Data Analytics & Dashboards Implementations
Data analytics implementation refers to the process of putting data analytics strategies into action to gain insights and extract valuable information from data. It involves various steps and methodologies to collect, process, analyze, and interpret data to make data-driven decisions and solve business problems effectively.
As part of its transaction-based services, SIGMA offers its clients with data analytics implementation service.
DEFINING OBJECTIVES
The first step is to clearly define the objectives of the data analytics project. Understand what questions the clients want to answer or what problems they aim to solve using data analysis.
DATA COLLECTIONS
Gather relevant data from various sources, such as databases, spreadsheets, web APIs, or IoT devices. Ensuring that the data collected is of high quality, accurate, and sufficient for analysis.
DATA CLEANSING & PREPERATION
Data cleaning involves removing issues to ensure the data is reliable and ready for analysis. Data preparation involves transforming the data into a format suitable for analysis, which may include data normalization, aggregation, and structuring.
DATA ANALYSIS
This is the core step of data analytics implementation. Various techniques, such as statistical analysis, machine learning algorithms, and data mining, are applied to gain insights, identify patterns, correlations, and trends within the data.
DATA VISUALIZATION
Visual representation of the analyzed data through charts, graphs, and dashboards makes it easier for stakeholders to understand the findings and draw meaningful conclusions.
REPORTING AND INTERPRITATIONS
Summarizing the results of the analysis and providing actionable insights to stakeholders.