3. Self-service for all - Freemium is the new normal, so 2017 will be the year users have easier access to their analytics. More and more data visualisation tools are available at low cost, or even for free, so some form of analytics will become accessible across the workforce. With more people beginning their analytics journey, data literacy rates will naturally increase - more people will know what they're looking at and what it means for their organisation. That means information activism will rise too.
4. Scale-up - Much a result of its own success, user-driven data discovery from two years ago has become today's enterprise-wide BI. In 2017, this will evolve to replace archaic reporting-first platforms. As modern BI becomes the new reference architecture, it will open more self-service data analysis to more people. It also puts different requirements on the back end for scale, performance, governance, and security.
5. Advancing analytics - In 2017, the focus will shift from "advanced analytics" to "advancing analytics." Advanced analytics is critical, but the creation of the models, as well as the governance and curation of them, is dependent on highly-skilled experts. However, many more should be able to benefit from those models once they are created, meaning that they can be brought into self-service tools. In addition, analytics can be advanced by increased intelligence being embedded into software, removing complexity and chaperoning insights. But the analytical journey shouldn't be a black box or too prescriptive. There is a lot of hype around "artificial intelligence," but it will often serve best as an augmentation rather than replacement of human analysis because it's equally important to keep asking the right questions as it is to provide the answers.
6. Visualisation as a concept will move from analysis-only to the whole information supply chain - Visualisation will become a strong component in unified hubs that take a visual approach to information asset management, as well as visual self-service data preparation, underpinning the actual visual analysis. Furthermore, progress will be made in having visualization as a means to communicate our findings. The net effect of this is increased numbers of users doing more in the data supply chain.
7. Focus will shift to custom analytic apps and analytics in the app - Everyone won't - and cannot be -both a producer and a consumer of apps. But they should be able to explore their own data. Data literacy will therefore benefit from analytics meeting people where they are, with applications developed to support them in their own context and situation, as well as the analytics tools we use when setting out to do some data analysis. As such, open, extensible tools that can be easily customised and contextualised by application and web developers will make further headway.
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