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 and analytics practice offers comprehensive services and solutions across competencies such as data management, business intelligence and 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.

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 can 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 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.

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.

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.