Enterprises are facing significant challenges when it comes to maintaining reliable, up-to-date data that is readily available to help them generate important business insights. The problem isn’t that traditional solutions don’t work–it’s that they are too difficult to scale. These approaches are designed to perform well for a small number of data sources, but enterprises today are dealing with a tremendous volume of datasets that they need to be able to analyze quickly and efficiently.
Traditional approaches to data mastering are slow, and don’t acknowledge that data environments and end user requirements are constantly changing. It can take months or years to complete mastering projects using only deterministic rules. And, as we have talked about extensively, these approaches place an unnecessary burden on data scientists, requiring them to spend a significant amount of time preparing data rather than focusing on more valuable work.
These challenges can be addressed, however, by applying the same agile approaches that have been used for many years now in software development to data management. Agile Data Mastering (ADM) is an approach that connects people, processes, and tools–treating data unification as an iterative process. By engaging stakeholders early and often, ADM enables teams to easily correct issues, accommodate emergent requirements, and quickly react to changing data.
There are several important features of ADM that set it apart from traditional data management solutions. The first is a reliance on machine learning and automation. Instead of using only deterministic rules, ADM incorporates probabilistic machine learning models that handle the heavy lifting of identifying relationships within data.
The second key feature of ADM tools is a collaboration between humans and machines. With ADM, subject matter experts are used to train and validate machine learning models, so the accuracy of the tool improves over time. This means that, inevitably, the amount of time a human needs to spend performing a specific task will decrease as the model improves.
The Benefits of Agile Data Mastering
Agile Data Mastering drives key benefits for enterprises, enabling them to change the way they manage and make use of their data.
ADM enables enterprises to:
- Scale easily: The combination of machine learning and human expertise enables organizations to easily and quickly integrate datasets from multiple sources–so that you can scale as needed without compromising accuracy.
- Improve time to value: By leveraging machine learning, ADM tools can deliver results in days instead of months or years.
- Improve data science efforts: By radically reducing the amount of time data science teams spend on data preparation, they can be freed up to focus on more specialized, high value work.
- Solve the ‘too hard’ problems: When the ‘cost to know’ is greatly reduced, the projects that organizations have deferred for years because of the anticipated high costs and risks can finally be addressed.
- Respond to the unexpected: Digital transformation will never be straightforward. Unanticipated questions and challenges will inevitably arise, but an ADM capability provides the capacity to respond effectively when the unexpected happens.
To learn more about Agile Data Mastering and the data challenges it can help solve, download our ebook below. Or to learn more about how Tamr uses Agile Data Mastering to help our customers drive analytic outcomes, schedule a demo.