Data Quality Gets Smart
It’s time for expert machine learning and data quality
In today’s competitive landscape, data quality matters more than ever. That’s why data users across every industry need to take a more active role in data quality. Yet, most machine learning applications aren’t designed to build on the expertise of these data users. Data cleansing and entity resolution platforms typically require IT expertise. Design is technical and time-consuming. And, the real data experts are a step or more removed from the process.
There is a better way
New innovations combine leading-edge machine learning with intuitive tools that business users can use to review and interact with data presented in familiar formats. Machine learning occurs directly as a result of users’ actions, so entity resolution accelerates and efficiencies increase. With this smart approach, organizations can improve the quality of their data with less effort and better results.
Companies across every industry look for ways to improve data — and for good reason. Data is an invaluable strategic asset that can make or break long-term success. Superior data means better analytics, more insight, greater opportunity. It’s an engine for growth.
Data is complex and messy. It can also be expensive to manage, requiring vast IT resources and huge upfront costs. But the costs of inaction are greater. Unstructured, disorganized data can introduce significant risk, inefficiency and waste. A single mistake can cascade throughout your organization, hindering performance, delaying important reports and damaging valuable customer relationships.
Read more as this paper explores the latest solution to data quality, machine learning, which can put business users first and simplify entity resolution.