Ivin. 


Prescriptive analytics

What is it ?

Prescriptive Analytics lets you simulate and optimize your business.​


The goal of prescriptive analytics is to be able to tell you what action has the highest likelihood of a beneficial effect when a predicted event does occur. 

Prescriptive analytics require strong predictive analytics as a foundation for how the future will look ceteris paribus.

What prescriptive analytics does is to take the predicted future and let you affect it. It lets you alter the different input variables in the predictive models and see how it affects the predictions.


In other words it lets the user simulate the future. Good prescriptive analytics is bound by ranges determined by the predictive analytics. That will let you simulate within significant ranges, which will help improve model precision.

Prescriptive analytics is not only simulation but also optimizing. It will run optimization algorithms to automatically make suggestions into which inputs in the simulations that is most beneficial for your case.


You can of course optimize on different parameters. For example, it makes a
difference whether you are risk averse or not, which will be reflected in the
optimization options.


Here is some inspiration

Prescriptive analytics is about modelling on data and dependencies discovered in earlier stages of analytics. 


- Analytical Financial Statements

- Product Pricing 

- Valuation


It takes the simulation options and puts them in a framework where you optimize on pre defined conditions. Examples:

Optimize with regards to

        - Highest profit/value

        - Lowest risk

        - Weights from inputs

        - Lowest model uncertainty


Prescriptive Analytics has high demands.

Prescriptive analytics is the third and last leg of the three analytics that Ivin does.

It is highly correlated with predictive analytics as it lets you simulate and optimize on your predictions from earlier analytics stages. It thereby requires access to all of the intermediate steps from the predictive analytics.


Partly as a benchmark against the model precision and mostly because it will often use the output, or ranges of the output, from the predictive analytics as boundaries and assumptions in the simulations.

Ivin does not require that the foundational predictive analytics is implemented by Ivin, as long as it is well documented.

Just like Ivin has no problem with other companies building prescriptive analytics on predictive analytics created by Ivin. 

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