Why 2019 is the year of post-modern analytics
Data is the new currency on which the modern economy thrives. Businesses that possess the know-how to effectively translate data into insights hold the key to unlocking unparalleled power and influence, and the best part is that every business today has the potential to become a powerful and influential entity.
A 2018 Data Literacy Index conducted by Qlik uncovered that large enterprises that have higher corporate data literacy experience US$320-$534 million in higher enterprise value. This shows that there is a clear business case for leaders to commit resources to ensure that employees are well equipped to operate in the new, data-driven environment. The best way to do so is to fundamentally change the way data and analytics are being managed and used by making it accessible to every employee across all levels. Moving into 2019, here are five trends that demonstrate why it will undoubtedly become the year of post-modern analytics.
1. Artificial Intelligence will make analytics more human, not less
The use of AI will make analytics more human, not less. Designing AI around humans will result in a higher impact for organisations over the next five years, rather than designing to remove humans from the process.
Across the information value chain - from reading the data, to preparing it, to critically analysing it with less bias, to presenting contextual results - AI can and will remove many of the bottle-necks that make users give up. Machine learning and telemetry will also capture the power of the collective, which can be fed back in a virtuous loop, further improving and contextualising the user experience. The paradox is, therefore, that data and analytics will become more human than ever, with AI in the mix.
What is often overlooked is the huge gap between the data created, and the human ability to process it and act upon it. There is also a gap between the availability of today's analytical tools and their adoption within organisations. Those gaps can and should be closed, and the result will empower humans.
2. Analytics will be more pervasive, and even re-shape business processes
Embedding analytics into business processes is not new but is now becoming mainstream. Users want analytics in their workflows as they help make data more actionable and increasingly also accessible in real-time. All of this is being fueled by machine learning and AI, which can provide contextualised insights and suggested actions.
It's the foundation of "continuous analytics" in which real-time analytics will be gradually integrated within a business operation or IoT device, processing data to prescribe actions in response to business moments and at the edge. In the next five years, intelligent applications will be ubiquitous.
New technologies, such as robotic process automation and process mining look at digital footprints and from a chain that can further automate or re-shape business processes. For example, when a customer places an order for a product online, it will have the ability to automate and re-shape sub-processes including receiving, fulfilling and invoicing the order.
3. The focus will shift from putting data in one place to attaining one view of the data
The ability to have a single view of all data has never been more important than now. Data is coming from all different directions, speeds, and formats, and being able to control that will be one of the key markers for empowerment and success in the data age. Historically, it has been cumbersome to put all data in one place, hence the emergence of data silos and governance issues.
There are two major forces that make getting a single view of data while keeping it where it resides a possibility. Firstly, as different vendors come together and standardise data models, what this means is that cloud-based data sources will have more consistent formats. Secondly, and more importantly, is the emergence of enterprise data catalogues. Accessible in a hub, data catalogues make it possible to audit the entire distributed data estate, delivering a shop-for-data marketplace experience.
4. Performance takes centre-stage as analytics scale
Performance has been a bottle-neck for distributed big data at scale and the reason why many Hadoop projects failed to become much more than cheap storage. Breakthroughs have recently been achieved through indexing, caching and pre-preparing very large and distributed datasets.
As companies of all sizes start to increase their adoption of hyperscale data centers, performance will rise in the selection criterion. Some organisations have moved their data back through 're-patriation' because they have not been seeing strong enough performance.
This becomes even more important in an IoT application. Increasingly, more workloads will run locally or at the edge to avoid latency. In short, efficient performance will be a deciding factor for how architectures will look – centralised or distributed.
5. Analytic platforms will evolve into virtuous systems, feeding of participation
BI and analytics are the most effective for organisations when viewed as a system and not simply as a series of artefacts and tools. An important difference is that individuals use tools, but people participate in systems.
A post-modern system contains a host of people with differing roles, skills or intentions. In this system, humans interact with non-human participants such as digital services, bots, intelligent agents, extensions, algorithms and so on, resulting in exchanges and learning that increases the value of the system, augmenting both the human and machine intelligence within it.
An open, self-learning system that improves with further participation will define how future platforms look like and enable data democracy and analytics empowerment.
