Strategic advice to leverage new technologies
Technology is at the heart of nearly every enterprise, enabling new business models and strategies, and serving as the catalyst to industry convergence. Leveraging the right technology can improve business outcomes, providing intelligence and insights that help you make more informed and accurate decisions. From finding patterns in data through data science, to curating relevant insights with data analytics, to the predictive abilities and innumerable applications of AI, to solving challenging business problems with ML, NLP, and knowledge graphs, technology has brought decision-making to a more intelligent level. Keep pace with the technology trends, opportunities, applications, and real-world use cases that will move your organization closer to its transformation and business goals.
Insight
Organizations should view customer analytics as a way to help align the enterprise and make it function from the same set of metrics to provide the much-talked-about but often difficult-to-achieve “single view” of the customer. This view serves as the basis for making decisions about how best to interact with your customers; in effect driving all aspects of the customer lifecycle — from acquisition, retention, and growth to maintaining loyalty and measuring ROI across the organization.
Here in Part XI, we discuss how completion time estimates are determined and the biases that affect those estimates.
AI strategy, at its core, must address vital questions, such as the following: How can AI deliver better value to customers? How can it help companies increase revenues, enhance efficiency, and reduce human errors? How can AI capabilities be integrated into the existing organizational processes to develop a distinct competitive advantage? To address those questions, AI strategy must closely align with a company’s business objectives, ensuring synergy between the corporate strategy and the AI strategy.
In a recent on-demand webinar, “Using AI to Improve Agile Teams,” Cutter Consortium Senior Consultant Jon Ward described a team that was able to cut time-to-market in half and reduce the cost to deliver by 60% by using Agile with artificial intelligence (AI). He addressed how AI could be used to further enhance a team’s productivity, where AI might inhibit it, and outlined where AI can be used to improve your productivity. This Advisor shares some of the questions Jon responded to at the end of the webinar, which you may also be considering.
How does an architect see things that may be dark otherwise? There are many examples where seeing things differently make things different, sometimes with extraordinary, decades-long impact.
This issue of CBTJ will help you understand that a data architecture should be much more than merely a technology roadmap. To be of any value to people in an organization, the architecture should guide the people in an organization to an understanding of how to organize for ever-changing information requirements.
The data scientist role is perhaps the most important of all roles in the adoption of big data–based decision making.
In this article, Sagar Gole and Vidyasagar Uddagiri help you understand which fundamental concepts — specifically, the six elements of an enterprise-wide data architecture — you should address today in order to “overcome challenges and leverage the opportunities and benefits of digital transformation.” They describe the “secret sauce” that prepares your organization to thrive during a digital transformation journey.