We know that a lot of organizations working on data and analytics struggle with getting the right tools and technologies in place and modernizing their architectures to keep up with the newer demands.
We see four main shifts in the area of data and analytics architectures and technologies.
First of all, the adoption of what we would call distributed architectures and approaches and technologies that support those. Historically, data and analytics programs have been supported by rather centralized architectures and tools. We brought all the data to a central location and then processed it in order to create some value. But in the face of modern pressures and demands that approach is now breaking down. There’s now too much data, it’s already too distributed; it’s too diverse, it’s too complex.
Value now needs to be generated at the point of creation of the data for timely response. This is one of the main reasons why distributed architectures are now… more required and more popular.
The second technology shift we see is the increase in self-service and broad distribution of analytics capabilities to everyone in the enterprise. The nature of data and analytics technologies is shifting such that less technical individuals right across the business can now engage with those tools and generate value from them. This is one of the main reasons why we see considerable growth in areas like self-service analytics tools, self-service data preparation technologies, and data governance-related tools that are enabling data stewards anywhere in the business to do more in creating trusted, high-value data.
Thirdly, we’re also witnessing an increase in the adoption of new and disruptive technologies such as data virtualization as a means to connect to highly distributed data, and the ability to access it on the fly. It also comes as no surprise that given the demands of digital business, the use of advanced analytics is also on the increase. While this doesn’t take away from the fact that the more traditional styles of analytics are still important, given the scale of everything today, technologies such as AI, data science and machine learning capabilities, natural language processing and blockchain are needed to cope with exponentially increasing volumes, complexity and pace.
And finally, another big trend we see relative to architectures and technologies is the increasing adoption of a metadata-driven approach to data and analytics. That is to say the importance of grounding all data and analytics architectures and technologies in a solid foundation of metadata. Having the ability to capture all of the knowledge about what data you have, where it resides, how it’s all related, who uses it, why, when and how — and then using that insight to provide more personalized, automated and properly governed solutions to the business.
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