Data Management

Unlike a few decades ago when traditional decision-making processes relied on experts’ experiences, many organizations today rely on a robust data management strategy to enhance availability of appropriate data for effective decision making. As such, proper data management is essential for organizations, people, and connected things to optimize the use of data within the bounds of policy and regulations. This practice will ensure that decisions made, and actions taken can maximize the benefit to the organization. 


Akira offers a variety of services to effectively assist you manage your data with established strategy and reliable methods to run a well-managed, secure, and compliant data management function. 

Data Governance

Data Governance


Effective data governance ensures that data is consistent and trustworthy and does not get misused. It is increasingly critical as organizations face new data privacy regulations and rely more and more on data analytics to help optimize operations and drive business decision-making.


Data Security

Data Security


Whether an enterprise needs to protect a brand, intellectual capital, and customer information or provide controls for critical infrastructure, the means for incident detection and response to protecting organizational interests have three common elements: people, processes, and technology.

Data Architecture

Data Architecture


As part of our data management services, we ensure that customers have up-to-date, curated, cross-silo enterprise data that creates a single version of the truth. Akira develops and deploys data architecture frameworks that enable the people in your organization to succeed in leveraging these new cloud-native technologies and tools.

Data Observability

Data Observability


With efficiency being the priority of most organizations, fully understanding the health of data in your systems will eliminate data downtime using application of best practices of DevOps to data pipeline observability.

Akira can help you implement and configure data observability tools that will automate monitoring and alerting to identify and evaluate data quality and discoverability issues. By surfacing data downtime incidents as soon as they arise, this will provide the holistic data observability framework necessary for true end-to-end reliability. This will connect to your existing stack quickly and seamlessly and does not require modifying your data pipelines. It monitors your data at rest and does not require extracting the data from where it is currently stored.

Interested in Data Management?

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Insights & Case Studies

Case Study - Migrating Complex SAS Processes to Databricks

Migrating Complex SAS Processes to Databricks | CMS

Akira used AWS cloud and modern, open-source/open-standard data technologies (Databricks & Python/Pandas/PySpark, Flake8) to migrate critical data processes from SAS to Databricks....

Case Study - Cloud Adoption Analysis and Cloud Service Provider (CSP) Recommendation

Cloud Adoption Analysis and Cloud Service Provider (CSP) Recommendation | FERC

FERC currently manages data set computations using on premise infrastructure and various departments relaying on its rapidly growing 14 TiB data warehouse for analytical tasks to be consumed enterprise wide...

Case Study - Modernization & Implementation of Enterprise Architecture

Modernization & Implementation of Enterprise Architecture | FERC

OIT provides IT services to EIA customers. OIT is responsible for providing IT infrastructure, application development and support and user services to over 600 Federal and contractor staff.....

Case Study - IT Policies Development and ServiceNow ITSM Implementation

IT Policies Development and ServiceNow ITSM Implementation | DOE

Office of Information Technology had no accepted or written policies for critical infrastructure management functions. Consequently, conflicting or unclear rules existed for decision-making and response actions...