You’ve heard it a million times, bad quality data in, bad quality insights out. In fact, recent research has found that poor quality data actually costs companies millions of dollars per year.
However, ensuring data is not only accurately collected, but retains its integrity over time, can be deceptively difficult. While the first and most important step is to create a sound data governance framework, there are also a number of tools and solutions available to help you cleanse, enrich and protect enterprise data. Here’s a look at 5 technology subsets that make up the data quality management technology ecosystem.
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Data Cleansing Tools
Data cleaning solutions seek to remove or fix corrupted, incorrectly formatted, duplicate, incomplete or irrelevant data within a dataset. Instead of correcting data once it's already entered into a database, the solutions prevent bad data from getting into the system all together by either removing or transforming (i.e. correcting) it during ETL processes. In simpler terms, data cleansing prepares data for analysis and storage.
Some examples of specific data cleansing tools include Data Ladder, OpenRefine, Tibco Clarity, and Trifacta.
Data Enrichment Solutions
In order to remain viable, data must be constantly maintained. Data enrichment refers to the process of appending or otherwise enhancing collected data with relevant context obtained from additional sources. In other words, data enrichment tools automatically update and/or complete existing data.
Data enrichment is especially impactful in sales and marketing scenarios where client content information is constantly shifting. Instead of updating client data all at once every year or two, data enrichment tools leverage RPA and other AI techniques to continuously update customer data.
Trifacta [Alteryx], Winpure and tray.io are just some of the companies that offer data enrichment solutions.
Data Validation Services & Automation
One of the risks associated with system migrations is data loss and corruption. In order to ensure data was accurately transferred, organizations use data validation tools to crosscheck input data against source data.
As the volume of data transferred is often too large to manually review, most organizations use automated data validation solutions such as experian, Infosys and iCEDQ.
Data Governance
Data governance platforms act as a centralized hub for all things data-related. They allow organizations to manage the availability, usability, security, and storage of enterprise level data from one place. They also automate audits and data capture, improve workflows, and prove compliance through documentation.
A key component of data governance platforms are data catalogs, an organized inventory of data assets in the organization. This repository facilitates dataset search and retrieval so that users and systems can easily find the information needed for business.
Examples of data governance platforms include talend, Collibra Data Governance, Avo and Alation.
Data Quality Platforms
Data quality platforms are centralized command centers for tracking and managing data quality throughout the entirety of its lifecycle. They sit on top of and coordinate data cleansing, validation, metadata management and enrichment processes. Data quality platforms also provide a unified view into data quality metrics, flagging areas of concern.
But that’s not all, every data quality platform has its own unique approach and value proposition. The challenge is identifying which one best aligns with your needs. Here are a handful of leading ones:
IBM InfoSphere QualityStage
IBM InfoSphere® QualityStage® is designed to support your data quality and information governance initiatives. It enables you to investigate, cleanse and manage your data, helping you maintain consistent views of key entities including customers, vendors, locations and products. The solution helps you deliver quality data for your big data, business intelligence, data warehousing, application migration and master data management projects. Also available for IBM System z®.
Trillium Quality
The suite’s data integration capabilities break down data silos and ensure data stays fresh for both IT operations and business insights. It integrates data from a wide range of sources, even complex mainframe and IBM i data, with next-generation on-premises and cloud data platforms. And it offers a full range of integration methods – from batch to real-time change data capture. Precisely Trillium differentiate itself with its modular, plug-and-play approach.
Claravine
Claravine’s Data Standards Cloud makes it easy for teams to standardize, connect, and control data collaboratively, across the organization. Leading brands use Claravine to take greater ownership and control of their data from the start, for better decisions, stickier consumer experiences, and increased ROI.