![]() ![]() ![]() That is the application integration domain, which is usually addressed with service oriented architectures and microservices architectures. the ability to take more intelligent business decisions and execute more intelligent business processes, is rapidly growing, fueled by the ability to exploit more diverse and disparate kinds of data, the key enabling asset.Īt first, the base for every enterprise architecture is to enable transactional applications to talk with each other, to connect business processes and execute their operations smoothly. We can represent this evolution in a simple diagram, depicted below: the degree of Enterprise Intelligence, i.e. And what IT organizations are facing at this time of their history is, by all means, the breakthrough of a brand-new integration domain. This is happening because though integration and governance still have to address some functional needs and concerns which are always the same across every context, like data movement, workflow orchestration or metadata management, these needs and concerns must translate and adapt to very different architectures, with different applicability and design focus, depending on the target integration domain. In other words, even if data integration and data governance are concerns which are as old as enterprise IT, they’ve never been so far from being solved and stable, and they’ve never been so critical to the outcome of every business innovation. For example, the most common frustration of every data scientist is that they need to spend too much time and effort in accessing and preparing the data, versus working on the actual models and intelligence, as outlined in a survey by Rexer Analytics.ĭespite these huge data integration and preparation efforts, the most common reason why data-driven innovation projects actually fail to move from experimentation to production is still the lack of accessibility, availability, quality and understanding of the various disparate data sets and sources. ![]() As you may have seen mentioned already, “the pace of change has never been this fast, yet it will never be this slow again.”īut what is the impact that this change is having on data integration and data governance, and how are IT organizations dealing with it? My perception, based on what we see happening among large and medium enterprises right now, is that most organizations are just simply not dealing with it yet, struggling to adapt their old practices and tools to their new needs, in what looks like a lost battle. These factors are all leading to the advent of the Intelligent Enterprise. The history of enterprise IT is now at a turning point, where several disruptions are coming together almost simultaneously: the vast adoption of IoT technologies, the rise of Data Science as a common IT practice, the availability of massive data assets and computational power which are now enabling large-scale usage of machine learning, the unprecedented distribution and scaling possibility offered by cloud architectures. In the past decades, the evolution of enterprise IT landscapes, of their maturity and of their business contexts and objectives, has always brought along essential demands of data integration, and critical concerns of data governance. ![]()
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