Strategic advice & alerts to leverage data analytics & new technologies
Leverage data and the technologies that generate it, from IoT to AI/machine learning, wearables, blockchain, and more, to improve decision-making, enrich collaboration and enable new services.
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.
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.
So 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.
Barry Devlin takes us on a journey to help us understand how context plays a big role in using data. Known for creating the first data warehouse architecture, he proposed a new standard for data architecture for today’s world in 2013. Devlin puts context-setting information at the heart of all data architectures, and for good reason. In the drive to digitize more business processes, the intricacies of how all stakeholders interact with data have been underexposed. Though it is understandable that getting a grip on technology and reorganizing your business is hard enough, it is precisely this interaction that will determine your success. If you turn your perspective around, as he argues, your data architecture will be of more value.
Robotics has benefited considerably over the past two years from advances in AI, with the biggest stemming from developments around deep learning neural net architectures and machine vision systems. Consequently, today we are seeing robots employing advanced autonomous navigation and intelligent object recognition capabilities — including image-sensing functions utilizing advanced pattern matching, shape detection, and face-tracking and analysis.
Tim Virtue focuses on the most significant risks of BaaS. He identifies common BaaS risks and proposes mitigation strategies for all of them. Virtue stresses that adoption of innovative business models is essential for new market entrants. In the build-versus-buy debate, he favors buy, although stresses that the BaaS provider should be a trusted partner, not simply a commodity supplier. Despite the significant risks involved in digital transformation adoption, doing nothing is the greatest risk of all.