From reading the news headlines of yet another retail chain closing its stores, one can easily be left with the impression that we’re in a retail apocalypse. But in reality, the overall retail industry is very strong and healthy—especially online.
What we’re witnessing however, is a transformation
Gartner analysts Merv Adrian and Donald Feinberg in a February 2018 report predict that “by 2022, more than 70 percent of new applications developed by corporate users will run on an open source database management system.” This isn’t surprising given the influx of multiple types of data from
The conversation around data preparation has been evolving. What started as a push for self-service access for specific use cases has now expanded to operationalizing a data pipeline across the enterprise. The goal is to create efficiencies and eliminate workflow silos to propel data strategy
Let’s say you’re the Chief Technology Officer of a bank or retailer struggling to infuse AI that aims to improve customer experiences. You likely face three main challenges:
Data sprawl: Your customer data is currently on multiple clouds, including on-premises and a cloud data lake storage
The best data catalogs can automate the process to collect, classify and profile data to ensure the highest standards of quality. Here are three popular use cases detailing why companies are moving towards IBM’s Watson Knowledge Catalog.
The fusing of analytics with leading technologies can unlock significant business value and bring new transformation opportunities for enterprise companies. In order to be successful, analytics-based initiatives such as AI and the Internet of Things (IoT) need massive amounts of big data—and also
Choosing the right data management solutions as the foundation for AI is crucial. Enabling AI optimization and usability is paramount, as is easy scalability to accommodate the increasing amount of data used by AI applications. This is true no matter where you store your data: on-premises, in the
Nearly every business is under competitive, disruptive, and regulatory pressures. As companies face digital transformation and modernization to meet their customers’ expectations, leveraging data and AI at the speed of business can be the biggest differentiator.
However, according to MIT Sloan, 81
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
For the past nine years, Stack Overflow, a question-and-answer website for programmers, has polled developers to understand what technologies they are using and to find out what technologies they want to work with next. This year, the nearly 90,000 survey participants revealed that, once again,
In a previous blog, I explained how data science capabilities, massive parallel processing (MPP)
and usability improvements in data warehouse appliances can help the bottom line—and why old-fashioned architectures might not cut it. But what does that look like in practice?
Research firm Quark +