Machine learning (ML) is rapidly helping businesses derive better insight and optimize their day-to-day operations. Yet an ML model is only as good as the data used to train and continually improve it. With the majority of enterprise companies already using a hybrid cloud, accessing domain-specific
The best decisions are made by extracting value from all the disparate data across your business. Yet aggregating data across external sources, regional silos and various forms of storage is not an easy challenge to solve.
Data-powered businesses need always-on access to data to keep operations
Purchasing options outside of the office are diverse and varied depending on what people want to buy, where and when they buy it, and what they need it for. While shoppers might have personal preferences, they don't limit ourselves to one retailer for all purchasing decisions. So why do that in a
While the proliferation of data will be readily apparent, deciding what to do in response will be less straightforward. The majority of workloads currently sit in traditional, on-premises environments but we’ll see many of them move to private and public clouds over the next over the next five
Big data isn’t just getting bigger. It’s getting more valuable. As companies work to unlock more value from their data, one of the biggest challenges to address is disconnected data silos. Big companies don’t have one data lake, they have data lakes, ponds and pools.
We’re living through the third great revolution in modern business. First came economies of scale, which we harnessed with the Industrial Revolution, the assembly line, and the creation of global markets. Second was network effects, seen most obviously in the rise of the Internet and the Web. Third
Quite often, we see that the need for data security and governance makes some organizations hesitant about migrating to the cloud. This is perfectly understandable given the types of data gathered and used by businesses today, the regulations they must adhere to on both a local and global level,
The benefits and pitfalls of different cloud deployment architectures can be intense topics of conversations spanning a wide range of industries. Some discussions foster new approaches to consider, and others make strong arguments as to which approach is well suited for different, industry-specific
Do you want to win the race to insight and beat your competition? If so, it’s time to rev up your analytics strategy. Explore how analytics platforms fueled by trusted information, designed for hybrid environments, and built on open technology can put you in the winner’s circle.
On the heels of several key announcements to broaden the IBM Cloud Data Services portfolio, see how a wide range of technologies can be implemented in a cloud-based, data warehouse architecture to support operational and analytical workloads.
Modern cloud solutions for analytics and information management will play a significant role in enabling and simplifying self-service access to the right data at the right time by all those who need it and have a right to use it. But this approach will require a process led by a strategy that takes
When it comes to cloud deployments of analytics and information management (AIM) technology, it is no longer a question of why to adopt cloud solutions but rather when and how. Today's modern cloud solutions are open for business - open for your data and open for your applications.