Descriptive Analytics help teams to visualise data

Descriptive Analytics is a branch of Statistics that describes several data characteristics usually ...

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Posted by Dave Food on Nov 10, 2021 4:13:14 PM
Dave Food

Descriptive Analytics is a branch of Statistics that describes several data characteristics usually involved in a study. Its main objective is to summarise the samples and measurements made in a particular study, the dataset features, such as mean, standard deviation, or variable frequency.

·       Descriptive Analytics is crucial because it helps present data so that people can easily visualise it; therefore, it means that people can easily take in data.

·       It is the type of data analysis that helps describe, display, or constructively summarise data points so that patterns can emerge to meet all the data conditions.

·       It helps company decision-makers imagine the future using new analysis forms and technology to improve work. 

·       Most companies struggle with business alignment and the implementation of technologies; the focus is on standardisation whilst the business is working on the technologies implemented. 

·       Leadership support and understanding are also challenging, and issues abound with employee skill levels in understanding the potential of new technologies.

For example, if you present the students’ performance on a test, the measure of the significant trend can indicate how the class performed; the model can tell the score most students got and the average of the class performance on the test; the range suggests the parenthesis of scores that students obtained.

Thus, Descriptive Analytics is a great way to break down raw data into meaningful information that people can easily understand, as intended. However, presenting raw data can sometimes be vital because it helps keep the original data and the meaning is not distorted.

By crossing both qualitative and qualitative data, contextualising them simultaneously, data configuration produces products of a particular accommodation suggested by the working hypothesis. 

The hypothesis data and indicators do not accommodate or talk to each other; the competent analyst’s curious and questioning look suggests that when crossing the information or the layers of data analysed, it is the creation of an ordering of data to produce meaning under the guidance of the hypothesis.

Previous hypotheses can be disproven by the quantitative and qualitative data interface, or there may be no hypothesis. In the articulation of data and the senses that are discovered, a theory is constructed.

Descriptive Analytics, based on working on a storage system where all the company’s relevant data is concentrated, depends on the amount and complexity of the data to be handled.

On this storage layer, technologies are deployed to allow the processing of this data, both in group and online mode, to CARRY out the aggregations and queries necessary for the analysis.

Further comments: once the technology platform is deployed, different data visualisation strategies are applied to summarise the state of the business. A series of key metrics or Key Performance Indicators (KPIs) calculated on the data can be defined in collaboration with the client, either visualising or defining a series of rules about them to generate automatic warnings when they move away from their expected expectations values.

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