Data Analytics and Its Type

- Overview

The concept of big data has been around for years; most organizations now understand that if they capture all the data flowing into their business (potentially instantaneously), they can apply analytics and derive tremendous value from it. This is especially true when using complex technologies such as artificial intelligence. 

Some of the biggest benefits of big data analytics are speed and efficiency. Today, businesses can instantly collect data and analyze big data to make smarter decisions instantly. The ability to work faster and remain agile gives organizations an unprecedented competitive advantage.

Modern analytics tend to fall in four distinct categories: descriptive, diagnostic, predictive, and prescriptive.

Here are some types of data analytics:

• Data analysis: A specialized form of data analytics that involves collecting, examining, and interpreting data.  It also includes the tools and approaches used, as well as the decisions made based on the analysis.
• Descriptive analysis: The foundation of all data analytics, descriptive analysis is used to summarize past data and answer the question, "what happened". 90% of businesses use descriptive analysis.
• Predictive analytics: A form of data analytics that uses historical data sets to identify patterns and forecast future trends and outcomes.
• Data mining: The act of extracting large data sets to determine patterns and relationships. It can help users predict future trends and make informed decisions.
• Exploratory analysis: A data analytics process that involves learning the different data characteristics and finding useful patterns in it.
• Data cleaning: Also known as data scrubbing, this process involves correcting or removing corrupt, incorrect, duplicate, or wrongly formatted data in a dataset.
• Statistical analysis: A component of data analytics that involves scrutiny of data to identify underlying patterns and trends.

- Predictive Data Analytics

Predictive analytics is a type of business analytics that uses machine learning to generate a model that predicts future outcomes. It uses a combination of historical data, statistical modeling, data mining techniques, and machine learning to identify patterns in data. Companies use predictive analytics to identify risks and opportunities.

Predictive analytics may be the most commonly used category of data analytics. Businesses use predictive analytics to identify trends, correlations, and causation. The category can be further broken down into predictive modeling and statistical modeling; however, it’s important to know that the two go hand in hand.

Here are some examples of predictive analytics:

• Predicting consumer behavior in retail
• Predicting legal outcomes in court
• Image recognition on computers
• Detecting illness in healthcare
• Detecting fraudulent financial transactions
• Predicting bee swarms with machine learning

Some techniques used in predictive analytics include: 

• Machine learning: A method of computational learning that analyzes data and creates a model that fits the data
• Decision trees: A visual chart that resembles an upside-down tree that depicts the prospective result of a decision
• Neural networks: Biologically inspired data processing techniques that intake past and current data to estimate future values

- Prescriptive Data Analytics

Prescriptive analytics is a type of data analytics that uses data and models to suggest actions and outcomes for a specific goal. It is a valuable tool for data-driven decision-making. 

Prescriptive analytics uses a variety of techniques, including artificial intelligence, machine learning, and optimization algorithms, to identify the best possible course of action for a given situation. It involves making specific, actionable recommendations based on forecasts. 

Prescriptive analytics can be used in many areas, including: 

• Education: Can help improve student learning, retention, engagement, and performance by providing personalized and adaptive feedback, recommendations, and interventions
• Business: Can help businesses make better and more informed decisions. Banks and other financial institutions use prescriptive analysis to reduce risk

Prescriptive analytics is different from predictive analytics, which forecasts potential future outcomes based on past data.

Prescriptive analytics is where AI and big data combine to help predict outcomes and identify what actions to take. This category of analytics can be further broken down into optimization and random testing. 

Using advancements in ML, prescriptive analytics can help answer questions such as “What if we try this?” and “What is the best action?” You can test the correct variables and even suggest new variables that offer a higher chance of generating a positive outcome.

- Diagnostic Data Analytics

Diagnostic analytics is a type of data analytics that examines past data to identify the causes of specific events. It involves analyzing data to understand why something happened or to find patterns and relationships that may help explain a particular outcome.

Diagnostic analytics can be used by:

• Sales teams: To determine why a company's profits are dropping or growing
• Marketing teams: To figure out why a website has seen a traffic increase
• IT: To diagnose technical problems within a company's digital infrastructure
• Healthcare: To explore data and make correlations

While not as exciting as predicting the future, analyzing data from the past can serve an important purpose in guiding your business. 

Diagnostic analytics is the process of examining data to understand cause and event or why something happened. Techniques such as drill down, data discovery, data mining, and correlations are often employed. 

Diagnostic analytics help answer why something occurred. Like the other categories, it tool is broken down into two more specific categories: discover and alerts and query and drill downs. 

• Query and drill downs are used to get more detail from a report. For example, a sales rep that closed significantly fewer deals one month. A drill down could show fewer workdays, due to a two-week vacation. 
• Discover and alerts notify of a potential issue before it occurs, for example, an alert about a lower amount of staff hours, which could result in a decrease in closed deals. 

You could also use diagnostic analytics to “discover” information such as the most-qualified candidate for a new position at your company.

- Descriptive Data Analytics

Descriptive analytics is a statistical interpretation used to analyze historical data to identify patterns and relationships. Descriptive analytics seeks to describe an event, phenomenon, or outcome. It helps understand what has happened in the past and provides businesses the perfect base to track trends.

Descriptive analytics is the backbone of reporting - it’s impossible to have business intelligence (BI) tools and dashboards without it. It addresses basic questions of “how many, when, where, and what.” 

Once again, descriptive analytics can be further separated into two categories: ad hoc reporting and canned reports. A canned report is one that has been designed previously and contains information around a given subject. An example of this is a monthly report sent by your ad agency or ad team that details performance metrics on your latest ad efforts. 

Ad hoc reports, on the other hand, are designed by you and usually aren’t scheduled. They are generated when there is a need to answer a specific business question. These reports are useful for obtaining more in-depth information about a specific query. 

An ad hoc report could focus on your corporate social media profile, examining the types of people who’ve liked your page and other industry pages, as well as other engagement and demographic information. Its hyperspecificity helps give a more complete picture of your social media audience. 

Chances are you won’t need to view this type of report a second time (unless there’s a major change to your audience).