In a world where information is power, we need postmodern analytics – analytics that decentralize data ownership and deliver high-caliber performance that easily scales to the masses. In 2019, we'll see this begin to emerge, as the technology shifts toward making data accessible to all people and all organizations. The following are the 10 virtues of a postmodern analytics platform:
Multi-cloud, hybrid, and edge will form a continuum.
IT leaders are increasingly migrating data to the cloud, but too much centralization may lead to vendor lock-in, inflexibility, and increased risk. A postmodern platform will enable businesses to centrally calibrate and distribute data to multiple clouds while using a hybrid, continuum approach that includes edge computing.
One of the biggest unsung megatrends of today is the rise of Kubernetes, software that can orchestrate and distribute containerized workloads to deliver microservices. Kubernetes can access and process data locally and at the edge, reaching beyond where modern BI platforms have been able to go – and software development teams are rapidly adopting it.
Centralized data will be replaced by a single view of all data.
In 2019, focus will shift from placing all data in one place to having a single view of all data. Not only will data models become more standardized, but the emergence of enterprise data catalogs will change the game. These catalogs are accessible in a hub, with one view of the entire federated data estate, and deliver a shop-for-data marketplace experience.
Analytics embedded in the process will reshape the process.
In the next five years, "intelligent" applications will be ubiquitous, and analytics will begin to reinvent the processes themselves. New technologies like robotic process automation, intelligent process automation, and process-mining will look at digital footprints and further automate or re-shape business processes in a more optimal way.
External innovation will outpace internal innovation by 2X.
Within any company, the number of people who can innovate is finite – but in an open ecosystem, innovation is unlimited. That’s why open platforms will gradually supersede closed ones. In 2019, the market will conclude that open APIs and extensions are a necessity, as innovation in open platforms will outpace innovation in closed platforms by a factor of 2X.
More data, bigger workloads, and more users are inevitable outcomes of successful analytics adoption – but most self-service BI solutions crumble under the load. Recently, we’ve seen performance breakthroughs via indexing, caching, and pre-preparing very large and distributed datasets. As companies of all sizes increase their adoption of hyperscale data centers, performance will rise in the selection criteria.
In BI, AI will remove many of the bottlenecks to getting needed insights, giving humans more time for what they do best – i.e., considering complex problems in context and connecting non-linear dots with the aid of intuition and empathy. In the next five years, AI designed around humans will have a much higher impact than AI that takes humans out of the process.
Visualization, conversation, and presentation technologies will merge.
In the last three years, machine-driven data storytelling has emerged, offering narrations through natural language generation. Adding natural language query and natural language processing, often referred to as “conversational analytics,” will make this approach more interactive and more readily adopted. Over time, data storytelling, conversational analytics, and presentation technologies will gradually merge
New methods of measuring data literacy will enable organizations to develop workers’ skills in a more targeted way. At the same time, new tools for determining a corporate data literacy score will help drive success against KPIs. With data literacy itself as a KPI, CDOs and other executives can steer performance as a strategic and differentiating initiative.
A post-modern BI system will contain a host of human – and non-human – participants with differing roles, skills, and intentions. Digital services, bots, intelligent agents, extensions, and algorithms will grow in diversity and sophistication, learning from humans to increase the value of the system. An open, self-learning system will define the post-modern BI of the future, enabling both data democracy and analytic empowerment.