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

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


Overview

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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