Data is the foundation of AI/ML and Analytics. Data hub provides a scalable platform that helps organizations get their data ready for analytics and AI. It can help organizations get ahead of their competitors and create new value for customers. However, if you don’t adopt AI/ML, it could also be a big threat to your business. As we have seen in the past few years, AI/ML is already used by many organizations as part of their digital transformation journeys to deliver better customer experience (CX), improve operational efficiency and drive growth through innovation in products & services offerings.
Data is the key ingredient for any kind of advanced analytics, machine learning or deep learning development. Data is also needed for developing predictive models in order to make predictions about future events based on historical data points.
Barriers to greater efficiencies with analytics and AI/ML
Data silos are a common problem in most companies, as data is often stored in multiple systems and repositories. Data quality can also be an issue, especially when it comes to unstructured data–such as text or images–which may not always have been captured consistently or accurately. Additionally, organizations need to ensure that they have the right governance and security policies in place before they start using analytics and AI/ML technologies on their datasets. Finally, many companies struggle with accessing all their relevant information from various sources at once; integrating disparate databases into one centralized hub would help solve this problem by enabling users across departments to collaborate more easily on projects related to AI/ML applications.
How organizations can get a more level playing field with a data hub
Organizations need a more level playing field with data to enable analytics and AI. Data Hubs are the foundation for enabling analytics and AI because they provide a scalable and secure way to store, access, and manage data. This means that all business users can get their hands on the same information at the same time without having to worry about whether they have rights or permissions on a particular dataset.
Furthermore, with a Data Hub in place you can easily spin up new tools or processes without disturbing the location of those tools or if they’re going to work together properly across different systems.
Why data management is so important for analytics
Data management is a critical enabler for analytics and AI/ML, as it ensures that data quality is high, which leads to better insights and more trustworthiness in the data used by organisations. Data management also helps you achieve greater agility in your business processes by enabling you to access relevant information quickly and easily – without having to wait hours or days for reports or queries from IT teams who may not be able to meet your needs at all times due to resource constraints.
Building on the foundations with AI/ML and Analytics
The Data Hub is the foundation for analytics and AI. It’s a cloud-based solution that enables business users to access trusted data for analytics and AI at speed, scale and cost. The data hub enables faster time to value for analytics and AI projects by providing access to relevant information in real-time while maintaining security across all layers of your organization. Analytics and AI are transforming how organizations generate value from their data.
The future of analytics and AI is bright and that it can help solve some of the most challenging business problems. Deployment of a Data Hub enables access to trusted data and provides security while ensuring that the data remains compliant with regulation since this is a logical architecture that represents how businesses can implement their own data platform.