At IBM, we understand both the exponential benefits AI can offer your organization as well as the unique challenges implementation can present. In 2018, we formalized the AI Ladder to provide a prescriptive approach to successful AI, and to impart lessons we've learned through over
When people dream about becoming a baker or a pastry chef, they often think about the delicious pastries they'll create, delighting their patrons with towering cakes wrapped in impossibly smooth fondant. But very rarely does anyone start off by thinking about the preparation involved in baking…
Imagine opening your mailbox and seeing a letter addressed to “current resident,” or having your financial institution’s AI powered digital assistant inform you that your replacement card is on its way to your old address.
Most people would take this impersonal letter, throw it in the trash, and go
High-quality data is the core requirement for any successful, business-critical analytics project. It is the key to unlock and generate business value and deliver insights in a timely fashion. However, stakeholders across the board are responsible for data delivery, quickly evolving requirements,
Haruto Sakamoto, the Chief Information Officer at a Japanese multinational imaging company, had a few challenges to contend with. His business units had a presence in 180 countries worldwide with geographically-dispersed data warehouses and business intelligence applications in various locations.
The number of business segments requiring data to drive contextual insights is increasing. Leaders are seeking new ways to manage the pressures of delivering high-quality data faster across their businesses. To date, many of these projects have focused solely on ingesting data into a data lake
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 IBM Institute for Business Value found that 85 percent of companies manage a multicloud environment. That means an overwhelming majority of businesses use a multicloud approach to enable greater flexibility and application modernization.
Some companies have found that while some clouds are a
On the latest episode of Data Decoded, we make our predictions for 2019: top data trends, challenges and what chief data officers (CDOs) will need to do to keep up with the ever-changing data landscape. Hear from Michele Goetz, principal analyst at Forrester Research and Jay Limburn, IBM
In part two of our podcast episode on Blockchain, Soma Shekar (Software Architect, IBM Analytics) discusses the enterprise application of blockchain in Master Data Management - going beyond blockchain application in cryptocurrency.
Blockchain — it’s one of the hottest topics in the tech industry, but what exactly is it? Yves Mulkers (Founder of 7wData) and Erik O’Neill (IBM Entity Analytics, Sr. Offering Manager) discuss the history of blockchain and what is means for businesses wanting to get ahead in the world of data.
There’s a general need for next-gen executives to not only understand corporate regulations, but be able to adhere to and follow them using metadata solutions like data governance. As the business world’s top asset becomes data, data governance will ensure that data and information being handled is
From machine learning to blockchain to artificial intelligence, data is dominating the conversation in the tech industry. In the first episode of Data Decoded, William McKnight, CEO of McKnight Consulting, and Yves Mulkers, founder of 7wData and a data/business intelligence architect, discuss the
The data lake may be all about Apache Hadoop, but integrating operational data can be a challenge. Learn how to deliver real-time feeds of transactional data from mainframes and distributed environments directly into Hadoop clusters and make constantly changing data more available.