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Data Modernization

Accelerate Your Data Modernization Journey to Achieve Valuable Business Outcomes

As organizations evolve into digital enterprises, data is becoming increasingly critical to driving business operations efficiently. Of course, not all data is critical, but the most critical data must be governed, monitored for high quality, and integrated with other data for actionable information that changes outcomes and drives value.

The success of an organization depends heavily on its ability to access actionable information that requires flexible data structures and modern platforms. By focusing on a cloud-first strategy combined with strong consulting capabilities we deliver a modern data ecosystem, which democratizes access to quality data, reduces the time to insight considerably, and drives compelling user experiences.

Our Approach

Our Service Offerings

Data is a crucial corporate asset which if poorly managed can saddle organizations with inconsistent data sets, data quality issues, and incompatible data silos. We offer a data management strategy, as a part of data modernization services that help organizations to gain better control over the data that is governed, secured, and managed. We provide a roadmap for cataloging and governing the data, implement data quality over the strong data governance framework, and classify the data according to its sensitivity while defining processes to safeguard it.

A well-defined data governance program is a key component of an effective data management strategy. We help you scale the data governance maturity model in these five phases:

Case Study

Enterprise Data Management on Hadoop

The Data Governance Maturity Model

At Innova, we also follow a standardized three-way data governance framework that helps you progress through the five levels in the maturity model. It facilitates access to high-quality and trustworthy data while ensuring compliance with regulatory, privacy, confidentiality, and other policies.

At the onset, we set up an inventory that acts as a single source of truth and provides insights into all the sources of data at hand, where the data is stored, and what eventually happens to it. The inventory is essential as it leads to improved efficiency and accountability for everybody in the organization.

Next, we identify the data owners who work in close collaboration with data stewards, who are responsible for the data within a specific domain and can manage the data for compliance, literacy, and quality.

Finally, we establish a governance committee that decides on the budget, data access policies and standards, and data quality issue priorities. This disciplined workflow when implemented in an organization ensures that the right data is available to the right person at the right time in a reliable and standardized form.

At Innova, we analyze the current data and the data gravity before undertaking a data migration project. We also identify the client’s objectives that can range from consolidating data to creating a data lake. We look at the outcomes that they want to achieve, which include extracting value from the data, saving costs, or ensuring data quality. Our experienced data migration and management team undertakes processes that are designed to achieve these goals at minimal risks and business impact.

To enable data migration, we establish a migration factory that helps us identify the data sets, decide on the different integrations and patterns required, and the data loading mechanisms. The migration factory seamlessly integrates with existing processes and SLAs and can be tailored to meet the client’s needs and requirements. In-built with accelerators, frameworks, and templates, the migration factory enables quicker time to market at lower costs.

For cloud data migration, in particular, we follow a well-defined approach that includes:

Case Study

Satellite Data Migration & Image Processing on Public Cloud

DevOps
Framework

Read More
  • Setup  a global CI/CD platform for prem and cloud deployments as a shared service across  the organization
  • Service Now based serv ice catalogs and CMDB
  • Leverage Innova’s test automation framework  to  support test automation as part of CI process
  • On demand provisioning / deprovisioning automation for infrastructure as code, application deployment
  • Application performance monitoring enablement

Cloud
Readiness Assessment

Read More
  • Work with Application owners to assess the readiness of the applications for cloud migration
  • Leverage our accelerators for topology discovery, infrastructure blueprinting, infrastructure as code and application deployment automation tools.

Create
Service Catalog

Read More
  • Leverage our Architecture patterns for Azure and enhance the service catalog with architecture patterns and configurations (T-Shirt Sizing) catering to various applications / groups
  • Incorporate cost optimization, scale, DR by leveraging Azure autoscaling, Spot-instances, optimum utilization of reserved instances

Cloud
Migration

Read More
  • Adopt/enhance existing architecture patterns.
  • Create service catdog for the application
  • Setup CI/CD pipeline for cloud deployment
  • Enhance the services catalog
  • Assist the application owners in end to end validation and cut-over

Monitor
and Feedback

Read More
  • Post migration, monitor infra, application performance, quality and cost of operations
  • Provide recommendations on operations improvement areas

The focus of data tools and teams in the last decade has largely been on data aggregation, transformation, and storage. However, data consumption, where the true value of data is realized, has been ignored or fragmented. Even though most organizations have digitized their data cycle, the linear approach from generation to consumption leads to complexity. This has inhibited the data consumers like the analysts, marketing teams, and data scientists and introduced hurdles in their activities. The solution to this lies in DataOps.

