Rather than shying away from ever increasing amounts of data, today’s businesses are using data-driven decision-making as a secret weapon. Learn more about why logical data warehouses are the natural next step for the analytics culture.
No less than traditional scientists, data scientists need a guiding strategy for solving problems. Such a methodology should directly address the problem at hand and should provide a framework for obtaining answers and results. Learn more about the Foundational Methodology for Data Science and how
Data visualization techniques can give data scientists a vital tool for representing the data that analysts and line-of-business users need to make strategic decisions. Discover how a few simple considerations of a specific data set in a real-world use case enables data scientists to implement cost
Agility is all about speed of response and the flexibility to turn on the proverbial dime in any new direction that a business requires. It’s the ability to operate at any scale, speed or scope of business that planning and circumstances may require. A true analytics platform is agile enough to
The modern data warehouse (DW) lives in the cloud and is rapidly evolving into an in-memory platform for high-performance in-database analytics. As evidenced by IBM’s launch last year of dashDB and the latest enhancement release to the service, the fully managed in-memory cloud DW is already a
Apache Spark will become a core technology in the logical data warehouse (LDW), and its sweet spot is as the workbench of choice for data scientists who interactively and iteratively explore, build and tune statistical models for machine learning, graph and streaming analytics.
How do you define a digital business? We organized a recent CrowdChat to discuss advantages, disadvantages, infrastructure needs, analytics, security and other key concerns for digital business. The participants, who included industry experts, also talked about how organizations need to change to
Big data without context is pretty much useless, especially when that context can fluctuate so widely—which is why the role of Hadoop in creating accurate analytics is crucial for deriving value from big data.
There’s no substitute for the real thing, especially if it’s Parmesan cheese from Italy’s Parmigiano-Reggiano region. Cheese makers tell how modern analytics supplements a 1,000-year-old process to ensure the quality and authenticity of this tasty product.
Without doubt, cloud-based data presents a wealth of potential information for organizations seeking to build and maintain competitive advantage in their industries. To fully realize the benefits of the cloud, organizations need to have a good hybrid information governance model in place. A key
As opposed to big data, which focuses on analysis of large and varied types of content, the concept behind digital business is the convergence of historical, real-time and mobile proximity data that offer fleeting digital business moments that can be taken advantage of by a digital business. This
Two years ago, everything was big data—nothing else mattered. And big data was and today still is the foundation for a successful business analytics strategy. Without data, you have no analytics. But with big data, you have the potential for great analytics. Why? Because big data is all types of