Data Quality Gets Smart
It’s time for expert machine learning
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 data quality with less effort and better results.
Companies across every industry look for ways to improve data quality — and for good reason. Data is an invaluable strategic asset that can make or break long-term success. Quality data means better analytics, more insight, greater opportunity. It’s an engine for growth.
Achieving quality data isn’t easy. 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.
Given the stakes, it’s no surprise that non-IT business users need to take a more proactive role in data access and management. Marketing and operations teams especially want greater control to manipulate data for specific business purposes. However, all business units now have a vested interest in achieving the highest quality data.
For these reasons, we’re seeing a shift away from traditional IT personnel exclusively managing data quality processes. Gartner refers to this trend as “the democratization of data quality,” with businesses looking for tools and technologies more useful for the data novice and non-expert. Complicated data quality processes no longer suffice in a fast-paced business environment.