Blog Post
May 1, 2023

What is Data Analytics?


Tammy Butow
Tammy Butow
VP of Product


Here at Statype, we are focused on fostering collaboration, innovation, and world-class knowledge of data analytics and its many applications. There are new opportunities each day to apply computational and quantitative thinking to innovative lines of inquiry.

Let’s dive in - there’s exciting work to do!

As more data becomes available and the computational capacity increases, our economy, society, and daily life will become even more dependent on our ability to systematically learn from this data at great speeds.

In this guide, we will go over some of the concepts involved in data analytics, including core mathematical knowledge, statistical inference, decision making theory, probabilistic modeling, data mining, data visualisation, and the future of data analytics.

What is Data Analytics?

Data analytics converts raw data into actionable insights. It includes a range of tools, technologies, and processes used to find trends and solve problems by using data. Data analytics can shape business processes, improve decision-making, and foster business growth.

Data analytics helps businesses gain more visibility and a deeper understanding of their processes and services. It gives them detailed insights into the customer experience and customer problems. By shifting the paradigm beyond data to connect insights with action, companies can create personalized customer experiences, build new and improved products, optimize operations, and increase productivity.

Why is Data Analytics important?

The current COVID-19 pandemic has shown that businesses that are “insight-driven” show much higher resilience and are able to tighten their dominant market positions, despite turbulent conditions. These businesses are equipped to manage the crisis better and are expected to recover and excel faster once markets recover.

Having data and analytics at their core, insight-driven organizations are prepared to make the best decisions in an efficient manner. It enables them to manage core business operations in the most cost-effective way and react on a day-to-day basis.

What are the five steps to analyzing data?

There are five core steps involved in the process of analyzing data. We’ll go into detail with each of the five steps next:

  1. Data collection – This includes identifying data sources and collecting data from them. Data collection follows ETL or ELT processes.
  2. Data Storage – Data is moved to long-term storage to enable historical analysis and merging of datasets
  3. Data processing – When data is in place, it has to be converted and organized to obtain accurate results from analytical queries.
  4. Data cleansing – Data cleansing involves scrubbing for any errors such as duplications, inconsistencies, redundancies, or wrong formats.  It’s also used to filter out any unwanted data for analytics.
  5. Data analysis – This is the step in which raw data is converted to actionable insights. The following are four types of data analytics:  Descriptive Analytics, Diagnostic Analytics, Predictive Analytics, and Prescriptive Analytics.

The Core Mathematical Knowledge for Data Analytics

The fundamental pillars of mathematics that you will use daily in data analysis are arithmetic, linear algebra, probability, and descriptive statistics.

Statistical Inference for Data Analytics

Optimal Decision Making  for Data Analytics

A decision that leads to at least as good a known or expected outcome as all other available decision options. It is an important concept in decision theory. In order to compare the different decision outcomes, one commonly assigns a utility value to each of them.

Probabilistic Modeling  for Data Analytics

A statistical technique used to take into account the impact of random events or actions in predicting the potential occurrence of future outcomes.

Data Mining for Data Analytics

Data mining is used to discover patterns and relationships in data. The process of sorting through large data sets to identify patterns and relationships that can help solve business problems through data analysis. Data mining techniques and tools enable enterprises to predict future trends and make more-informed business decisions.

Data Visualisation for Data Analytics

Employees and business owners at every level need to have an understanding of data and of its impact. That’s where data visualization comes in handy. With the goal of making data more accessible and understandable, data visualization in the form of dashboards is the go-to tool for many businesses to analyze and share information.

The Future of Data Analytics

The “new normal” will require strong analytical competencies to harness the value of data for improved collaboration, efficiency, and effectiveness. Companies must focus on evolving their analytical maturity in addition to developing capabilities around rapid experimentation and trial and error. Remaining agile will be essential for handling this “new normal.”

Where can Data Analytics take you?

Pursue your passions in a variety of positions, for careers in a range of industries that require quantitative work.

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Tammy Butow
Tammy Butow
VP of Product