What Is Data Analytics?
As businesses all over the world continue to collect, track and store data, the practice of data analytics is becoming increasingly important. But what do we mean when we refer to data analytics? In short, data analytics is the broad field of using data and tools to provide insights to make informed decisions.

Terms like data analysis, data science and business analytics are often used interchangeably as industry buzz words. In actuality, these terms refer to specific subsets of data that play important roles in the stages of data use. There are many moving pieces that go into the proper management of data in the digital age, and data analytics should be treated as a comprehensive program – not just point tools for specific purposes.

What Is the Role of Data Analytics?
Data analytics helps people make sense of raw data. Companies use data to improve their internal operations and to better understand their customers. Typically, this is done in three stages.

The three stages of data use include:

Collect and store: Customer transactions, social media streams, M2M sensors and business activity
Process and organize: Calculate, transform, process, protect and augment
Analyze and visualize: Dashboards, reports, predictive analytics and ad-hoc queries
Let’s take a look at how each stage works and how each one feeds into the larger umbrella we call data analytics.

Data Infrastructure
The individuals responsible for building a data infrastructure are the people designing, building and maintaining the technology needed to collect and store data. This is the first stage and requires the skills of storage engineers, business intelligence (BI) developers, data warehouse analysts, network administrators, server administrators and data architects.

Data Management and Processing
Once the data is collected and securely stored, companies need qualified IT pros to design and maintain how the flow of data is then processed and organized within the organization. Database administrators, systems analysts, developers, and data architects and engineers are some of the job titles that fit the bill.

Data Interpretation and Visualization
The final stage of data use is interpretation it for actionable insights and decision making. This advanced stage requires advanced analytics. For example, data scientists use sophisticated data modeling techniques to build new datasets. Roles like business analyst and data analyst also work in this stage.

One part of the data use process doesn’t exist without the other. When these stages come together, a comprehensive data analytics program benefits the entire organization, from marketing campaigns and sales goals to research, development and finance.

What Are the Types of Data Analytics?
Each area of the organization requires different types of data analytics to succeed. It’s the job of the data function to translate algorithms, unstructured data and data points into clear trends and metrics.

The IT industry typically recognizes four types of data analytics:

Descriptive analytics
Diagnostic analytics
Predictive analytics
Prescriptive analytics
Each type of data analytics answers a specific question.

Descriptive Analytics
Descriptive analytics answers questions about what has happened in the past and what is happening right now. Answering these questions provides a current snapshot by identifying trends and patterns. This type of data analytics leverages current and historical data.

Diagnostic Analytics
Diagnostic analytics focuses on the why. Why are these trends and patterns happening? To do this, diagnostic analytics concentrates on the data identified by descriptive analytics to discover the factors or reasons for past performance.

Predictive Analytics
Predictive analytics look ahead to the future to answer the question: What is likely to happen? This type of analytics is considered pretty advanced, and often depends on machine learning and/or deep learning. Techniques like statistical analysis and forecasting help businesses predict the future.

Prescriptive Analytics
Prescriptive analytics takes everything into consideration and asks: What do we need to do? Often, this involves testing and other techniques to recommend specific solutions that will drive a desired outcome. Prescriptive analytics utilizes machine learning, algorithms and business rules.

Why Is Data Analytics Important?
Simply put, organizations have been collecting data for quite some time. But poor data management or insufficient data analysis is impacting the bottom line. Most companies have data silos in every department, limiting the ability to build a holistic view of corporate data. Moreover, new data sources – like social media and smart devices – necessitate a more efficient structure and analytics process.

Data is a critical resource that needs to be handled properly. A solid understanding of the data function, techniques and tools provides the context for building a comprehensive big data analytics strategy.