How to Choose the Right Data Quality Tools
Without built-in data quality, your organization is throwing money out the window. According to the Harvard Business Review, it costs 10 times more to complete a unit of work with flawed data. Finding the right data quality tools has always been a challenge. By choosing and leveraging smart and workflow-driven self-service data quality tools with embedded quality controls, you can implement a system of trust that scales. Let’s explore some ways to find the right data quality tools for your organization.
Why standalone data quality tools won’t cut it
There is a plethora of standalone data quality tools on the market. Register for any big data tradeshow and you will discover plenty of data preparation and stewardship tools offering several benefits to fight bad data. But only a few of them cover data quality for all.
Standalone data quality tools can provide a quick fix but won’t solve problems in the long run. It’s common to see specialized data quality tools requiring deep expertise for successful deployment. These tools are often complex and require in-depth training to be launched and used. While these tools can be powerful, if you have short term data quality priorities, you will miss your deadline. Don’t ask a rookie to pilot a jumbo jet. The flight instruments are obviously too sophisticated, and it won’t be successful.
Building data quality into integrations
A proactive approach to data quality allows you to check and measure that level of quality before it even really gets into your core systems. Accessing and monitoring that data across internal, cloud, web, and mobile applications is a big task. The only way to scale that kind of monitoring across all of those systems is through data integration. This is why you need data quality tools that are capable of managing data in real-time.
Of course, avoiding the propagation of erroneous data by inserting control rules into your data integration processes is key. With the right data quality tools and integrated data, you can create whistleblowers that detect some of the root causes of overall data integrity problems.
Then you will need to track data across your landscape of applications and systems. That allows you to parse, standardize, and match the data in real time. You can organize the activity to check the correct data whenever needed.
On the other hand, you will find simple and often robust apps that can be too siloed to be injected into a comprehensive data quality process. Even if they successfully focus on the business people with a simple UI, they will miss the big part — collaborative data management. And that’s precisely the challenge. Success relies not only in the data quality tools and capabilities themselves, but in their ability to talk to each other. You therefore need to have a platform-based solution that shares, operates, and transfers data, actions, and models together.
Why data quality tools should be in the cloud
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 tools 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 successful data quality toolset in the cloud:
- 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 tools 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. Talend Data Fabric includes combined data integration, preparation, and stewardship that enables business and IT to work together to create a single source of trusted data in the cloud, on premises, or hybrid.
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