The data quality tools market continues to show strong revenue growth, driven by cost, process optimization and digital business initiatives. Applying data quality tools to existing and emerging business scenarios will enable data and analytics leaders to deliver greater business value.
The discipline of data quality assurance ensures that data is "fit for purpose" in the context of existing business operations, analytics and emerging digital business scenarios. It covers much more than just technology. It includes program management, roles, organizational structures, use cases and processes (such as those for monitoring, reporting and remediating data quality issues). It is also linked to broader initiatives in the field of enterprise information management (EIM), including information governance and master data management (MDM)
Siloed data sources, duplicate entries, data breach risk—how can you scale data quality for ingestion and transformation at big data volumes?
Data and analytics capabilities are firmly at the top of CEOs’ investment priorities. Whether you need to make the case for data quality to your c-level or you are responsible for implementing it, the Definitive Guide to Data Quality can help.
Download the Definitive Guide to learn how to:
Stop bad data before it enters your system
Create systems and workflow to manage clean data ingestion and transformation at scale
Make the case for the right data quality tools for business insight
Published By: iKnowtion
Published Date: Nov 09, 2011
Data quality is a key component for optimum customer communications programs. Find out how to determine whether you have appropriate tools in place to support current and future marketing analysis functions.
Learn what a Web Service is and how it works, the advantages of using a Data Quality Web Service, the technology assessment for implementation, and several case studies (Saab and other real world case studies) to demonstrate real life successes.
Tom Brennan and John Nydam explain the Melissa Data and Stalworth partnership, discuss the business problems caused by bad data, and describe how DQ*Plus provides a complete data quality solution for enterprise applications and commercial databases.
"Increasingly, brands are looking to differentiate based on an exceptional customer experience. The key to improving the customer experience is being able to effectively measure what’s working and what you need to improve. IBM host a webinar presenting tips on how to measure the customer experience for your brand and how to use that data to build better journeys.
Please join IBM and guest speaker Andrew Hogan from Forrester Research as we share tips on how to best measure the digital experiences customers have with your brand and how to use that information to build better journeys.
The webinar will provide attendees with:
• Best practices to measure the quality of digital customer experiences
• Guidance on the kinds of tools to use to capture the right CX metrics
• Tips for integrating metrics, including the role of customer journeys
• Techniques to drive action and improve digital experiences"
As the volume of data coming into organizations – from both internal and external sources – continues to grow and makes its way across departmental systems in many different formats, there is a critical need to create a single, holistic view of the key data entities in common use across the enterprise. Master Data Management (MDM) aims to accomplish this goal. Not surprisingly, MDM has become a significant priority for global enterprises, with the market expected to triple from $9.4B to $26.8B by 2020 according to analysts.
The reality, though, is that while seemingly everyone is investing heavily in the tools to manage data, few are putting a great enough emphasis on the data itself. And that’s a problem. Poor data quality is said to be costing businesses $3.1 trillion annually – and that’s just in the US alone. The information being put into MDM tools must be mastered first and foremost.
Published By: Tricentis
Published Date: Jan 08, 2018
Data. It seems to be everywhere today and yet we can never get
enough of it. But as it turns out, a lack of data isn’t our problem
-- our problem is the difficulty piecing together, understanding and
finding the story in all the data that’s in front of us.
In software testing in particular, the need for consolidated,
meaningful test metrics has never been higher. As both the pace of
development and the cost of delivering poor quality software
increase, we need these metrics to help us test smarter, better and
Fortunately, business intelligence now exists to make this goal a
reality. The analytics these tools provide can help drive efficient
and effective testing by providing teams with insight on everything
from testing quality and coverage to velocity and more. And this
knowledge can position the QA team as trusted experts to advise
the entire software development team on steps that can ensure a
better quality end result.
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