Big Data Quality must always be verified to ensure that data is safe, accurate, and complete. Data is moved through multiple IT platforms or stored in Data Lakes. The Big Data Challenge: Data often loses its trustworthiness because of (i) Undiscovered errors in incoming data (iii). Multiple data sources that get out-of-synchrony over time (iii). Structural changes to data in downstream processes not expected downstream and (iv) multiple IT platforms (Hadoop DW, Cloud). Unexpected errors can occur when data moves between systems, such as from a Data Warehouse to a Hadoop environment, NoSQL database, or the Cloud. Data can change unexpectedly due to poor processes, ad-hoc data policies, poor data storage and control, and lack of control over certain data sources (e.g., external providers). DataBuck is an autonomous, self-learning, Big Data Quality validation tool and Data Matching tool.
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Globally, teams in risk, procurement, and compliance are under pressure to manage geopolitical risks and business risks. Third-party risks are impacted by the complexity of domestic and international businesses, as well as complex and diverse regulations. It is crucial that companies proactively manage third-party relationships. This cutting-edge platform, powered by D&B Data Cloud's 520M+ Global Business Records with 2B+ annual updates for third-party risks, is an AI-powered solution that mitigates and monitors counterparty risk on a continual basis. D&B Risk Analytics uses best-in class risk data, including alerts for high-risk purchases and match points of more than a billion. This helps to drive informed decisions. Intelligent workflows allow for quick and thorough screening. Receive alerts on key business indicators.
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LinkageWiz
Robust algorithms for probabilistic data matching leverage shared identifiers like names, birth dates, gender, addresses, Social Security Numbers, and business names, among others. These algorithms facilitate the importation of data from various desktop and corporate database systems, enhancing versatility. Such data matching software can identify up to 99% or more of all possible matches. For businesses, this capability can translate into substantial additional revenue or significant cost reductions, while also improving fraud detection efforts. In the realm of medical research, effective data matching can determine whether a project succeeds in yielding meaningful findings or ultimately falls short. LinkageWiz stands out as an efficient and user-friendly solution, offering exceptional value by integrating many features typically found in separate products into one comprehensive package, making it a preferred choice for various applications. Furthermore, its streamlined interface allows users with varying levels of expertise to navigate the software with ease.
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Data Ladder
DataMatch Enterprise (DME) is Data Ladder's entity resolution and data matching platform. It identifies records that refer to the same person, business, or entity across disconnected systems, then links and consolidates them into a single accurate record. Core functions include data profiling, standardization, matching, deduplication, and merging, supporting use cases such as Customer 360, KYC, fraud detection, and master data management.
The platform is available through a no-code visual interface for business users and a REST API for developers, allowing the same matching engine to be embedded in applications, data pipelines, or AI agent workflows. Match results are rule-based and traceable, so users can see the specific logic behind each linked record rather than a single opaque score.
Recent additions include entity graphs for visualizing connected records, live search for real-time matching, and Docker as a deployment option in addition to cloud and on-premises environments.
Independent benchmarking across 15 studies shows DME identifying 5 to 12% more matches than comparable tools, with fewer false positives and accuracy up to 99%. In a large-scale test, it processed 10 million records in 41 minutes.
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