Data Management
In the current environment we live in, we tend to capture all the events in a business. As we are capturing every heart beat in few businesses, the velocity of the data defines the usage of the technology as well. The faster we get the data, the better the infrastructure should be. Typical transactions in million transactions a day is taken care by traditional RDBMS, anything we have options to choose from NoSQL to Big Data technologies as our Data Storage option.
Services
Data Migration
Data migration is the process of transferring data between storage systems or environments, crucial for cloud migrations and hardware replacements. It can be done via online migration (using internet or WAN for real-time access) or offline migration (using physical storage for large-scale data transfers). Database migration involves moving databases, often with tools provided by cloud services to maintain data security and integrity. Best practices include planning ahead, testing thoroughly, ensuring security, backing up data, and monitoring the migration process to avoid disruptions and ensure a smooth transition.
Data Governance
Data governance is the practice of managing and safeguarding an organization’s data assets, ensuring that data is used responsibly and effectively. It involves three main components: people, who include data stewards and data owners responsible for quality and compliance; processes, which govern how data is acquired, stored, and shared; and technologies, such as data catalogs and access controls to maintain data integrity and security. Data governance is crucial for ensuring data quality and accuracy, promoting better decision-making. It ensures compliance and security, safeguarding sensitive information while meeting regulatory standards. Additionally, it helps in reducing data silos by unifying data management, fosters data literacy and collaboration across teams, and supports scaling business intelligence as data volumes grow.
Data Storage
Data storage is the process of preserving digital information for future use, ensuring data remains accessible even after the computer powers down. It deals with two main types of data: input data, provided by users (like typing or uploading files), and output data, which computers generate after processing. Initially, data storage was necessary due to the limitations of memory (RAM), which couldn’t retain data after shutdown. Storage devices are either Direct-Attached Storage (DAS), directly connected to a computer, or network-based storage, accessible via networks like cloud storage. Data can be stored using different media, including magnetic (hard drives), optical (CDs, DVDs), mechanical (floppy disks), and even biological media (DNA/RNA).
Master Data Management
Master Data Management (MDM) is a discipline focused on ensuring the accuracy, consistency, and uniformity of an organization’s essential data. It creates a single source of truth or a “golden record” for key data entities like customers, products, locations, and suppliers by consolidating information from various internal and external sources. MDM eliminates data inconsistencies, enabling data sharing across departments and improving decision-making with reliable data. Key MDM domains include customer, product, location, and supplier data. It leverages data governance, data integration, data quality management, and metadata tracking to create and maintain trusted master records. An example is reference data, such as zip codes or airport codes, which are universally accepted identifiers. MDM provides a trusted data foundation, supporting accurate and consistent data for business operations and decision-making.
Tools and Technologies Supporting Our Services :
Data Integration & Virtualization
Integration is crucial for streamlining applications by exchanging data within or across enterprises, following predefined workflows. Real-time integration is increasingly common, revolutionizing existing application landscapes. Applications now integrate through APIs for near real-time, lightweight integration with minimal transformations (e.g., Dell Boomi, MuleSoft). Additionally, offline integration via nightly jobs facilitates data push and pull. With microservices gaining traction, this field is evolving rapidly with innovative technologies and ideas.
Services
Data Mart
A Data Mart is a specialized subset of a data warehouse, designed to focus on a specific business area or department. It provides quick and easy access to relevant data, enabling more efficient and targeted analysis. By isolating data pertinent to particular functions, data marts streamline decision-making processes, improve operational efficiency, and reduce the complexity and cost associated with managing large volumes of data. Essential for businesses looking to leverage their data for specific insights and better outcomes, data marts are a key component in modern data management strategies.
Data Lake
A data lake is a vast repository for storing all types of data—structured and unstructured—at any scale, from small sensor readings to massive social media streams. It consolidates diverse data, including logs, images, and videos, without needing pre-structuring. Data lakes support various analytics, such as dashboards, big data processing, and machine learning, enabling deep exploration and insight. They help organizations identify growth opportunities, improve customer retention, and make informed decisions. Essentially, a data lake serves as a comprehensive reservoir for valuable data-driven insights.
Automated Data Pipe Lines ( Cloud Tech )
An automated data pipeline is a system that efficiently moves data from its sources to its destinations with minimal manual intervention. It involves the extraction of data from various sources, transformation of the data for refinement, and loading of the processed data into target systems like data warehouses or analytics platforms. Automation enhances efficiency by ensuring consistent data flow, provides timeliness with real-time or near-real-time updates, and scales seamlessly with growing data volumes. It can be implemented as batch or real-time (streaming) pipelines and can run on on-premises infrastructure or leverage cloud-native services. Tools like Apache NiFi, Apache Airflow, and cloud services like AWS Glue and Google Dataflow are commonly used to manage these pipelines.
ETL, Power BI | Tableau
Data analysis is defined as a process of cleaning, transforming, and modeling data to discover useful information for business decision-making. The purpose of Data Analysis is to extract useful information from data and taking the decision based upon the data analysis.
Tools and Technologies Supporting Our Services :
Data Story Telling
Reporting is the key element in any application to understand the operations and helps to analyze the activities to take operational, tactical and strategic decisions. Reporting systems are designed to benefit all the different types of stakeholder’s right from Operational User, Customers, Partners and Knowledge workers who takes the strategic decisions. Stories are powerful medium in which we can explain the events in the business. We listen attentively to our customer needs and think from their perspective and provide our reports | stories accordingly.
Services
Operational Reporting
Operational reporting is a process that generates detailed, regular reports on an organization’s daily activities, such as production costs, resource expenditures, financial transactions, and process efficiency. It provides real-time data, supporting informed decision-making and timely adjustments to operations. This reporting helps improve efficiency, reduce costs, and manage supply chains effectively. By tracking performance metrics and KPIs, organizations can optimize processes and address issues promptly. In sectors like manufacturing, retail, and finance, operational reporting is essential for maintaining smooth, efficient operations.
