Teradata introduced some enhancements to its Vantage platform last year in which they expanded its analytics functions and language support, and strengthened tools to improve collaboration between data scientists, business analysts, data engineers and business personnel. Some of the key enhancements included expanding the native support for R and Python, extending the ability to execute a wide range of open-source analytics algorithms, and automatic generation of SQL from R and Python code. These updates are included to reduce data silos, enabling a wide range of data and analytics personas to collaboratively run complex analytics in a self-service manner.
The amount of data flowing into organizations is growing exponentially, creating a need to process more data more quickly than ever before. Our Data Preparation Benchmark Research shows that accessing and preparing data continues to be the most time-consuming part of making data available for analysis. This can potentially slow down the organizational functions which depend on the analysis results. Trying to get ahead of the backlog with incremental improvements to existing approaches and traditional technologies alone can be frustrating.
Analytics and data provide visibility into an organization’s past, present and potential performance. However, not all organizations are using analytics that provide timely insights — insights that not just reflect what happen but direct a successful course for the future. Demand for personalized and relevant insight only intensifies in a black-swan event. To maintain business continuity in times of pressure, it is critical that organizations not waste any time or resources when using analytics and data to optimize operations and decision-making. Just having an analytics and data-first mentality and operating in the cloud is insufficient for success, as those are just part of an effective data and analytics effort. Organizations also should include data science and machine learning that can provide an excellent digital experience; unfortunately, this is no simple task.
Topics: business intelligence, embedded analytics, Analytics, Business Intelligence, Collaboration, Internet of Things, Data, Digital Technology, natural language processing, Conversational Computing, AI and Machine Learning
Having effective analytics enables businesses to understand far better than ever before the data they’re collecting, and to do so in greater volumes and more forms. These new capabilities are especially relevant to sales organizations. When applied to sales data, analytics can help sales teams achieve quotas and forecast more consistently, as well as understand the impacts of incentives and maximize the potential of territories, all of which help improve sales performance. These benefits provide the foundation for a business case to adopt analytics tools that generate information to guide actions and decision-making for sales organizations.
Topics: Customer Experience, Voice of the Customer, business intelligence, embedded analytics, Analytics, Collaboration, Data Governance, Data Preparation, Information Management, Internet of Things, Contact Center, Data, Digital Technology, Digital Commerce, blockchain, natural language processing, data lakes, Intelligent CX, AI and Machine Learning, subscription management, agent management
We are happy to share some insights about IBM drawn from our latest Value Index research, which assesses how well vendors’ offerings meet buyers’ requirements.