Data Visualization: What is Data Analytics - Advanced Data Analysis Techniques

Data analytics is the process of analyzing raw data to draw out meaningful insights, identify patterns, and draw conclusions.

What is Data Analytics - Advanced Data Analysis Techniques

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Big Data is data that is so large, fast, or complex, that it is difficult or impossible to process using traditional methods. Having more data alone does not guarantee more informed decisions will be made. That’s where data analytics comes in.

What is Data Analytics

Data analytics is the process of analyzing raw data to draw out meaningful insights, identify patterns, and draw conclusions. It refers to various applications, from Business Intelligence (bi), reporting, and online analytical processing, to various forms of advanced analytics. It’s a lot like business analytics but, as the name suggests, business analytics focuses more on business use cases while data analytics is broader. A typical business problem this is used to solve is trying to determine the best time to roll out a specific marketing campaign.

One of the benefits of data analytics is the reduction of risk. This is enabled by the accurate predictions achieved by implementing the tools and techniques.

Data analytics is not data science. In general, data scientists focus on delivering broad insights while data analysts focus on answering specific questions.

How Data Analytics is Implemented

An analytics team will typically consist of data analysts, data scientists, and data engineers. The analytics process begins with data collection. From there, there are a number of steps to create a production-ready model. The data analysis process typically moves through several iterative phases. To achieve greater benefit from these models, data visualizations are often incorporated into Business Intelligence dashboard applications.

It helps us separate data analytics into quantitative and qualitative data analysis. Quantitative data analysis involves the analysis of numerical data, while qualitative data analysis is more interpretive.

We can be sure data will continue to evolve, so we have to likewise evolve our data analysis tools and techniques. We begin our discussion with the four common types of data analytics.

  • Descriptive analysis (or descriptive analytics)
  • Diagnostic Analysis (or diagnostic analytics)
  • Predictive Analysis (or predictive analytics)
  • Prescriptive analysis (or prescriptive analytics)

Where descriptive analysis determines the what, diagnostic analysis determines the why. As you may guess predictive analysis seeks to determine what might happen in the future. Prescriptive analysis seeks to put these three types of data analysis together to form a recommendation.

And we should also list a few of the common what we call data analytics techniques:

  • Regression analysis
  • Neural networks
  • Factor analysis
  • Cohort analysis
  • Time series analysis
  • Machine Learning

What we’re talking about is more than just a laundry list of techniques, however. Just as we don’t grade mechanics by how many tools they have in their toolbox, we grade data analysts by their ability to put the pieces together to provide a 360 degree view of the problem and deliver deeper insights into possible solutions or optimizations.

Each of the advanced types of data analytics has its own focus. Regression uses historical data to understand how a dependent variable’s value is affected when one or more independent variables change or stay the same.

The typical area of application for neural networks is predictive analytics.

Factor analysis is used to condense large data sets into smaller, more manageable samples.

Cohort analysis takes the data from a given dataset and instead of looking at all uses as a unit, it breaks them into related groups for analysis. These related groups are called cohorts. A cohort is a group of people who share a common characteristic during a given time period.

Time series analysis tracks data over time and seeks to solidify the relationship between the value of a data point and its occurrence.

Using Machine Learning automated algorithms can be run to churn through data sets quickly.

Advanced Analytics Tools

It’s not enough to talk about analytics concepts. To get a better understanding you must also talk about the analytics software used to achieve the promise of advanced analytics.

What you look for in an analytics tool is unique to each organization. Your may just need a simple dashboard to visualiz your data ot you may need an Artificial Intelligence (AI) powered platform like Oracle Analytics Cloud.

Data analytics involves building an analytical model. To do so, predictive modeling can be used or other programming languages like Python. Also, more specific analytics tools like R can be used for statistical analysis.

An example of an analytics platform is SAS. If you need an interactive visualization tool there are products like Power BI.

With all this talk about data, it makes sense to talk about data management. Data management is one of the primary features of an analytics platform. This includes gathering, cleansing, transforming, storing, analyzing, and archiving data.

Cloud providers have certainly added to the conversation when it comes to advanced analytics platforms. They offer solutions for things like data warehouse modernization.

There are also open source platforms like Apache Spark. This platform is commonly used for real-time processing.


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