Dan Sommer, senior director of Qlik, predicts the data literacy trends startups need to look out for in 2017.
In a short space of time we’ve witnessed an explosion of data, an increase in processing it and a move towards information activism. Therefore, having employees actively able to work with – and master – the huge amounts of information available, such as data scientists, application developers, and business analysts, are integral for any UK startup to be successful.
Unfortunately, however, there still aren’t enough people with the expertise to handle the ever increasing, vast levels of data and computing. When there is talent available, chances are there will be fierce competition, with many startups potentially priced out of hiring data analysts and the like.
The situation needs to change. Yes, that will mean upskilling more data scientists in 2017, but there will be a greater focus on empowering employees more broadly. That will go beyond information activists and towards providing more people with the tools and training to increase data literacy. Just as reading and writing skills needed to move beyond scholars 100 years ago, data literacy will become one of the most important business skills for any member of staff.
So, what will change to see culture-wide data literacy become a reality among startups and growing businesses? Here are my predictions:
Combinations of data
With more fragmentation of data and most of it created externally in the cloud, there will be a cost impact to hoarding data without a clear purpose. That means we’ll move towards a model where businesses have to quickly combine their big data with small data so they can gain insights and context to get value from it as quickly as possible.
Hybrid thinking
Hybrid and multi-environment will emerge as the dominant model, meaning workloads and publishing will happen across cloud and on premise. The cloud provides scalability and ease of access, with long staying power provided by on premise.
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 business.
Scaleup
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.
Advancing analytics
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.
Visualisation
Visualisation as a concept will move from analysis-only to the whole information supply chain – it 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 visualisation as a means to communicate our findings. The net effect of this is increased numbers of users doing more in the data supply chain.
Focus
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.
These trends lay the foundation for increased levels of not just information activism, but also data literacy. After all, 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 — that’s going to close the chasm between the levels of data we have available and our ability to garner insights from it. Which, let’s face it, is what we need to put us on the path toward a more enlightened, information-driven, and fact-based era.