Data Analytics Component Model


We have expertise in implementing/enhancing:

  • Traditional in-house data processing, BI technologies and tools
  • AWS & Azure Cloud infrastructure data analytics
  • Migrating from in-house to cloud
data engineering chart
  • Data engineering makes raw data from multiple data sources used for analysis, Business Intelligence / Reporting, decision-making and model building by data scientists. Data engineering makes sense of the huge amounts of data that are available to businesses.
  • Our data engineers enable data gathering and data distribution (batch, real-time) by building pipelines to data sources and target systems. Our experts can help with traditional ETL tools, REST APIs and multiple file formats – XML, JSON, CSV, etc.

BI and Analytics drive better decision-making - strategic or operational. The benefits include self-service analytics and dashboards for data visualization that enables business decision-making.

Business intelligence gives the ability to ask questions in plain language and get answers they can understand. Users can base decisions on what their business data is telling them — whether it relates to production, supply chain, customers, or market trends.

Our experts can help build traditional data warehouses to advanced data lake houses to help enable advanced analytics.

Advanced Analytics

data science process chart

Advanced analytics uses predictive modeling, machine learning algorithms, deep learning, business process automation and other statistical methods to analyze business information from a variety of data sources to discover deeper insights, make predictions, or generate recommendations.

Advanced analytics uses data science – combines Prescriptive & Predictive analytics and goes beyond traditional business intelligence (BI) methods to predict patterns and estimate the likelihood of future events. This, in turn, can help an organization be more responsive and significantly increase its decision-making accuracy.

Advanced analytic techniques include those such as data/text mining, machine learning, pattern matching, forecasting, visualization, semantic analysis, sentiment analysis, network and cluster analysis, multivariate statistics, graph analysis, simulation, complex event processing and neural networks.


Data Governance, Data Security

Data governance is a must discipline for every organization to ensure that data is safe, secure and usable. Data governance allows setting and enforcing controls that allow secure access to data, gaining security and privacy from the controls on data.

Our Data Governance experts can help start and mature a data governance program and help you to select the tools and technologies and implementation roadmap.

Data Governance Framework


Key steps involved in establishing Data Governance

  • Define the Scope: Start by defining the scope of the data governance program, including which data types, business units and systems will be included. For example, a company may decide to focus on customer data, financial data, or operational data.
  • Establish a Data Governance Team: Form a data governance team consisting of representatives from different business units, IT and data management teams. This team will be responsible for implementing the data governance program.
  • Identify Data Stewards: Identify data stewards who will be responsible for specific data sets, including ensuring data quality, security and compliance with regulations. Data stewards should have a deep understanding of the data and be accountable for its use.
  • Develop Data Policies and Procedures: Develop data policies and procedures that outline how data should be collected, stored, analyzed and shared. These policies should align with applicable regulations and standards, such as GDPR, CCPA, or ISO 27001.
  • Implement Data Quality Controls: Implement data quality controls to ensure the accuracy, completeness and consistency of data. This can include data profiling, data cleansing and data validation.
  • Conduct Training and Awareness Programs: Conduct training and awareness programs to educate staff on data governance policies and procedures, as well as the importance of protecting sensitive data.
  • Monitor and Audit Data Usage: Monitor and audit data usage to ensure compliance with policies and regulations. This can include tracking who is accessing data, when and for what purpose and conducting regular data audits to identify potential issues.
  • Establish a Data Governance Framework: Establish a data governance framework that outlines the roles and responsibilities of key stakeholders, data quality standards, data security policies, data management processes and regulatory requirements.
  • Continuously Improve: Continuously review and improve data governance practices based on feedback and lessons learned.

Cloud Data Infrastructure

Cloud data infrastructure refers to the tools, technologies and services that are used to store, manage, process and analyze data in the cloud. Cloud data infrastructure provides several benefits, including scalability, flexibility, cost-effectiveness and the ability to process and analyze large amounts of data. This can help businesses make better-informed decisions, drive innovation and gain a competitive advantage in the marketplace.

cloud data infrastructure

Here are some of the key components of cloud data infrastructure:

  • Cloud storage: Cloud storage provides a scalable and secure way to store data in the cloud. Services such as Amazon S3, Google Cloud Storage and Microsoft Azure Blob Storage allow businesses to store and cost-effectively retrieve data.
  • Data processing: Cloud data infrastructure provides tools and services for processing and transforming data. Services such as Amazon EMR, Google Cloud Dataproc and Microsoft Azure HDInsight provide distributed processing for big data workloads.
  • Data warehousing: Cloud data warehouses allow businesses to store and analyze large amounts of structured data. Services such as Amazon Redshift, Google BigQuery and Microsoft Azure Synapse Analytics provide fast and scalable data warehousing.
  • Data analytics and machine learning: Cloud data infrastructure provides tools and services for analyzing data and building machine learning models. Services such as Amazon SageMaker, Google Cloud AI Platform and Microsoft Azure Machine Learning enable businesses to build, train and deploy machine learning models at scale.
  • Data governance and security: Cloud data infrastructure provides tools and services for managing data governance and security. Services such as AWS Identity and Access Management, Google Cloud Identity and Access Management and Microsoft Azure Active Directory provide identity and access management, data encryption and other security features.

Data Monetization / Data Products

Data is the most valuable asset of the organization that drives the business by providing lots of answers to what is happening, what happened and what may happen.

Data monetization is the process to use data assets to create a measurable economic benefit. It helps to turn the data insights into actionable initiatives to gain an advantage over the competition and make profits.

We bring our expertise to monetize your data assets that help to create additional revenue sources.



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