What We Offer

Our Capabilities

Embracing the cloud for better data management

Gain accurate, real-time and actionable insights with better data management and analytics.

Data from disparate sources makes it difficult for organizations to get a unified view. Increased computing power, mobile technology, thousands of news feeds and sources, the rise of digital businesses and millions of IoT devices have spawned an exponential growth in the volume of data. That challenge gets bigger with decreased storage costs and the transition to cost-effective and elastic public and converged private clouds.

The task of managing all that data raises formidable issues. How do you collect the data? How do you store it? Can you trace it back? In order to derive actionable insights, it has to be gleaned, sorted, moved, processed, analyzed and integrated efficiently. Decisions, policies, strategies and the ways of working are all defined by analyzing past data and determining future trends. With more and more businesses adopting cloud strategies, another challenge is in finding people with the right skill sets to execute their cloud migration and data integration programs.

The right approach for businesses is to create an effective strategy to manage cloud and data migration. That may involve a “lift-and-shift” approach, migrating from a legacy database to a cloud-native database, determining the ETL (extract, transform and load) framework, moving to an IaaS (infrastructure as a service) provider to support a broad variety of analytics workflows, or zeroing in on SaaS (software as a service) or PaaS (platform as a service) offerings. Businesses could leverage a combination of those offerings to define a comprehensive cloud-based modern data architecture. 

We offer services and solutions for:
  • Big Data; data lake; and data warehouse
  • ETL; ELT; and data pipes
  • Data governance; data lineage; and DataOps
  • Data catalog; data quality
  • Data virtualization
  • MDM; and reference data
  • Transformation to public cloud (AWS, MS Azure, GCP)
Our services and solutions include data science-based solution accelerators such as machine learning data catalog.

Transform enterprise-wide with revamped architecture

Convert data into business assets with support to build, deploy and manage cloud-based architectures.

We at Iris have partnered with several businesses to help them turn their data into business assets and help them stay competitive. We specialize in building, deploying and maintaining cloud-based modern data architectures spanning best-of-breed tools and technologies. 

Our suite of solutions for data and analytics services comprises information management, ETL, enterprise data warehousing, data quality and governance, master data management and Big Data – data management and analytics.

We partner with industry leaders such as Amazon Web Services and Microsoft Azure for IaaS platforms and IBM Bluemix for PaaS platforms. We offer a comprehensive suite of cloud computing services. Our consultants execute petabyte-scale implementations with deep skills across Cloudera and Hortonworks distributions and Big Data skills of Hive, HBase, Sqoop, Spark, Yarn, Oozie, etc. 

We apply a 'data transformation framework' that helps organizations upgrade their traditional data applications — operational data stores (ODSs), data warehouses, and other data platforms and practices within data warehousing and enterprise data management — to modern data architecture. The framework covers transformation across the dimensions of data sources and types, data formats, data delivery, data and design patterns, and data landscape and governance.

In addition, we partner with customer organizations in building domain-based centralized data lakes including provisioning for a raw, unrefined view of data to data scientists and analysts for further discovery and analytics.

We offer services and solutions for:
  • Enterprise reports; and visualization
  • Dashboards; and scorecards
  • BI consolidation – report rationalization
  • Transformation to modern BI
  • Advanced analytics
  • Transformation to public cloud (AWS, MS Azure, GCP)
Our services and solutions include data science-based solution accelerators such as the Iris rationalization accelerator.

What We Offer

Mastering data lineage

Tracing your data’s journey with our accelerators cuts redundancy and helps your business get audit-ready.

Data lineage is about knowing the source of the data and tracing the data lifecycle as it transforms and gets into reports and analysis. It helps reduce redundancy and misuse of data by ensuring that all data is catalogued and owned, with the correct lineage and is audit-ready. Data cataloging and data lineage are critical for business analysis and data governance; they are also now mandatory for regulatory compliance.

