Intelligent, flexible classification capabilities within IBM Watson Data Platform help users govern, prepare, integrate and analyze data quickly
We hope you have been having a great experience discovering, cataloguing and governing data with IBM Data Catalog as part of IBM Watson Data Platform. We’d like to inform you that the Data Catalog service is now generally available (GA), and all Beta plan instances will be retired on January 31, 2018.
In the world of Data Science, the time required to transform data to good quality is a recurring barrier towards gaining insights. Data scientists or analysts will spend a bulk of their effort in cleaning the data using a variety of handwritten scripts. IBM Watson Data Platform’s data refining tools aim to reduce the pain associated with creating good quality data. The tool has an intuitive user interface and templates enabled with powerful operations to shape and clean data. It also provides metrics and data visualization which aid in every step of the process. Incremental snapshots of the results are provided allowing the user to gauge success with each iterative change. Saving, editing, and running the steps within projects provide the ability to refine data of almost any size within the Watson Data Platform.
Machine learning is one of the most exciting areas of data science, with enormous potential to transform data into the pure gold of competitive advantage. Data scientists can seem like wizards when their models first accurately predict customer or market behavior, or reveal valuable insight from previously untapped data sources.
IBM Watson Data Platform, a platform designed to make data discoverable and accessible to data professionals around the world. Central to this is our new IBM Data Catalog offering, which enables companies to build a 360 view of all information, making data accessible for self-service analytics and data science initiatives.
We are excited to announce the beta of the IBM Analytics Engine, providing a single Hadoop and Spark service under the Watson Data Platform. It makes it easier for data engineers, data scientists and developers to develop and deploy analytics applications. With integration through Jupyter notebooks in Data Science Experience, IBM Analytics Engine provides the foundation for executing data science and machine learning workloads. The IBM Analytics Engine utilizes the Hortonworks Data Platform as the underlying Hadoop distribution, providing access to a market leading open source Hadoop distribution.
Developers want to spend their time building great solutions, not managing infrastructure. Unfortunately, when companies host their own database solutions, concerns about performance, tuning and security often fall to developers, which means they must spend more time monitoring their data layer and less time writing code. That’s why we developed Compose Enterprise, which provides businesses with a suite of fully managed Database-as-a-Service (DBaaS). Every database is version controlled, patched regularly and maintained by a team of engineers, so developers can skip the admin tasks and work on building performant apps.
Ten years ago, Chief Data Officers (CDOs) were a rarity. Large corporations such as Visa, Capital One and Yahoo! led the way in appointing CDOs, but the job title had yet to become mainstream. Then the global financial crisis of 2007-2008 hit. Organizations heard the alarm bells ring, and CDOs – suddenly in high demand – were asked to help align operations with a raft of new regulatory requirements around data governance and reporting.
Helping your data scientists work more productively is a key priority. The answer is to use automation to give them more time for analysis without compromising the quality of the data they use. IBM Data Catalog, a new beta solution that’s part of Watson Data Platform, offers tools to automate and simplify data discovery, curation, and governance.