What DataOps delivers?

Innova's DataOps offerings and service facilitate the collaboration between traditionally siloed roles to drive innovation. We help enterprises evolve their data management strategies to handle data at scale while being aligned with real-world market changes as they happen. We adopt an agile, collaborative and change-friendly approach to DataOps which includes these four phases:

Step 1: We create virtualized datasets that are automatically updated in the background and are transparent to data consumers. This unified data helps find data errors and create insightful reports.

Step 2: We create personal spaces called Data Pods that create a unique environment for each team and are capable of auto scaling and hosting microservices and data mounts.

Step 3: We ensure privacy and security with role/policy based secured access to the encapsulated data pods. Each service communicates securely with other services.

Step 4: We integrate DataPods to CI/CD that help accommodate infrastructure updates easily and provision for a pipeline to run through different environments.

Innova helps organizations to leverage data and achieve positive business outcomes by empowering their data teams with the tools and support they need. We facilitate data enablement by focusing on data literacy, enterprise-wide data culture, and user-friendly technologies. The first step we take is to bridge the gap between IT and the business. We then identify the challenges that impede the complete use of data such as lack of communication between departments, insufficient budget, lack of skilled human resources, and a lack of efficiency and scale. Our data enablement initiatives ensure that the client scales the data maturity curve, unlocking the complete potential of data and gaining a competitive edge.

In the course of our data enablement strategy, we emphasize that the one who creates the data also owns it. We understand that data enablement is a program and not a standalone project or initiative. As clients embark on their data enablement journey we ensure a culture-fit by aligning all the stakeholders, C-suite, and specialists. We assess the present and desired state to establish a vision and define measurable goals and KPIs. Finally, we enable self-service data access which democratizes the use of data and empowers citizen data scientists to accelerate the time to insights.

Case Study

Enterprise Data Enablement and Cloud Infrastructure Setup

Data Management

Data is a crucial corporate asset which if poorly managed can saddle organizations with inconsistent data sets, data quality issues, and incompatible data silos. We offer a data management strategy, as a part of data modernization services that help organizations to gain better control over the data that is governed, secured, and managed. We provide a roadmap for cataloging and governing the data, implement data quality over the strong data governance framework, and classify the data according to its sensitivity while defining processes to safeguard it.

A well-defined data governance program is a key component of an effective data management strategy. We help you scale the data governance maturity model in these five phases:

Case Study

Enterprise Data Management on Hadoop

The Data Governance Maturity Model

At Innova, we also follow a standardized three-way data governance framework that helps you progress through the five levels in the maturity model. It facilitates access to high-quality and trustworthy data while ensuring compliance with regulatory, privacy, confidentiality, and other policies.

At the onset, we set up an inventory that acts as a single source of truth and provides insights into all the sources of data at hand, where the data is stored, and what eventually happens to it. The inventory is essential as it leads to improved efficiency and accountability for everybody in the organization.

Next, we identify the data owners who work in close collaboration with data stewards, who are responsible for the data within a specific domain and can manage the data for compliance, literacy, and quality.

Finally, we establish a governance committee that decides on the budget, data access policies and standards, and data quality issue priorities. This disciplined workflow when implemented in an organization ensures that the right data is available to the right person at the right time in a reliable and standardized form.

Data Migration

At Innova, we analyze the current data and the data gravity before undertaking a data migration project. We also identify the client’s objectives that can range from consolidating data to creating a data lake. We look at the outcomes that they want to achieve, which include extracting value from the data, saving costs, or ensuring data quality. Our experienced data migration and management team undertakes processes that are designed to achieve these goals at minimal risks and business impact.

