As we close out 2016 we are reflecting on the past year, which we dubbed “the year of the information activist.” We’ve seen an explosion of data and an increase in processing that has led to great opportunities for the chosen few who can handle these mega volumes of data. These growing numbers of skilled employees who are data scientists, application developers, and business analysts actively able to work with, and master, huge amounts of information are sought after and can reap big benefits.
But the numbers of these experts aren’t growing at the same rate as data and computing, leading to a gap in these resources. Just when we should have been ushered into a new digital era of facts, the paradox is that the gap is widening between the data created and the capability of the broader populous to consume it. For many, they simply shut down and go with their gut — the irony being that we may be worse at making data-driven decisions now than when we had less data. Multiple data points are spouted around, diluting them, and making them lose their meaning, leading to information pollution, bias, fatigue, and sometimes the most dangerous state of all — information ignorance. Some even say we’re in the “post factera.”
In 2017, the counter reaction will be that societies and organisations will start to wake up to the imperative that data literacy is needed for all — beyond information activists — just like reading and writing was 100 years ago. We’ve identified 10 trends we predict will help lay the foundation for increased data literacy. In 2017…
Information pollution will become a critical concern. Whether actively pushed through deliberate click-baits, misinformation, or a result of exponential data growth, the post-fact era will lead to more conflicting and inaccurate data points, and in some cases even good data getting polluted with bad data. It becomes increasingly important to understand what is the right data. To source critique, certify, and argue with data in a governed way will be the cornerstone of data literacy and increase in importance.
Big data will become less about size and more about combinations.With more fragmentation of data, and the majority of it created externally and in the Cloud, looking at singular data sets without context will diminish in value. The following wave will be about the ability to quickly combine big data with small data to serve specific use cases.
Self-service visualisation will become a commodity, accessible to all. Freemiums are now expected, making 2017 the year when barriers to access great analytic tools will be virtually removed. With more people able to begin their analytics journey, data literacy rates will proliferate — starting more on the path of information activism.
Modern BI will overtake traditional BI as the new reference architecture. Data discovery has graduated to modern BI, and will become the “new normal” in organisations. In 2017, this will evolve to not just complement, but increasingly replace archaic reporting-first platforms. As modern BI becomes the new reference architecture, it will open up for more bottom-up self-service for more people. But it also puts different requirements on the back end for scale, performance, governance, and security.
Hybrid cloud and multi-platform will emerge as the primary model.Because of where data is generated, ease of getting started, and its ability to scale, we’re now seeing an accelerated move to cloud. But one cloud is not enough, because the data and workloads won’t be in one platform. In addition, data gravity also means that on premise has long staying power. Hybrid and multi-environment will emerge as the dominant model, meaning that the workloads will happen across cloud and on premise — resulting in that model marginalising a cloud-only approach.
Focus will shift from “advanced analytics” to “advancing analytics.”Advanced analytics will continue to proliferate, 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 can’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 ask the right questions as it is to provide the answers.
We will learn the other side of “personal analytics.” There are two angles to personal analytics, one of which is how information activists and beyond can increasingly self-serve to utilise information for their personal benefit. But the other side is how information becomes increasingly granular and is utilised to drill down to “segment of one.” The overlap in these use cases of personal analytics is fast becoming a marketer’s dream. By understanding customers’ preferences and behavioural patterns, companies can utilise such data to tailor more personalised products, services, and messages. However, consumers will increasingly realise the value of their personal information as it becomes more available to others.
The digital and physical worlds will begin to meet in analytics.Analytics won’t just be everywhere, but increasingly in everything. Pokémon GO© is an indicator of the next-step change after mobility, and the corporate world will take notice. This will mean that analytics will start appearing in the context of geospatial, touch, voice, virtual reality, and gamification, and will continue on the path of being connected to devices.
Focus will shift to custom analytic apps and analytics in the app.Everyone won’t — and can’t be — both a producer and a consumer of apps. Yet they should be able to explore their data. Hence, data literacy will benefit from meeting people where they are, with more contextualised and customised analytic applications, as well as analytics that reach us in our “moments.” As such, open, extensible tools that can be customised and contextualised by application and web developers will make further headway.
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. Further, more progress will be made in having visualisation being a means of also communicating out findings. The net effect of this is that increased numbers of users can do more in the data supply chain, advancing the concept of data literacy.
What does it all mean?
These trends lay the foundation for increased levels of not just information activism, but also data literacy. New platforms and technologies that can catch “the other half” (i.e., less skilled information workers and operational workers on the go) will help usher us into an era where the right data becomes connected with people and their ideas — putting us on the path toward a more enlightened, information-driven, and fact-based era.