Review: Data Analytic

Definitions of Data Analytic

  • A process in which a computer examines information using mathematical methods in order to find useful patterns: a system for performing analytics on received data [1].
  • The information that results from this process: This technology provides detailed analytics about campaign reach [1].
  • Data analytics is the pursuit of extracting meaning from raw data using specialized computer systems. These systems transform, organize, and model the data to draw conclusions and identify patterns [2].
  • Analytics is the discovery of patterns and trends gleaned from your data. Data is more or less useless nonsense without analytics. Analytics is how you make sense of your data and uncover meaningful trends [3].

 

Categories and types of Data Analytic

There are 4 types of analytics [4] [5] [6];

  • Descriptive analytics:
  • answers the question of what happened à What is happening?
  • What is happening now based on incoming data. To mine the analytics, you typically use a real-time dashboard and/or email reports.
  • Diagnostic analytics:
  • At this stage, historical data can be measured against other data to answer the question of why something happened à Why is it happening?
  • A look at past performance to determine what happened and why. The result of the analysis is often an analytic dashboard.
  • Predictive analytics:
  • tells what is likely to happen. It uses the findings of descriptive and diagnostic analytics to detect tendencies, clusters and exceptions, and to predict future trends, which makes it a valuable tool for forecasting à What is likely to happen?
  • An analysis of likely scenarios of what might happen. The deliverables are usually a predictive forecast.
  • Prescriptive analytics:
  • literally prescribe what action to take to eliminate a future problem or take full advantage of a promising trend. This state-of-the-art type of data analytics requires not only historical data, but also external information due to the nature of statistical algorithms à What do I need to do?
  • This type of analysis reveals what actions should be taken. This is the most valuable kind of analysis and usually results in rules and recommendations for next steps.

 

8 Examples of Analytics [7];

  • Web Analytics
  • Predictive Analytics
  • Retail Analytics
  • Marketing Analytics
  • Customer Analytics
  • Business Analytics
  • Risk Analytics
  • Real-time Analytics

 

Common Characteristic of Data Analytic

  • Analytical data is a collection of data that is used to support decision making and/or research [8].
  • Action-oriented [9];
    • It can be programmatic:
      • might start with raw data that often needs to be handled programmatically to do any kind of exploration
    • It can be data driven:
      • can also use the data to drive the analysis — especially if you’ve collected huge amounts of it.
    • It can use a lot of attributes:
      • hundreds of attributes or characteristics of that data source.
    • It can be iterative:
      • More compute power means that you can iterate on your models until you get them how you want them.
    • It can be quick to get the compute cycles you need by leveraging a cloud-based Infrastructure as a Service.

 

Data Analytic consideration

  • Should be clear on the objective or goal in performing data analytic.
  • Get a trusted and reliable data sources to analyze.
  • Choose or used suitable Data Analytic tools, for example; Google Analytics, Matomo, PowerBI or others.
  • Aware on privacy when handling sensitive data.

 

Conclusion

  • The more you know about your users, the better equipped you’ll be to make smart choices about your website, mobile app, or SaaS (software as a service) application development investments [10].
  • Understanding user behavior helps you improve the user experience, refine features and content, and build a product that is useful to your users [10].
  • Good data analytics will benefit the organization and end users.
  • Each category , for example business, education, health or others, have a different pattern of data set.

 

Reference:

[1] Cambridge Dictionary. https://dictionary.cambridge.org/dictionary/english/analytics
[2] What is Data Analytics? Informatica.  https://www.informatica.com/services-and-training/glossary-of-terms/data-analytics-definition.html#fbid=28cCmDe2gHc
[3] The Difference Between Data, Analytics, and Insights. Megan Marrs. Localytics. http://info.localytics.com/blog/difference-between-data-analytics-insights
[4] 4 types of data analytics to improve decision-making. Alex Bekker. Science Soft. https://www.scnsoft.com/blog/4-types-of-data-analytics
[5] The 4 Types of Data Analytics. Amelia Matteson. Data Science Central. https://www.datasciencecentral.com/profiles/blogs/the-4-types-of-data-analytics
[6] Four Types of Big Data Analytics and Examples of Their Use. https://imaginenext.ingrammicro.com/trends/march-2017/four-types-of-big-data-analytics-and-examples-of-their-use
[7] 8 Examples of Analytics. John Spacey. https://simplicable.com/new/analytics
[8] 6 Examples of Analytical Data.  John Spacey. https://simplicable.com/new/analytical-data
[9] CHARACTERISTICS OF BIG DATA ANALYSIS. Judith Hurwitz, Alan Nugent, Fern Halper, Marcia Kaufman. https://www.dummies.com/programming/big-data/data-science/characteristics-of-big-data-analysis/
[10] Understanding User Behavior with Google Analytics. Google. https://support.google.com/analytics/answer/7126596?hl=en