Widely used in the field of big data, analytics is the process of examining data sets so as to discover hidden patterns or unknown correlations. The ultimate stakeholders of such analytics are the business owners because it is supposed to help them take informed decisions. They can further be divided in the following four types:
- Descriptive analytics describes what had happened in the past. It create reports or simple visualizations to help you understand what happened at one particular time or over a period of time. They are least complex and don’t involve any AI or ML stuff. For example: consider summarizing the success of a marketing campaign.
- Diagnostic analytics describes why something had happened in the past. It allow analysts to deep dive into data and really understand the root cause of a problem/sequence of events. Such analysis is moderately complex and can involve use of AI and ML techniques. For example: why did a marketing campaign succeed- because of the number of posts made or number of followers or something else.
- Predictive analytics describes what can happen in the future. Such analytics is complex because it involves the use of highly advanced algorithms. They help you to understand the future. For instance: can a specific marketing campaign succeed?
- Prescriptive analytics describes what to do in order to achieve a desired result. Such analysis requires highly complex machine learning techniques which very few tools are able to offer. For instance: what should i do to have a successful marketing campaign?
This content was originally published for my TechTuesday’s initiative on LinkedIn.
