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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Organizations have worried about how to protect sensitive data in big data platforms since they were first proposed for enterprise use. This is because big data environments have typically lacked the extensive security features available with the more traditional relational data warehouses that companies have become accustomed to.
In a well-functioning team, collaboration and knowledge transfer are simply byproducts of the team’s work. The fact is that embracing a business process management system (BPMS) results in a certain amount of culture shock for the uninitiated; the learning curve from a technical and business process modeling standpoint is considerable. All of these elements should be discussed at this review meeting.
The architecture of the Web is elegant, and it delivers value. Is it possible to have complex, convoluted architectures that are inelegant, but that enable value? Maybe. But my intuition and experience suggest otherwise. I think that architecture needs to be as close to invisible as possible to be valuable. Architecture needs to be the invisible hand that guides the enterprise and the people within in and around it.
In Part I of this series, I explored the data lake and other metaphors for data storage in use today. Leaving technology platforms aside (which I strongly urge you to do, at least for now), understanding what a data lake could or should be starts, unsurprisingly, with knowing what the business needs from it and that it cannot get anywhere else. That understanding comes from a conceptual architecture, which therefore must have a much broader scope than its central topic. A conceptual architecture is a picture that forms the basis for conversation, understanding, and agreement between business and IT. It doesn't have enough detail for IT to build it. It must be simple enough for business people to take it in and understand what's going on.
The potential of gamification as a business transformation and performance improvement tool is immense — if done right. The focus of this Executive Update is enterprise gamification: gamifying business scenarios and processes to derive more value.
In his keynote address on NoSQL key value stores at the 2014 Velocity Conference, Brian Bulkowski discussed "in-memory" mechanisms and how they can be handy in electronic advertisements and similar situations. He mentioned during the keynote that "in-memory key value is Agile" is significant. This very thought opens up opportunities to consider the technologies of big data as enablers of business agility.
To achieve pervasive, sophisticated, wide, deep, joined-up architecture descriptions, we need to develop a new generation of EA tools and techniques. The fact is that in an enterprise of any complexity, the architecture of roles, processes, applications, flow of work and data, data stores, and data sources quickly exceeds the ability of any of us to hold in our minds effectively. We need powerful ways to capture, map, navigate, and trace linkages, interfaces, and change initiatives.
In the November-December 2015 issue of Cutter IT Journal (CITJ), I shared “Five Steps to Digital Transformation.” This Executive Update picks up where that discussion left off and dives deeper into the front end of the transformation process.