To enable data migration, we establish a migration factory that helps us identify the data sets, decide on the different integrations and patterns required, and the data loading mechanisms. The migration factory seamlessly integrates with existing processes and SLAs and can be tailored to meet the client’s needs and requirements. In-built with accelerators, frameworks, and templates, the migration factory enables quicker time to market at lower costs.

For cloud data migration, in particular, we follow a well-defined approach that includes:

Case Study

Satellite Data Migration & Image Processing on Public Cloud

DevOps
Framework

Read More
  • Setup  a global CI/CD platform for prem and cloud deployments as a shared service across  the organization
  • Service Now based serv ice catalogs and CMDB
  • Leverage Innova’s test automation framework  to  support test automation as part of CI process
  • On demand provisioning / deprovisioning automation for infrastructure as code, application deployment
  • Application performance monitoring enablement

Cloud
Readiness Assessment

Read More
  • Work with Application owners to assess the readiness of the applications for cloud migration
  • Leverage our accelerators for topology discovery, infrastructure blueprinting, infrastructure as code and application deployment automation tools.

Create
Service Catalog

Read More
  • Leverage our Architecture patterns for Azure and enhance the service catalog with architecture patterns and configurations (T-Shirt Sizing) catering to various applications / groups
  • Incorporate cost optimization, scale, DR by leveraging Azure autoscaling, Spot-instances, optimum utilization of reserved instances

Cloud
Migration

Read More
  • Adopt/enhance existing architecture patterns.
  • Create service catdog for the application
  • Setup CI/CD pipeline for cloud deployment
  • Enhance the services catalog
  • Assist the application owners in end to end validation and cut-over

Monitor
and Feedback

Read More
  • Post migration, monitor infra, application performance, quality and cost of operations
  • Provide recommendations on operations improvement areas

Data Operations

The focus of data tools and teams in the last decade has largely been on data aggregation, transformation, and storage. However, data consumption, where the true value of data is realized, has been ignored or fragmented. Even though most organizations have digitized their data cycle, the linear approach from generation to consumption leads to complexity. This has inhibited the data consumers like the analysts, marketing teams, and data scientists and introduced hurdles in their activities. The solution to this lies in DataOps.

What DataOps delivers?

Innova's DataOps offerings and service facilitate the collaboration between traditionally siloed roles to drive innovation. We help enterprises evolve their data management strategies to handle data at scale while being aligned with real-world market changes as they happen. We adopt an agile, collaborative and change-friendly approach to DataOps which includes these four phases:

Step 1: We create virtualized datasets that are automatically updated in the background and are transparent to data consumers. This unified data helps find data errors and create insightful reports.

Step 2: We create personal spaces called Data Pods that create a unique environment for each team and are capable of auto scaling and hosting microservices and data mounts.

Step 3: We ensure privacy and security with role/policy based secured access to the encapsulated data pods. Each service communicates securely with other services.

Step 4: We integrate DataPods to CI/CD that help accommodate infrastructure updates easily and provision for a pipeline to run through different environments.

Data Enablement

Innova helps organizations to leverage data and achieve positive business outcomes by empowering their data teams with the tools and support they need. We facilitate data enablement by focusing on data literacy, enterprise-wide data culture, and user-friendly technologies. The first step we take is to bridge the gap between IT and the business. We then identify the challenges that impede the complete use of data such as lack of communication between departments, insufficient budget, lack of skilled human resources, and a lack of efficiency and scale. Our data enablement initiatives ensure that the client scales the data maturity curve, unlocking the complete potential of data and gaining a competitive edge.

In the course of our data enablement strategy, we emphasize that the one who creates the data also owns it. We understand that data enablement is a program and not a standalone project or initiative. As clients embark on their data enablement journey we ensure a culture-fit by aligning all the stakeholders, C-suite, and specialists. We assess the present and desired state to establish a vision and define measurable goals and KPIs. Finally, we enable self-service data access which democratizes the use of data and empowers citizen data scientists to accelerate the time to insights.

Case Study

Enterprise Data Enablement and Cloud Infrastructure Setup

Resources

DataOps -Innova Solutions

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DataOps whitepaper

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