Published 20. Jan. 2017
Is 2017 the Year of Data Literacy?
There's an explosion of data, an increase in processing, and a move towards information activism over the past 12 months says Qlik's Senior Director Dan Sommer. The problem is there aren't enough specialists to process them. Could this be the year that the resource gap will be closed?
People that could work and master huge amounts of information, such as data scientists, application developers, and business analysts, have become a valuable entity. However, “Unfortunately, there still aren’t enough people with the expertise to handle the ever-increasing, vast levels of data and computing,” observes Dan Sommer, Senior Director at Qlik.
2017 is the new digital era of facts, what with all the information currently being produced and held by businesses today, “But, without the right number of specialists to consume and analyze it, there’s a gap in resources. Data is, unfortunately, growing faster than our ability to make use of it.”
If this is the case, many business leaders are left with no other choice but rely on gut instinct to make even the most important decisions, “Unable to hone in on the most important insights, they’re presented with multiple – and sometimes conflicting – data points, so the most important ones seem unreliable.”
The situation needs to change and for that to happen, “Upskilling more data scientists in 2017 is a must, but there will be a greater focus on empowering more people 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.”
7 Data Literacy Predictions
What will change to see culture-wide data literacy become a reality? Here are Dan’s predictions:
- Combinations of Data – Big data will become less about size and more about combinations. 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 the move is 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. Combining data will also shine a light on false information more easily, improving data accuracy, as well as understanding.
- Hybrid Thinking – In 2017, hybrid cloud and multi-platform will emerge as the primary model for data analytics. 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 workloads and publishing will happen across cloud and on-premise.
- Self-Service for All – “Freemium” is the new normal, and 2017 will be the year users have easier access to their analytics. More and more data visualization tools are available at low costs, 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.
- 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.
- 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.
- Visualization as a Concept will Move from Analysis-Only to the Whole Information Supply chain – Visualization 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.
- 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 customized and contextualized by application and web developers will make further headway.
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 in an era where the right data becomes connected with people and their ideas. It’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.”