Getting most of your current Oracle ERP solution, through our unique AI/data-driven analysis we can quickly identify opportunities hidden in your processes and help you achieve operational efficiencies.
Many customizations are no longer used by your business processes or have been replaced with features in the product. Our RICE & CEMLI analysis can uncover how your customizations and extensions are impacting your overall performance. Armed with this information, we can help you make decisions to retire and simplify customizations during an upgrade process, and create a business case to justify the investment.
Planning and funding are two of the most important hurdles to a successful ERP migration. We’ll leverage our decades of experience in implementation and Oracle engineering to build a business case and migration plan for your migration to Oracle Cloud ERP. Our holistic approach includes master data cleansing, RICE/CEMLI analysis, comprehensive 3 stage testing, user enablement and change management.
Our team of Oracle professionals can run or augment your migration to cloud. With over 80 years of combined implementation and engineering experience at Oracle, and a vast network of professionals we can bring to the table, we can de-risk your migration and ensure that best practices are followed to make your migration successful.
Hypercare to stabilize and refine an implementation and the processes running on it is arguably one of the most critical elements of any upgrade or migration project. Our methodology for assessing post implementation gaps and risks to make the right change to people, process or platform when dealing with post migration issues ensures you get the most stable solution. We also provide Application Managed Services for long term support and ongoing care of your applications. With Oracle Cloud applications releasing updates at least 8 times per year, a team of experts can make all the difference between a stable and successful migration, and a failed implementation.
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Optimized cloud spend analytics, resulting in a significant reduction in expenses, automated cost-center chargebacks, and granular insights for IT leaders
Data Indicators was engaged to help the client reduce cloud storage cost and optimize cloud spend analytics. The development project’s goal was to understand the costs of GCP and AWS, report accurate cost-center chargebacks, understand GKE namespace usage and costs, and create combined cloud spend views for IT leaders. The team also aimed to create optimization/tagging alerts to help the client manage their costs effectively.
Google BigQuery, Google Cloud SQL for PostgreSQL and Microsoft SQL Server, Google Virtual Machines, Google Cloud Storage, and Apache Airflow. To support data cataloging across the data mesh, we are implementing Secoda. For data governance, we have chosen Immuta. In addition, we used Docker where appropriate and Terraform for infrastructure management.
Standardized tooling and centralized data management supports fast onboarding and regulatory compliance for data teams
A large healthcare client needed a solution to help them make data-centric decisions, and a project was created to deliver this. The solution involved the collection and curation of large amounts of data and providing The Client with critical insights to inform decision-making. With the help of the Data Indicators team, a scalable, secure, and easily accessible solution was developed and deployed using GCP hosting and tools.
Data Indicators developed and delivered a core suite of processes and tooling that allowed The Client’s teams to retain ownership and responsibility over their product data while providing governance and data cataloging capabilities. This enabled The Client to ensure that data usage agreements and regulatory requirements are adhered to. The solution has resulted in a 400% faster time-to-onboarding for data teams and provided a standardized suite of tooling for ETL and data quality, as well as a centralized data catalog and centralized data access governance, including rights management for data usage agreements and support for regulatory compliance and auditability.
Google BigQuery, Google Cloud SQL, Google Cloud Storage, Google App Engine, Java, Spring Boot, Typescript, RESTful, Python and Google Apigee.
Comprehensive architectural guidance on foundational, structural, semantic, and organizational levels of interoperability, covering interconnectivity, data format and models, governance, and best practices.
Client needed a partner to provide architectural guidance on the four levels of interoperability, which include:
Foundational: Interconnectivity requirements between systems.
Structural: Defining the format, syntax, and organization of data.
Semantic: Underlying data models and use of data elements.
Organizational: Governance, legal policies, standards, and best practices.
The client was seeking a reliable partner to help them establish an integrated MarTech infrastructure that could support the creation of a centralized API-driven customer profile store, real-time customer engagement, content personalization, segmentation, and data science. They also required support in enhancing and maintaining their Hadoop-based data lake, implementing event-based data streaming pipelines, and ensuring data governance, privacy, and regulatory compliance. The client was looking for a trusted advisor to guide them through this process and help them achieve their marketing goals.
Data Indicators was tasked with the following deliverables:
Apache Spark ( Python and Scala ), Apache NiFi for real-time orchestration and transformation, Apache Airflow for batch orchestration and transformation, Snowflake Data Warehouse, Snowflake Snowpipe real time ingestion, Snowflake Snowpark , AWS S3, AWS EC2, Docker, Kubernetes and Adobe Experience Cloud