Our Data & Analytics practice offers comprehensive services and solutions across competencies such as data management, business intelligence & visualization, and data science & advanced analytics. We provide those solutions and services through accelerators based on data science (artificial intelligence, machine leaning and natural language processing). Our accelerators have delivered for our clients substantial benefits through data transformations and business intelligence transformations.

Our Data Catalog solution accelerator, for instance, helps in cataloging data by addressing the challenge of data duplication (where the same data element shows up in multiple metadata headers, resulting in duplicate data fields and data sets). This is accomplished using machine learning and natural language processing techniques with exact and fuzzy matching of data attributes and distribution, and statistical matching for data values.

Growing data volumes, delayed data distribution, near real-time requirements and shifts to the cloud are accentuating the need for data transformation across industries. The continually expanding range of data sources, and diverse data types and data formats make those transformations all the more important.

Many organizations focus on technology platform transformations, but miss out on standardizing and cataloging their diverse structured and unstructured data assets. That leaves business users with the daunting task of discovering and profiling the data before they can begin to derive actionable insights from them.

Frameworks for data transformation

Manage large data volumes with our framework-based approach.

Growing data volumes, delayed data distribution, near real-time requirements and shifts to the cloud are accentuating the need for data transformation among organizations. Newer data sources, data types and data formats characterize the nature of modern data. In order to meet those challenges, organizations need to look at how their data platforms – operational data stores and data warehouses – could be transformed and how enterprise data management needs be addressed.

We recommend a framework-based approach to Data Transformation. This involves not just  a change of technology platform but also a comprehensive transformation across all dimensions of data.

Our framework that helps organizations transform their traditional data applications — operational data stores (ODSS), data warehouses, other data platforms within data warehousing and enterprise data management — to a modern data architecture. The framework covers transformation across the dimensions of data sources and types, data formats, data patterns, design patterns, data delivery, data landscape and governance.

ML data catalog accelerator

Overcome data cataloging constraints, duplication and licensing restrictions.

Our Machine Learning Data Catalog (MLDC) accelerator was built to solve the challenge of standardizing and cataloging the data as part of a data transformation program. It addresses the challenge of data duplication – where the same data element have multiple metadata headers, resulting in duplicate data fields and data sets. It assists data and business analysts in their efforts to standardize and define the metadata in a data catalog. The data catalog will provide a searchable business glossary of data sources and common data definitions gathered from data discovery, classification, and cross-data source entity mapping.

This is accomplished using machine learning and natural language processing techniques with exact and fuzzy matching of data attributes and distribution and statistical matching for data values.

This accelerator is flexible and can be customized for the specific data cataloging requirements of an organization without licensing restrictions and with cost savings. Furthermore, the data catalog built by the accelerator can be used for other data governance activities in technology tools like Collibra, Waterline, Solidatus and Apache Atlas.

The MLDC accelerator can be deployed as part of a data transformation initiative and helps in cataloging both structured and unstructured data assets. This helps in delivering the full value of data transformation to an organization.

Rationalization accelerator

Our data-science driven accelerator has consistently delivered a more than 30% reduction in the volume of reports.

The Iris Rationalization Accelerator leverages data science — specifically Machine Learning (ML) and Natural Language Processing (NLP) — to reduce the time, cost and complexity commonly associated with report rationalization. As a result, it allows users to perform report rationalization cost-effectively on a regular basis, enabling the extraction of deeper BI insights faster from a smaller number of relevant reports.

  • Identifies similar and duplicate reports
  • Provides logical report groups and standard report templates
  • Creates a business glossary
  • Rationalizes the report set, often reducing report volume by >30%

The Iris Rationalization Accelerator, which is driven by data science, has consistently delivered for our clients a more than 30% reduction in the total volume of reports. Some of our clients have seen a more than 70% reduction in the volume of reports and cost reductions of up to 40%. Industry estimates indicate that the savings from even a 10% reduction in the volume of reports will pay for the cost of the rationalization effort.

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