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May 22, 2017

Data Quality as a Service - Infosys DQneXT

Previous Blog : We discussed "Data Quality as a Service - The Challenge and The Opportunity"

Infosys Data Quality Solution - DQneXT

"DQneXT" is Infosys's comprehensive Data Quality solution that addresses many of the Data Quality requirements of an Organization. The DQ solution is based on SaaS model, multi-tenant and hosted on Infosys's SAP Cloud Platform. The architectural advantage of this solution in comparison to a traditional DQ tool are as illustrated below

Typical Data Quality Program

Infosys - DQneXT

*      Typically On premise

*      Multiple tools for profiling, enrichment & deduplication

*      Rules are created for every customer

*      Timeline for implementation of 3 domains is 4-6 months

*      Mostly target master data

*      Annual License cost is applicable

*      Cloud based multi-tenant solution

*      SaaS based solution that works on subscription model

*      One stop shop for profiling, enrichment, de-duplication and more

*      Starts with a rich library of Rules 

*      Timeline for onboarding is in days

*      Goes beyond master data to handle transaction & configuration data

*      Flexibility to pick and choose services & pay as you go

 Services offered under DQneXT

Infosys offers a combination of several services that are typically desired by most Organizations in a single packaged application. These services are typically employed at different stages of the data quality program from Data Discovery to Data Archival. The different packaged services and their relevance in the data quality life cycle are illustrated below

  • Data Profiling Service

Data Profiling is a basic service aimed at providing insights into the current quality levels of data. This leverages a rich repository of rules for each domain consisting of frequently used business rules, industry standard rules and technical rules that drive the data maintenance in SAP. These profiling services help understand the status of enterprise data quality and help establish the cleansing requirements for the data. The data can be analyzed along various dimensions like Accuracy, Consistency, Completeness, Integrity, Duplicacy, etc. The data quality is measured against defined thresholds and represented in business friendly dashboards. There is provision for report extraction for business review and corrections.

  • Data Enrichment Service

Data enrichment service provides the capabilities for ensuring the identified issues with data are fixed using automated fixing rules. They can be leveraged for bulk and routine fixes where a large number of records have similar issues. The pre-determined fixing rules and capabilities help shorten the data remediation cycle times and helps prepare data for "Fit to Use"

  • Configuration and Data Management Service

This is a special service aimed at next set of capabilities that help proactively identify and resolve issues with configuration data quality that impacts business process and transactions. They are used for early detection of bad data in transactions that could be due to configurable, obsolete or restricted data that impacts the end processes and could cause downstream impact to business.

  • De-duplication Service

The de-duplication service is an essential service that finds use in duplicate identification and survivorship. Typical used cases are

(1) Identifying duplicate records from historical data and

(2) Prevention of new duplicates at the point of creation of data

The one time identification of duplicate data helps get rid of duplicate records in system and identify the surviving records. This cleanup helps in making unique data available to business transactions for consolidated reporting and analytics. Similarly, the prevention of creation of duplicate data helps in keeping system clean going forward basis and yielding better search results improving the overall health of organization's data.

Key benefits of this solution

The DQneXT solution / service offering has been designed keeping in mind the evolving trends in the industry leveraging the SaaS based offering on cloud called DQaaS. This has significant benefits compared to the traditional approach of on premise DQ tools pre-dominant in today's market.

  1. Client need not invest in expensive infrastructure, licenses and sign up for annual maintenance services
  2. Works on subscription model with flexibility to choose required services without having to pay for unwanted modules/services
  3. Packaged services with comprehensive offerings including profiling, enrichment, proactive problem identification and de-duplication
  4. Quick Client on-boarding process with pre-set processes that can be realized in a matter of few days
  5. Pre-delivered rich repository of rules consisting of master, transactional and configuration rules
  6. New rules added to the repository on an ongoing basis become available to the Clients at regular intervals without any significant cost or effort


Infosys DQneXT provides muti-dimension capabilities for identification and resolution of the data quality problem that Organizations try to address through multiple industry standard licensed tools. This provides a cost effective and efficient alternative to the businesses that are adapting to the cloud based services.

