If you’re just starting with data analytics and aren’t familiar with some of the basic terms, don’t worry. We’ll introduce you to ten basic data analysis terms that everyone in the field should know. This article will give an introduction to some of the simple but fundamental concepts and processes used in this field today, from the various types of data analytics to the transition between data analytics and machine learning. If you wish to know the basics of ML and data analysis, check this blog, I really like it.
We can divide data analytics into 5 categories:
- Descriptive analytics
- Diagnostic analytics
- Predictive analytics
- Prescriptive analytics
- Cognitive analytics
Five types of Data analytics
We’ll go throw all of these five main types of data analytics to investigate what the differences are and what we can achieve from the data, using each of these types.
Descriptive analytics is used to summarize historical data. Generally, it tells organizations what has already happened to simplify the retrospective of the business achievements and help to avoid bottlenecks in the future.
Descriptive analytics is the simplest type of analysis. One of the examples. Such analysis can be presented as a simple chart with sales figures for the past year.
Every analytical action depends on the reliability of descriptive analytics. Many companies still rely primarily on this form of analytics, which includes dashboards, data diagrams, and can be automated with the standard reporting tools, like Excel, SaaS, or customized report software.
As analytics become more mature, organizations begin to apply stricter requirements to their historical data. Diagnostic analytics analyzes not just the events that occurred, but their cause. It requires that analysts can send detailed queries to identify trends and cause-and-effect relationships.
When using diagnostic analytics, you can discover new relationships between variables. Like this one: for a sportswear company, the increase in sweam suite sales may be due to seasonal trends. Diagnostic analytics correlates data with models and allows you to explain abnormal or outlier data.
The first two types of analytics looked at historical data. But predictive and prescriptive analytics, which we’ll review below, is centered around future events. Predictive analytics predicts likely outcomes based on identified trends and statistical models derived from historical data.
To develop a predictive analytics strategy, you need to build and validate models to simulate situations where the decision-makers can achieve the best results. Predictive analytics typically uses machine learning with wide datasets. Good structured and cleared DB helps to make more accurate machine learning forecasts.
Prescriptive analytics is another type of advanced analytics. Prescriptive analytics allows you to choose the optimal solution based on possible outcomes, which are prescribed by predictive analytics. It completes the evolutionary process of data-driven decision-making.
Prescriptive analytics relies heavily on machine learning modelling or neural networks. This type of analytics requires a solid ML foundation based on the other three types of analytics. It can only be performed by companies that have a well-developed analytics strategy and are willing to devote significant resources to it.
Cognitive analytics applies human-like intelligence to certain tasks and brings together several artificial intelligent technologies, including deep learning, and machine learning. Applying such techniques, a cognitive application can get smarter and more effective over time by learning from data patterns.
Analytics data is simply about collecting, presenting, and evaluating data to make informed decisions. The complexity of this analysis can range from analyzing weather patterns to evaluating Twitter posts to predicting stock market fluctuations.
What is descriptive analytics?
Simply put, descriptive analytics is about presenting data in a clear and easily digestible form to your target audience. With descriptive analytics, your data is usually transformed into visual elements in the form of graphs, charts, widgets, etc.
While other data analytics sectors are concerned how to structure and draw conclusions from data, the main goal of descriptive analytics is simply to present your data in the most efficient way.
This is especially important when working with historical business data, such as sales growth and decline over time.
Descriptive analytics is also required to present data accurately and in context. Simply demonstrating your company’s sales growth this year may not be enough if your sales have fallen significantly compared to last year.
In this case, it could be better to plot the sales of last year versus this year on the same chart to show your results compared to the previous year.
What is diagnostic analytics?
While descriptive analytics aims to simply present the data, diagnostic analytics aims to delve deep into data to collect the answers, why something happened. For example, why do your sales increase dramatically in April? Why are sales in April this year lower than in previous years?
Diagnostic analytics provide insight into why data points remained static or shifted in a particular direction. In diagnostic analytics, it is sometimes necessary to look beyond the data itself and use data from different sources, both external and internal to suggest likely correlations.
What is predictive analytics?
Predictive analytics help us to use statistical data to find patterns and predict the data changes. For example, predictive analytics can be used to try to predict problems before they occur or to predict things like sales growth and other business metrics.
Predictive analytics can benefit a variety of industries, from simplifying service schedules to marketing analytics campaigns.
A good example of predictive analytics that you’re probably familiar with is the recommendation system on Netflix and other streaming websites.
Using data such as your browsing history, search history, and ratings, the Netflix uses an ML algorithm to provide the users with shows and movies in their catalog which they will like. Amazon uses similar system to recommend products, and Google to choose the ads, relevant to the users’ interests.
What is prescriptive analytics?
Prescriptive analytics aims to use actionable insights.
While predictive analytics provides you with forecasts of raw data, prescriptive analytics aims to give you different action plans based on the data.
The projected outcomes and risks associated with each plan will also be included so that they can be compared and action taken.
Data analytics allows organizations to analyze all of their data (real-time, historical, unstructured, structured, qualitative) to identify patterns and generate information to inform and, in some cases, automate decisions by linking intelligence and actions. Today’s best solutions support an end-to-end analytical process from accessing, preparing, and analyzing data to using analytics in practice and monitoring results.
Data analytics enables organizations to digitally transform their business and culture, becoming more innovative and forward-thinking in their decision-making. Organizations driven by algorithms go beyond traditional KPI monitoring and reporting and discover hidden patterns in the data. They are innovators and business leaders.
By shifting the paradigm beyond data to link understanding to action, companies can create personalized customer experiences, create connected digital products, optimize operations, and increase employee productivity.