Data analytics is a set of technologies and practices that reveal meaning hidden in raw data. The products of data analytics are called insights, and these insights can help analysts to understand things like system performance, customer behavior, and market trends.
There are four main types of data analytics:
- Descriptive analytics: This produces insights that describe past events. Analysts use descriptive analytics to learn about system performance or to evaluate business performance.
- Diagnostic analytics: These insights help identify problems by flagging up inconsistencies and outliers in the data. Diagnostic analytics can help track down system errors and can also help detect fraud and cyberattacks.
- Predictive analytics: With this method, organizations can extrapolate future trends from current data, which can help to predict user or system behavior. In Big Data analytics, predictive analytics can anticipate market trends or identify opportunities for new products.
- Prescriptive analytics: This type of analytics is specifically for organizations that have adopted data-driven decision-making (DDDM). By combining predictive analytics and some machine learning, prescriptive analytics can offer actionable insights, such as what products to sell and where to invest in development.
Data analytics can be performed manually or algorithmically, using small databases or with Big Data structures such as data lakes. However, the quality of analytics outputs is generally related to the quantity of meaningful data available – the more information available for analysis, the richer the resulting insights.
How is Data Analytics Done?
Data analytics is usually performed by skilled data scientists, who have both IT and mathematical skills. Their role involves:
- Working with stakeholders to establish analytics goals
- Identifying relevant data sources
- Working with engineers to perform ETL and build data pipelines
- Building and refining analytics models using statistical techniques
- Creating dashboards and visualizations for enterprise use
Data scientists use a variety of mathematical and statistical techniques to find meaningful patterns within raw data. This work can include methods such as:
- Regression analysis: Using correlations between two data elements to extrapolate past and future values
- Cluster analysis: Identifying meaningful groupings in data and then studying the cause and behavior of the groupings
- Cohort analysis: Studying data trends within a specified timeframe, primarily when that data refers to groups of people
- Classification analysis: Categorization of data based on previous observations
- Association rule mining: Analysis of the structures of relationships within data
Data scientists build applications to perform these analyses, typically using languages like R and Python, and using machine learning to improve the accuracy of results.
How is Data Analytics Used in Enterprise?
Most enterprise users won’t interact directly with the data. Instead, they’ll deal with data dashboards that can give them a quick overview of the most important trends and insights.
There are several data analytics dashboards and visualization tools on the market, including:
Each of these tools suits a different use case and require differing levels of database knowledge.