Big Data and The Internet of Things: Enterprise Information Architecture for A New Age
Robert Stackowiak, Louis Nagode
Format: PDF / Kindle (mobi) / ePub
Enterprise Information Architecture for a New Age: Big Data and The Internet of Things, provides guidance in designing an information architecture to accommodate increasingly large amounts of data, massively large amounts of data, not only from traditional sources, but also from novel sources such everyday objects that are fast becoming wired into global Internet. No business can afford to be caught out by missing the value to be mined from the increasingly large amounts of available data generated by everyday devices.
The text provides background as to how analytical solutions and enterprise architecture methodologies and concepts have evolved (including the roles of data warehouses, business intelligence tools, predictive analytics, data discovery, Big Data, and the impact of the Internet of Things). Then you’re taken through a series of steps by which to define a future state architecture and create a plan for how to reach that future state.
Enterprise Information Architecture for a New Age: Big Data and The Internet of Things helps you gain an understanding of the following:
- Implications of Big Data from a variety of new data sources (including data from sensors that are part of the Internet of Things) upon an information architecture
- How establishing a vision for data usage by defining a roadmap that aligns IT with line-of-business needs is a key early step
- The importance and details of taking a step-by-step approach when dealing with shifting business challenges and changing technology capabilities
- How to mitigate risk when evaluating existing infrastructure and designing and deploying new infrastructure
Enterprise Information Architecture for a New Age: Big Data and The Internet of Things combines practical advice with technical considerations. Author Robert Stackowiak and his team are recognized worldwide for their expertise in large data solutions, including analytics. Don’t miss your chance to read this book and gain the benefit of their advice as you look forward in thinking through your own choices and designing your own architecture to accommodate the burgeoning explosion in data that can be analyzed and converted into valuable information to drive your business forward toward success.
also illustrated the addition of closed-loop event processing and business rules where the sensors are deployed in the brick-and-mortar stores. In addition to our cash register bottleneck example, as shoppers enter the store with mobile phones that run our loyalty application, we might want to begin monitoring where they are located and have our salespeople better assist them based on information we have gathered on products they have recently been shopping for on our web site. The diagrams we
bring down the cost of computing, mainframes and software were still too expensive to do much experimentation. All of that changed with the introduction of lower cost minicomputers and then personal computers during the late 1970s and early 1980s. Spreadsheets and relational databases enabled more flexible analysis of data in what initially were described as decision support systems. But as time went on and data became more distributed, there was a growing realization that inconsistent approaches
define, implement, and manage the proposed future state information architecture • Breakdown of skills evaluated (by architecture type) • Assessment of skills needed to fulfill the vision and identification of important gaps • Proposed solutions to filling the skills gaps • Next steps including scheduling of activities outlined in subsequent chapters of this book As we noted near the end of the agendas for delivery of the presentations and the report, we are not simply presenting the
history so far (when it began, the process that was used in developing the project plan, and who was engaged in the process) • Our initial visioning outcome (potential business drivers discovered and the impact on current state architecture) • Key business drivers and KPIs (including critical success factors, key measures identified, and business priorities) • Data source mappings and the analysis necessary to deliver information aligned to our key business drivers (Business Information
real time. Examples might include recommending products that could be of interest during a web site shopping visit or equipment that should be checked out for maintenance because its failure is predicted in the near future. Web site activity data is typically analyzed using predictive analytics models. The models’ results are periodically provided as updates (using batch feeds) to a real-time recommendation engine. The engine then recommends that the web site serve up specific web pages or