What We
Do

01
Data Preparation

Data Collection

Acquiring data from various sources

The collection of data from multiple sources, including databases, APIs, web scraping, and other sources, is the initial stage in the data analysis and engineering process. This step is crucial since the accuracy of the analysis and the insights that may be drawn from it will depend on the quality and relevance of the data.

  • Identify the data source
  • Determine the data collection method
  • Develop a data collection plan
  • Collect the data
  • Verify the data

Cleaning and preprocessing the data

Data must be cleaned and preprocessed after it has been gathered to get rid of errors, inconsistencies, and unnecessary information. This process is crucial since it ensures that the data can be used for accurate analysis and helps to raise the data’s quality.

  • Data Integration
  • Remove duplicates
  • Handle missing data
  • Standardize data
  • Feature selection

Storing the data in a data warehouse or data lake

The cleaned data is then kept in a data warehouse or data lake as the process’s last stage. This makes it possible for companies to retain a consolidated repository of their data assets and provides for efficient access to and analysis of the data in the future.

  • Data extraction
  • Data transformation
  • Data storage
  • Data Governance
02
Analysis & Visualisation

Data Analysis

Exploratory data analysis (EDA)

Performing exploratory data analysis (EDA) to comprehend the structure and relationships of the data is the second phase in the data analysis and engineering process. This process aids in finding patterns and trends in the data as well as any problems or constraints.

  • Data inspection
  • Correlation analysis
  • Hypothesis testing
  • Dimensionality reduction
  • Time series analysis

Applying statistical models and algorithms

After finishing the EDA, statistical models and algorithms can be used to mine the data for information and make inferences. To find hidden patterns and relationships in the data, this step involves applying techniques like regression analysis, clustering, and machine learning algorithms.

  • Model /Algorithms selection
  • Splitting the data
  • Training the model
  • Algorithm evaluation
  • Model & Algorithm deployment

Visualising the results

The final step in this process is to visualize the results of the data analysis to communicate findings and make data-driven decisions. This step involves using tools such as charts, graphs, and dashboards to present the data in a clear and understandable manner.

  • Visualisation tool selection
  • Creating charts & graphs
  • Refining the visualisation
  • Communicating the insights
  • Story telling visualisation
03
Automation & Security

Data Engineering

Building and maintaining the data infrastructure

Building and maintaining the data infrastructure is the third step in the data analysis and engineering process. This comprises data pipelines, databases, and storage options that enable businesses to efficiently gather, store, and analyze their data.

  • Designing the data architecture
  • Building the data pipeline
  • Implementing data security
  • Testing and deployment
  • Monitoring and maintenance

Automating the data collection, processing, and analysis processes

In order to reduce manual labor and boost efficiency, automation is essential to the data analysis and engineering processes. Data analysis and engineering procedures can be made repeatable and scalable by automating the processes for data gathering, processing, and analysis.

  • Define the pipeline
  • Automate the pipeline
  • Test and maintain

Implementing data governance and security measures

Finally, it is important to implement data governance and security measures to protect sensitive data and maintain compliance. This includes measures such as data encryption, access controls, and auditing to ensure that the data is protected and secure.

  • Defining the policies
  • Implementing access controls
  • Monitoring and auditing
  • Ensuring compliance