AI and ClearScape Analytics

Move from Being an AI Experimenter to an AI Achiever with ClearScape Analytics and Vertex AI

Danielle Stane
Danielle Stane
2023년 2월 23일 5 최소 읽기

AI has moved from silos to the board room. According to a recent AI survey, among executives of the world’s 2,000 largest companies (by market capitalization), those who discussed AI on their earnings calls were 40% more likely to see their firms' share prices increase.  However, only 12% of the organizations are AI achievers – companies that have a differentiated AI strategies and the ability to operationalize for value. In fact, most companies (63%) are AI experimenters – companies that lack mature AI strategies and capabilities to operationalize. 

To become an AI achiever, companies need capabilities to operationalize at scale to achieve business value. Operationalizing AI at scale refers to the ability to implement and deploy AI solutions across an organization in a consistent and efficient manner. It is more than just having a well-defined AI strategy. This includes having data management at scale, ability to use and integrate best possible AI models, and make these models actionable. 

With ClearScape Analytics and Google Vertex AI, you can move seamlessly from experimenting to achieving business value to fuel your AI-led digital transformation. ClearScape Analytics refers to the analytic capabilities available within Teradata VantageCloud. It helps users scale AI/ML quicker and more effectively to solve their most complex challenges, reduce cost and friction, and accelerate time-to-value throughout the organization. In conjunction, Google Vertex AI helps build AI-models with minimal expertise.  

The combination will allow users to operationalize Vertex AI models at scale with the robustness of Teradata VantageCloud.  

ClearScape Analytics provides better answers, faster results, and value at scale.  

ClearScape Analytics provides three main advantages to users: better answers, faster results, and value at scale. ClearScape Analytics has the most extensive variety of in-database functions and an open ecosystem that enhances the opportunities and methods available to discover the highest quality of answers and provide the diversity of ideas needed for success. Its unleashed performance delivers unmatched analytics speed and execution. By expediting project start-up and enabling easier access to analyzed data, models can be moved into production faster than ever before. Analytics featuring broad accessibility and ease of implementation empowers users throughout an organization to accelerate value with greater AI/ML adoption.  

Google Vertex AI helps build high-quality AI models with minimal expertise. 

With Google Vertex AI, users can take advantage of various cutting-edge algorithms to build AI models in less time. It can also help reduce training time and cost with an optimized AI infrastructure.  

Linking the capabilities of ClearScape Analytics and Vertex AI  

Combining the power of ClearScape Analytics and Google Vertex AI enables you to move from AI-experimentation to AI-operationalization in a seamless way. The range of capabilities can be accomplished in 3 easy steps. 

  1. Accelerate data preparation by quickly integrating disparate datasets that span diverse environments, data lakes, and object stores. Using the powerful capabilities of ClearScape Analytics in-database analytics, data scientists can transform data into rich, reusable analytic datasets. 
  2. Build and train high quality ML models fast with Vertex AI using analytic datasets prepared with ClearScape Analytics 
  3. Operationalize the Vertex AI models at scale in VantageCloud. The API integration with Google Vertex AI offers VantageCloud users direct, transparent, and real-time access to all their models, which in turn delivers the crucial insights needed to drive business outcomes.  

Let’s dive into a business use case that demonstrates how these combined tools deliver AI achievement. One of the main goals for any company is to preserve and increase their customer base. Thus, customer churn identification and prevention are important part of a differentiated AI strategy.   

To gain insights into why customers are churning, companies need to integrate data from various sources, such as customer master data, digital journeys, customer payment transactions, and social media. This requires a comprehensive approach to data integration that ensures data is collected, transformed, and loaded into the analytics environment in a consistent and efficient manner. 

Data preparation is also a critical aspect of the data analytics process. Before data can be analyzed, it needs to be cleaned, transformed, and enriched with additional features. This often involves the use of advanced analytics techniques to identify patterns and relationships in the data, and to create new features that can be used in the analysis. ClearScape Analytics can help accelerate the data preparation process by rapidly integrating and creating various customer-related features, also known as the analytic dataset. 

The analytic dataset created using ClearScape Analytics can be transferred seamlessly to Google Cloud, and then used to train an AI-model to predict customer churn. The AI-model can be trained using a variety of machine learning algorithms, such as logistic regression, decision trees, random forests, and AutoML, to name a few.  These algorithms can be used to identify patterns and relationships in the data that can be used to predict which customers are most likely to churn. 

Once the AI-model has been trained, it is ready to be operationalized. The primary challenge for many businesses is operationalizing these scores and using them to drive real-world outcomes. However, with ClearScape Analytics, the AI model score can be seamlessly integrated with operational data. This involves deploying the model to a VantageCloud production environment, where it can be used to predict customer churn in real-time. The business user can use VantageCloud SQL to fetch customer related data and specify the Vertex AI model endpoint. The SQL query will score the customer data with the Vertex AI model and return customer churn predictions and predicted probability.  The predictions can then be integrated with operational data like contact data or help desk information. With this additional information, business users can operationalize the AI model scores, such as sending offers to existing customers prevent churn. This can lead to significant improvements in customer satisfaction, loyalty, and overall business performance. This final step turns the work and insights gathered into actions and differentiates the AI Experimenters from the AI Achievers. 

In addition, ClearScape Analytics also provides a range of capabilities, such as the ability to monitor the performance of the model, and the ability to scale the model to handle large volumes of data. 

Customer churn prediction is just one of the various use cases which you can operationalize using ClearScape Analytics and Vertex AI. Other common use cases for ClearScape Analytics and Google Vertex AI include fraud detection, predictive maintenance, and supply chain optimization. Each of these use cases involves the analysis of large amounts of data, and machine learning algorithms to identify patterns and relationships that can be used to make more informed decisions, unlock full potential of AI, and drive value for stakeholders. 

In conclusion, the integration can help accelerate your AI-enabled digital transformation journey by operationalizing sophisticated Vertex AI models with the scalability and robustness of VantageCloud ClearScape Analytics in a variety of use cases. Utilizing this integration to its fullest potential will push users to move from AI Experimenters to AI Achievers. 

약 Danielle Stane

Danielle is a Solutions Marketing Specialist at Teradata. In her role, she shares insights and advantages of Teradata analytics capabilities. Danielle has a knack for translating complex analytic and technical results into solutions that empower business outcomes. Danielle previously worked as a data analyst and has a passion for demonstrating how data can enhance any department’s day-to-day experiences. She has a bachelor's degree in Statistics and an MBA. 

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