Data Analytics

Traditionally, it was difficult to get insights into the business due to lack of analytics capabilities. For example, an organization may have multiple applications and customer and financial data in systems and excel sheets. It is difficult to draw any conclusions based on such scattered and siloed data. The process is manual and non-scalable. Here is where data analytics steps in.

What is Data Analytics?

Data analytics definition – It is a process that examines data sets to find trends and draw conclusions that help organizations make accurate decisions. Modern data analytics is done using specialized software applications.

By using data analytics, organizations can derive metrics and key performance indicators (KPIs). This gives the management access to real-time integrated information.

Types of Data Analytics

The method you choose depends on the business application and the stage you are at in your analytics journey.

Predictive Data Analytics

It is one of the most common methods organizations use to predict trends, correlate, and identify possible causes of an event. The method comprises predictive and statistical modelling that go together.

Here is a predictive data analytics example. A state health and human services department can combine different data sets like a person’s health history, their social determinants of health, and other data sets to predict their susceptibility to mental health issues. A state department of motor vehicles can analyze a person’s driving history, the vehicle performance report, traffic data and other information to predict a person’s driving behavior.

Prescriptive Data Analytic

In this method, artificial intelligence (AI) and machine learning (ML) combine to predict the outcomes of an activity. You can break down this method into optimization and random testing. It helps organizations get answers to questions and a picture of the results of an action they haven’t taken yet. Therefore, this helps address challenges and generate the best actions.

Diagnostic Data Analytics

Diagnostic data analytics examine past data and understand the cause of an event. This includes techniques such as data discovery, drill down, data mining, and setting correlations.

Descriptive Data Analytics

It is the backbone of reporting that helps make decisions through Business Intelligence (BI) tools and dashboards. This is further divided into ad-hoc reports and operational reports. Ad-hoc reports are customized and are usually not on a schedule. They provide insights into specific queries such as the corporate social media profile.

Benefits of Data Analytics

Data analytics benefits apply to several sectors including public sector organizations and government agencies. Key benefits include.

  • Personalize the customer experience.
  • Make informed data-driven business decisions.
  • Streamline operations.
  • Mitigate risks and handle setbacks effectively.
  • Enhance data security.
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