Shorten your path to AI with Watson Knowledge Catalog

Director and Distinguished Engineer, Offering Management, IBM

Data can be an organization’s most valued asset, providing insights that help strengthen business. Knowing what works and what does not can help you invest more resources in what would work in the future.

But there are two main challenges that might impede you from realizing the true value of your data and slowing your journey to adopting artificial intelligence (AI).

Challenge 1: Inefficient data management

The first challenge is the inefficiency in managing all the disparate data that flows through your enterprise. You need to ensure a fluid handoff and sharing of data and metadata among business analysts, data scientists, data stewards and data engineers.

Business analysts should collaborate with data scientists to solve business challenges and streamline operations using AI. To achieve their shared goal, they need to know what data, algorithms and models are available; if they can trust them; and a way to share updates across teams.  

The data steward ensures data assets are inventoried, documented and follow governance rules. Any new data source must be queued for curation, documented and published for use across compliance and data science teams. Should a data source change, the steward should know the impact of those changes, both from a lineage perspective as well as what operational models could be impacted by the change.

Data engineers build transportation systems so new data source requests are fulfilled. They aim to ensure data is delivered efficiently and reliably; meets operational models and targets; and fuels AI objectives. When new data sources need to be integrated, it is critical for the engineer to know its purpose while ensuring safe passage to its destination. And once new data is available, the data engineer needs to share its availability.

In many companies, analysts, stewards and engineers reside in different organizations. Their teams each have unique agendas and needs, and they may have completely different definitions for the same data that they deal with. 

Challenge 2: Finding the right tools for all data users

The second challenge involves the plethora of options available in the market to solve for each data user’s needs. There are solutions that aim to help data scientists, data stewards or data engineers. Some products focus on the collection and governance of metadata referred to as metadata management while others focus on the consumption of metadata for use by business analysts and data scientists.

The knowledge catalog

A unified enterprise data catalog can enable any user, including business analysts, data scientists, engineers and stewards to discover and consume data from wherever and whenever they need it and in whatever structure or form they need it. According to a Research and Markets report, "the data catalog market size is expected to grow from $210.0 million in 2017 to $620.0 million by 2022." 

The knowledge catalog helps address the two main challenges to help you realize the true value of your data. IBM delivers a common set of catalog services so you can use open source metadata sharing services and embed base catalog services in all of your offerings. A knowledge catalog can help tailor user experiences to make the processes of harvesting metadata, curating assets and knowledge sharing as efficient as possible. 

When automation kicks in, efficiencies improve. When efficiencies improve, you are on a faster path to value through the democratization of data.

A knowledge catalog combines the capability of metadata management with robust, role-based user experiences. You can significantly improve efficiencies by eliminating time wasted looking for knowledge assets and ensuring only approved, quality assets are used.

The knowledge catalog can serve multiple stakeholders, eliminating inefficiencies associated with “lost in translation” issues. It can serve as the single, trusted source of a company’s inventory of knowledge assets. This includes data sources, machine learning models, business intelligence reports, business terms, regulatory compliance and governance assessments. 

You can combine the capabilities from the IBM Information Governance Catalog (IGC) with the innovative user experiences from Watson Knowledge Catalog, which is designed for the consumption of metadata for active use.

Interested in seeing how a knowledge catalog can shorten your path to AI? Start now with a no-cost tour of Watson Knowledge Catalog.