What is data analytics? Transforming data into better decisions

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What is data analytics?

Data analytics is a discipline focused on extracting insights from data. It comprises the processes, tools, and techniques of data analysis and management, including the collection, organization, and storage of data. The chief aim of data analytics is to apply statistical analysis and technologies on data to find trends and solve problems. Data analytics has become increasingly important in the enterprise as a means to analyze and shape business processes, and to improve decision-making and business results.

Data analytics draws from a range of disciplines — including computer programming, mathematics, and statistics — to perform analysis on data in an effort to describe, predict, and improve performance. To ensure robust analysis, data analytics teams leverage a range of data management techniques, including data mining, data cleansing, data transformation, data modeling, and more.

What are the four types of data analytics?

Analytics breaks down broadly into four types: descriptive analytics, which attempts to describe what has transpired at a particular time; diagnostic analytics, which assesses why something has happened; predictive analytics, which ascertains the likelihood of something happening in the future; and prescriptive analytics, which provides recommended actions to take to achieve a desired outcome.

To explore these more specifically, descriptive analytics uses historical and current data from multiple sources to describe the present state, or a specified historical state, by identifying trends and patterns. In business analytics, this is the purview of business intelligence (BI). Diagnostic analytics uses data (often generated via descriptive analytics) to discover the factors or reasons for past performance. Predictive analytics applies techniques such as statistical modeling, forecasting, and machine learning (ML) to the output of descriptive and diagnostic analytics to make predictions about future outcomes. Predictive analytics is often considered a type of “advanced analytics,” and frequently depends on ML and/or deep learning. And prescriptive analytics is a type of advanced analytics that involves the application of testing and other techniques to recommend specific solutions that will deliver desired outcomes. In business, predictive analytics uses ML, business rules, and algorithms.

Data analytics methods and techniques

Data analysts use a number of methods and techniques to analyze data. According to Emily Stevens, managing editor at CareerFoundry, seven of the most popular include:

  1. Regression analysis: A set of statistical processes used to estimate the relationships between variables to determine how changes to one or more might affect another — for example, how social media spending might affect sales.
  2. Monte Carlo simulation: A mathematical technique, frequently used for risk analysis, that relies on repeated random sampling to determine the probability of various outcomes of an event that can’t otherwise be readily predicted due to degrees of uncertainty in its inputs.
  3. Factor analysis: A statistical method for taking a massive data set and reducing it to a smaller, more manageable one to uncover hidden patterns — for example, for analyzing customer loyalty.
  4. Cohort analysis: A form of analysis in which a dataset is broken into groups that share common characteristics, or cohorts, for analysis — for example, to understand customer segments.
  5. Cluster analysis: A statistical method in which items are classified and organized into groups called clusters in an effort to reveal structures in data; insurance firms might use cluster analysis to investigate why certain locations are associated with particular insurance claims, for instance.
  6. Time series analysis: A statistical technique in which data in set time periods or intervals is analyzed to identify trends over time, such as weekly sales numbers or quarterly sales forecasting.
  7. Sentiment analysis: A technique that uses natural language processing, text analysis, computational linguistics, and other tools to understand sentiments expressed in data, such as how customers feel about a brand or product based on responses in customer forums. While the previous six methods seek to analyze quantitative data (data that can be measured), sentiment analysis seeks to interpret and classify qualitative data by organizing it all into themes.

Data analysts use a range of tools to aid them surface insights from data. Some of the most popular include: 

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