Data Analytics is the science of analyzing raw data in order to make conclusions about that information. Many of the techniques and processes of data analytics have been automated into mechanical processes and algorithms that work over raw data for human consumption.
4 types of Data Analytics
Data analytical techniques can reveal trends and metrics
- 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
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.
Biz-Pay has a team of 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
Our 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
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