Accelerate to AI, data-driven business with IBM Watson Data Platform
IBM announced a series of upgrades and new offerings to Watson Data Platform, an integrated set of tools, services and data in the IBM Cloud that enables data scientists, developers and business teams to gain intelligence from data.
These new features will provide data professionals with the foundation they need to safely and securely share data across teams and environments, in order to more easily analyze and prepare data for AI applications.
By powering self-service data access and unifying data for visibility across all sources, Watson Data Platform addresses many of the problems data professionals encounter day-to-day, including inaccessible and disparate data sets, and discrete tooling that obstructs collaboration.
These new offerings include:
• Creates a complete and easily searchable index of all structured and unstructured data living in existing systems, cloud platforms and IoT data streams.
• Provides rules-based governance for controlled data access and a detailed view of who should see and work with different pieces of data to help teams stay within compliance.
• Automatic data profiling and classification, using machine learning to categorize data and assign metatags to quickly organize it into searchable stores.
• Enabled by the open standards base of Watson Data Platform, as well as a wide range of more than 30 connectors to both open and proprietary systems, which brings together a view of all data and organization touches.
• Eases the complex job of preparing, cleansing and processing data to be shared broadly, as well as for ingestion into AI and machine learning apps.
• Provides quicker discovery, visualization and sharing of data, enabling data scientists to work in real-time with developer and business teams to build new data-based models and functions.
• A combined Apache® Spark™ and Apache® Hadoop™ service that acts as an intelligent repository for data, enabling users to understand the size, value and creation of each piece of a large dataset.
• Allows developers and data scientists to collaborate to view and build with the intelligence living in data workloads, without the need to manage the infrastructure intricacies behind it.
• Powered by IBM Cloud Object Storage, the scalable, persistent data storage layer that makes data readily available for processing and analysis. The combination of Cloud Object Storage and Analytics Engine separates compute and storage, enabling companies to take greater advantage of the agility and economics offered by the cloud.
Watson Data Platform in action
How do these new offerings help companies solve complex problems – from increasing customer retention to innovating to bring new products to market? A potential scenario for this new functionality could involve a retail company that wants to explore customer-buying patterns and find new ways to increase sales:
• A data scientist would need to view purchase transactions that live in both on-site servers and cloud databases, shape it into seasonal or demographic-specific categories, and analyze the findings in correlation with customer feedback.
• Tapping into the features of Data Catalog, Data Refinery and Analytics Engine, the data engineer (or data scientist) could access the data where it lives, shape it as needed, and build a machine learning model, all while knowing they’re only working with data they have proper clearance to access (even potentially sensitive information like credit card data).
• This model could then be shared with a developer to quickly deploy it into an AI app that markets season-specific clothing based on customer preferences.