Data Quality Software
When data proliferates across an increasing variety of people, the risks of data leaks, data breaches, fake news, and rogue and inconsistent data naturally increases. And, when you lose control of your data, you can generate negative press, lose your job, or even kill your business. Find out how the right data quality software can manage the integrity of your data (and your organization’s reputation) collaboratively and safely.
The risks of standalone data quality software
The market is flooded with standalone solutions billed as “data quality software”. Look more closely and you will find these are usually data preparation and stewardship tools that offer several benefits to fight bad data. But only a few of them cover data quality for all. These standalone “data quality” solutions can often provide a quick fix but won’t solve problems in the long run.
It’s also common to see specialized data quality software that requires deep expertise for successful deployment. These tools are often complex and require in-depth training to be launched and used. In some cases, this type of data quality software can be helpful for long term projects, but if you have short term data quality priorities, you will miss your deadline.
Three vital capabilities of data quality software
You will eventually confront multiple use cases where it will be impossible for one person or team to manage your data successfully. To overcome these situations, you need a unified platform with data quality software in the cloud. Working together with business users and empowering them on the data lifecycle will give you and your team superpowers to overcome traditional data quality obstacles such as cleaning, reconciling, matching, or resolving your data. The next three capabilities are vital to achieving true data quality and are part of every data quality software solution:
- Data profiling: The process of gauging the character and condition of data stored in various forms across the enterprise. Data profiling is commonly recognized as a vital first step toward gaining control over organizational data. The key to this step is deep visibility into data, including individual data sources and specific records. With that deep visibility into the data statistical data profiling is performed, and custom rules and other modifications to the data that is not conforming to your organizations’ standards are applied.
- Data stewardship: The process of managing the data lifecycle from curation to retirement. Data stewardship is about defining and maintaining data models, documenting the data, cleansing the data, and defining its rules and policies. It enables the implementation of well-defined data governance processes covering several activities including monitoring, reconciliation, refining, de-duplication, cleansing, and aggregation to help deliver quality data to applications and end users.
- Data Preparation: The process of cleansing, standardizing, transforming, or enriching the data. Data-driven organizations rely on data preparation tools that offer self-service access to tasks that used to be done by data professionals, such as data experts, now done by operational workers that know the data best. It requires workflow-driven, easy-to-use tools with an Excel-like UI and smart guidance.
With cloud-based data quality software in place, the whole organization wins. Quality data will lead to more data use while reducing the costs associated with “bad data quality” such as decisions made using incorrect analytics. In this era of data overload, standalone data quality tools won’t cut it. You need solutions that work in real-time across all lines of business and don’t require data engineer-level knowledge to use.
Data quality use case: DMD Marketing
For DMD Marketing, a pioneer in healthcare digital communications and connectivity, data quality is a key differentiator. Because the principal service DMD provides—emails to health care professionals—is a commodity that can be supplied more cheaply by competitors, DMD needs to maintain its edge in data quality. The company’s client base needs to know they are targeting the proper healthcare professionals, so having clean data for names, addresses, titles, and more is vital.
DMD chose to deploy Talend Cloud Data Preparation and Data Quality for its data stewardship and self-service functionality. The company felt it was important to enable its internal users and its clients, to get in and see the email data and web tracking data on their own—without needing advanced technical skills. The company also wanted to move away from manual processes with manual data checks and is now automating as much as possible, then providing human users access so they can augment and enhance the data.
The ROI for DMD Marketing includes raising the mail deliverability rate to a verified 95 percent, reducing email turnaround time from three days to one, and getting a 50 percent faster time to insight. DMD Marketing’s success in empowering internal users and clients to monitor data quality proves it’s not true that “data quality software is complicated and just for experts.”
Embed data quality into every step of the pipeline
Talend Data Fabric is a single, unified suite of apps for collecting, governing, transforming and sharing data, whether on-premises, cloud or hybrid. With Talend Data Fabric, data quality and governance can be automated in every step of your data pipeline, from capturing data lineage and cataloging data; to data profiling, cleansing, and enrichment; to data stewardship throughout the data lifecycle. Clean, reliable data is produced through intelligent de-duplication, validation and standardization methods. Unlike legacy vendors or point solutions that have separate tools for data integration and data quality, Talend embeds data quality across the data value chain into all our products, so developers can create data integration jobs with data quality functions built-in.
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