San Diego, United States – Teradata announced ClearScape Analytics, the significantly expanded and newly named analytics capabilities that span the entire suite of Vantage products. Teradata Vantage’s analytics have long been the cornerstone of the platform’s appeal to enterprise customers for its ability to accelerate data insights and time to value.
With these new capabilities, Vantage customers can now take advantage of the most in-database analytic functions anywhere in the market and critical artificial intelligence/machine learning (AI/ML) model management tools (i.e., ModelOps) to meet the growing analytic demands of their organisations.
These newly released features elevate Teradata’s analytics capabilities even further beyond competitors by introducing more than 50 new in-database time series and ML functions, and integrated ModelOps that are designed to rapidly operationalise AI/ML initiatives. This new functionality – in combination with the launch of Teradata VantageCloud Lake, Teradata’s product based on all-new, next-generation cloud-native architecture, also announced – is intended to provide customers the ability to activate massive amounts of data and solve complex business challenges with a robust library of open and connected analytics tools designed to provide autonomy, ease of access, and real-time insights.
“Data is only as valuable as its ability to be synthesised for actionable, real-world insights that drive better outcomes,” says Hillary Ashton, chief product officer at Teradata. “Over its 40+ year history, Teradata has been laser-focused on helping customers extract the most value from their data with consistently high performance, unmatched scalability, and a trove of analytic functionality. With the launch of Teradata VantageCloud Lake and the availability of ClearScape Analytics across the VantageCloud platform, Teradata is continuing its tradition of listening to its customers and helping them accelerate their digital transformations by providing a data platform that is born in the cloud with end-to-end support for advanced analytics across the cloud ecosystem.”
ClearScape Analytics’ new in-database time series functions span the entire analytics lifecycle, from data transformation and statistical hypothesis tests, to feature engineering and machine learning modeling. Because these functions are built into the database, they are highly performant and require limited data movement. This can represent significant cost and friction reduction, particularly when an organisation wants to apply time-series analytics on large volumes of data such as millions of products or billions of sensors. When the results of these analytic functions, such as forecasts, are stored inside the database, organisations can easily integrate that with other data. For example, a manufacturer could integrate a sensor anomaly detection score with the location of a machine and find out the location of a predicted failure, or a global retailer could integrate forecast results with price to predict revenue.
Additionally, with ClearScape Analytics, complex machine learning functions can be easily integrated into analytic pipelines – a collection of related operations that go from data preparation all the way through modeling and deployment – but packaged together to address specific problems. For example, a classification pipeline can be tweaked and tuned specifically for fraud detection that might go into a financial application, or a time series pipeline might be used for demand forecasting in a retail or manufacturing scenario. And with ModelOps embedded into ClearScape Analytics, organisations will be able to quickly scale AI/ML initiatives to unlock the full value of their investment while mitigating risk. ModelOps plays a key role in model governance and risk management, which will become increasingly important as companies send more models into production.
“Teradata’s comprehensive analytics offering has long helped organisations make the most use of their data, regardless of where it sits within an organisation,” says Dan Vesset, group VP, analytics and information management market research at IDC. “Teradata’s continued investment in this area with ClearScape Analytics underscores its commitment to help customers operationalise analytics and AI/ML at scale to solve the most pressing business challenges – from real-time customer personalisation to supply chain optimisation – across all industries.”
ClearScape analytics details
The recent additions to ClearScape Analytics significantly expand the overall capabilities of Teradata VantageCloud and are intended to deliver value at every stage of the analytics lifecycle.
Key benefits of ClearScape Analytics include:
- Solve more complex problems with newly added in-database analytics capabilities – *NEW* Teradata’s recently expanded in-database analytics library includes more than 50 new time series functions as well as a broad array of machine learning capabilities that are designed to support full end-to-end machine learning pipelines. These native functions make it possible to process machine learning at scale. Teradata VantageCloud is designed to not only support scaling massive amounts of data, but also to enable larger model complexity, including significantly more model variables, for deeper insights.
- Deploy models confidently with integrated ModelOps – *NEW* Despite the significant investments most organisations put into AI/ML, most predictive models are never implemented into production. Teradata VantageCloud makes it easier than ever to operationalise these investments using its governed ModelOps tool which is designed to supply the framework to manage, deploy, monitor, and maintain analytic outcomes. Teradata VantageCloud ModelOps includes capabilities such as auditing datasets, code tracking, model approval workflows, monitoring model performance, as well as alerting when models become non-performing. ModelOps can be leveraged to schedule model retraining as organisations drive towards autonomous retraining based on data drifts.
- Activate more resources by allowing experts to work with tools of their choice – *ENHANCED* With VantageCloud, data teams can use the languages and tools of their choice (e.g., Dataiku, Python, H2O.ai, etc.) which makes it easier for businesses to tap into analytic talent across the organisation. Models developed outside of Teradata VantageCloud can be imported directly into Teradata VantageCloud to be run in parallel and at scale. In addition, integration with services such as Amazon SageMaker allow data science teams to tap into the expanding array of data science services in the cloud.
- Drive more collaboration and efficiency with the enterprise feature store – *ENHANCED* Greater value can be achieved when data teams work collaboratively across the organisation to drive business results. By leveraging Teradata’s feature store to foster collaboration and reuse models, organisations often see a significant reduction in the amount of effort spent on data preparation and feature definition. This makes trying out new ideas easier, faster, and less expensive.
Follow us and Comment on Twitter @TheEE_io