Predictive Analytics
Predictive analytics leverages historical data, statistical models, and machine learning to forecast future outcomes. It analyzes past and current data patterns to predict future events, enhancing decision-making across various domains. Key applications include refining business operations, assessing risks, guiding marketing strategies, and optimizing supply chains. Predictive models come in three types: classification for categorizing data, clustering for grouping similar data, and time series for forecasting based on historical trends. By anticipating future trends, predictive analytics helps organizations stay competitive and make proactive decisions.
Reports and Dashboards
Reports and Dashboards are both crucial for data analysis, yet they serve different roles:
Reports are static documents that provide detailed snapshots of historical data at specific intervals (daily, weekly, monthly). They are often used for in-depth analysis of past performance and trends, such as financial statements or project status reports.
Dashboards, on the other hand, are dynamic and interactive, offering real-time or near-real-time insights. They use visual elements like charts and graphs to give a high-level overview of key performance indicators (KPIs) and allow users to explore data interactively. Examples include sales dashboards or marketing performance trackers.
Choose reports for detailed historical data and scheduled updates, and dashboards for real-time insights, strategic monitoring, and interactive data exploration. Both tools are essential for comprehensive data communication and decision-making.
Tools and Technologies Supporting Our Services :
Data Management
In the current environment we live in, we tend to capture all the events in a business. As we are capturing every heart beat in few businesses, the velocity of the data defines the usage of the technology as well. The faster we get the data, the better the infrastructure should be. Typical transactions in million transactions a day is taken care by traditional RDBMS, anything we have options to choose from NoSQL to Big Data technologies as our Data Storage option.
Services
Data Warehouse
Our company is proficient in both the Bill Inmon’s CIF approach to Enterprise Data Warehousing and Ralph Kimball’s Data Mart approach. CIF follows a top-down method, providing comprehensive raw materials for flexible modeling and reporting. Data Mart, in contrast, adopts a bottom-up approach, focusing on predefined problems and specific outcomes. Our data team selects the appropriate model based on organizational resources and project requirements.
Data Engineering
Learn to handle business events, transactions, social, and market data using RDBMS, NoSQL, and cloud file storage. Master Python and its libraries to process datasets according to application needs. Build robust data pipelines for enterprise-level analysis, integration, cleansing, and exception handling. Become a proactive and productive Data Engineer ready to excel in any company.
Data Analyst
Data analysis is defined as a process of cleaning, transforming, and modeling data to discover useful information for business decision-making. The purpose of Data Analysis is to extract useful information from data and taking the decision based upon the data analysis.
Tools and Technologies Supporting Our Services :
Data Integration & Virtualization
Integration is crucial for streamlining applications by exchanging data within or across enterprises, following predefined workflows. Real-time integration is increasingly common, revolutionizing existing application landscapes. Applications now integrate through APIs for near real-time, lightweight integration with minimal transformations (e.g., Dell Boomi, MuleSoft). Additionally, offline integration via nightly jobs facilitates data push and pull. With microservices gaining traction, this field is evolving rapidly with innovative technologies and ideas.
Services
Data Lakes
Data Lakes store raw data for flexibility and accessibility, unlike structured Data Warehouses. They cater primarily to Data Scientists and analytical users, accommodating all data types without immediate structuring. Utilized extensively in ML and AI domains, Data Lakes are typically hosted on cloud platforms like AWS and Azure, or on-premises Big Data environments.
Data Pipe Lines
Learn to handle business events, transactions, social, and market data using RDBMS, NoSQL, and cloud file storage. Master Python and its libraries to process datasets according to application needs. Build robust data pipelines for enterprise-level analysis, integration, cleansing, and exception handling. Become a proactive and productive Data Engineer ready to excel in any company.
Automated Data Pipe Lines ( Cloud Tech )
Data analysis is defined as a process of cleaning, transforming, and modeling data to discover useful information for business decision-making. The purpose of Data Analysis is to extract useful information from data and taking the decision based upon the data analysis.
ETL, Power BI | Tableau
Data analysis is defined as a process of cleaning, transforming, and modeling data to discover useful information for business decision-making. The purpose of Data Analysis is to extract useful information from data and taking the decision based upon the data analysis.
Tools and Technologies Supporting Our Services :
Data Story Telling
Reporting is the key element in any application to understand the operations and helps to analyze the activities to take operational, tactical and strategic decisions. Reporting systems are designed to benefit all the different types of stakeholder’s right from Operational User, Customers, Partners and Knowledge workers who takes the strategic decisions. Stories are powerful medium in which we can explain the events in the business. We listen attentively to our customer needs and think from their perspective and provide our reports | stories accordingly.
Services
Operational Reporting
Data Lakes store raw data for flexibility and accessibility, unlike structured Data Warehouses. They cater primarily to Data Scientists and analytical users, accommodating all data types without immediate structuring. Utilized extensively in ML and AI domains, Data Lakes are typically hosted on cloud platforms like AWS and Azure, or on-premises Big Data environments.
OLAP | Analytical Reporting
Learn to handle business events, transactions, social, and market data using RDBMS, NoSQL, and cloud file storage. Master Python and its libraries to process datasets according to application needs. Build robust data pipelines for enterprise-level analysis, integration, cleansing, and exception handling. Become a proactive and productive Data Engineer ready to excel in any company.
Visualization and Dashboard Development
Data analysis is defined as a process of cleaning, transforming, and modeling data to discover useful information for business decision-making. The purpose of Data Analysis is to extract useful information from data and taking the decision based upon the data analysis.
Tools and Technologies Supporting Our Services :