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.
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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.
How do you put context-setting information at the center of your data architecture? Adrian Jones illustrates how to do this by putting Cutter Consortium Senior Consultant Barry Devlin’s architecture into practice. He stresses the importance of context-setting information by pointing out the increased vulnerability to which we are exposing ourselves. We produce and use more and more data. In the backs of our minds, we know that data governance is of growing importance, but we don’t act in the right way on this knowledge. The problem with data governance is that it is never part of a data architecture but rather addressed as a separate process. If our data architectures are not aware of the vulnerability being introduced, accidents are just waiting to happen. Jones hands you the recipe for avoiding these accidents.
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.
Christian Kaul and Lars Rönnbäck explore what it means to adopt a data-centric paradigm. It certainly isn’t enough to have a data-centric data architecture; the implications are much more fundamental. The ultimate consequence is that you need to create a model-driven organization. By doing so, data architecture determines the shape of the organization, not the other way around.
Clearly, blockchain will have an impact on technology and the ways we conduct business and how governments and not-for-profits provide services. But until we alleviate several clear barriers, progress will be slow. We discuss a handful of those barriers in this Advisor, including lack of interoperability, lack of mature toolsets, and lack of qualified personnel.
In this Advisor, we consider the four types of blockchain and explore some financial services where blockchain can be used effectively to enhance customer experience and increase the efficiency of those services.
According to a recent Cutter Consortium survey, approximately a quarter of organizations are looking into using smartbots and intelligent assistants for their customer experience initiatives. Intelligent agents and smartbots enable customers to conduct common interactions in a conversational manner via speech and natural language speech or text-powered interfaces.