Next Blog : We will discuss "FAQ's on DQneXT "

May 9, 2017

"Data Quality as a Service" - The Challenge and The Opportunity

"Data Quality as a Service" - The Challenge and The Opportunity


Enterprise data quality is no more an option. It is expected by Customers, demanded by business and enforced by regulators. Awareness on data quality and its impact on day to day operations and ultimately the business has been increasing steadily over last couple of years. Organizations have realized, good quality data is the foundation for an Organizations growth and sustenance in the long run. Poor data quality plagues the Organization in more than one ways and has direct and indirect costs associated with it. Data quality challenges impediment the growth of organizations and unreliable insights are persisted to other strategic initiatives that leads to lack of trust in enterprise data and can result in poor business decisions. Organizations have thus realized the need to manage Enterprise Information as a strategic asset and are finding ways and means to improve the value of this asset.

Though data quality tool vendors have been around in the market for a considerable duration, they have not been able to influence the adoption much. With growing economies, Organizations have had other focus and priorities like acquisitions, growth, market capitalization, expansion, etc etc. Slowly, the underlying problem with the data has grown multi-fold having escaped everyone's attention. This is seemingly apparent now as the businesses continue to grapple with "the data problem" that has reduced the operational efficiency, interfering with decision making and posing a larger threat if not controlled immediately.

The Challenge

Some of the key challenges associated with data quality include:

  • Data Quality is perceived to be one time initiative requiring significant time and money
  • Data assets lack clearly defined ownership
  • Most Organizations lack a well-defined strategy and governance for improving data quality
  • Ongoing data governance & sustenance is a key issue in master data & data quality programs
  • Data proliferation & duplication due to inadequate business rules and stewardship
  • Lack of consistent repeatable way to measure and score data quality

How big is the challenge?

Below are some snapshots of the key data problems and it's magnitude of impact on Organizations as estimated by some of the Industry's leading analysts. It clearly indicates the different nature of data quality issues and it's implications on operational and financial well-being of the Organization.

The Opportunity

As more organizations continue to focus on data quality, and as different departments within an organization continue to focus on their data elements, it presents a huge opportunity for the Vendors of the data quality tools and the service providers who help Organizations manage their data quality. Analysts estimate that only about 10% of Organizations have formal metrics for data quality, about 25% have informal metrics and rest do not measure data quality at all.

Gartner estimates that Data Quality Tools market is among the fastest growing in the enterprise software sector and continues to grow strongly year on year. It is currently growing at 13.5% and forecasts that this market's growth will accelerate to 16.7% by 2018, bringing the total revenues for Data Quality tools market to $2.24 billion.

Evolution of Organizational needs

In the context of the above opportunity, we see further evolution of Organizational needs that reduces the Total Cost of Ownership and at the same time deliver more comprehensive solutions that addresses different requirements in managing the life cycle of data quality. Some of the key requirements are

  • Look out for "Data Quality" as an end-to-end service 
  • Overcome limitations of licensed products. Ex Different tools for profiling and transformation
  • Need for continuously improving business rules to cater to the changing nature of business
  • Identification and archival of unused or redundant data on periodic basis for improved search and better performance
  • Proactively identify issues that arise due to configurations and suggest corrective course of action

"DQaaS - Data Quality as a Service"

With the advent of SaaS, the traditional approach of owning data quality applications on Organizations own computers or in own data centers is losing steam.  There is growing interest in the provisioning of data quality as a service and the vendors that provide this service. This is giving rise to a new breed of SaaS enabled services focusing on Data Quality termed "DQaaS - Data Quality as a Service".

Data quality software as a service (DQaaS) refers to data quality functions such as profiling, validation, standardization, matching, cleansing etc. delivered over web using a cloud-based or hosted model in which an external provider owns the infrastructure and provides the capabilities in a shared, multitenant environment used by its customers on a subscription basis. This provides a reasonably quick alternative to otherwise expensive and time-consuming deployments of data quality tools or the development of custom-coded solutions.

Next Blog : We will discuss "Data Quality as a Service - Infosys DQneXT"

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