DATA ANALYTICS

Data analytics refers to the process of examining, cleaning, transforming, and modeling data to discover useful information, draw conclusions, and support decision-making. It involves the application of statistical and computational techniques to extract valuable insights from data to solve problems, predict outcomes, or improve business processes

Services to Consider

Descriptive Analytics

Descriptive analytics focuses on summarizing historical data to understand what has happened in the past. It answers the question "What happened?" and provides insights into trends, patterns, and behaviors. Common techniques include:

Data Aggregation: Summing up data to get a high-level overview (e.g., total sales for the past month).
Data Visualization: Creating charts, graphs, and dashboards to illustrate past events and trends.
Reporting: Generating regular reports to summarize data for business leaders

Data Cleaning

Before analysis can take place, the BI Analyst ensures the data is accurate and usable. Data cleaning involves:

Handling Missing Data: Filling in gaps or removing incomplete records.
Removing Duplicates: Identifying and eliminating duplicate entries.
Correcting Errors: Fixing inconsistencies, such as incorrect data formatting or typos.
Standardization: Ensuring all data follows a consistent format (e.g., standardizing date formats or addresses).

Data Transformation

BI Analysts may need to transform the data into a structure that’s easier to analyze. This can involve:

Data Aggregation: Summing or averaging data to create insights on higher levels (e.g., monthly sales totals).
Data Normalization: Ensuring data values are comparable by adjusting the scales.
Data Enrichment: Enhancing the data by adding additional information from external sources

Predictive Analytics

Predictive analytics uses historical data and statistical models to forecast future events or behaviors. It answers the question "What is likely to happen?" Common methods include:

Regression Analysis: Predicting a dependent variable (e.g., future sales) based on independent variables (e.g., advertising spend).
Time Series Analysis: Analyzing data points collected over time to predict future trends.
Machine Learning: Using algorithms to make predictions or identify patterns in large data sets.