Top 5 Data Analytics Trends for 2019

Users want data analytics trends in their existing workflows to make insights more actionable, and they’re increasingly asking for insights in real time

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data management

Technology is changing faster than most of us can comprehend. These changes are impacting our lives at a personal level, and also at the community and corporate levels. It has reshaped the way society functions by bringing forth a shift in power, from physical ownership to owning information. But this is not the only change that technology – and, more importantly, data – will bring in the future. As the year is coming to an end, here are five trends that will define data analytics in 2019:

  • Data literacy will become a key performance indicator (KPI) for enterprises

With the increasing awareness about the need for data literacy, individuals and organizations are now looking to improve their data literacy readiness. But improvement requires them to first understand what level they currently are at – something which hitherto was a major challenge. New methods of data literacy analysis have enabled organizations to more accurately gauge their workforce’s data readiness, as well as to enhance their skillsets through more targeted and relevant strategic interventions.

Thanks to initiatives like The Data Literacy Project, it is also now possible to determine the data literacy score of an entire organization. A recently Data Literacy Index done by Qlik on behalf of the Data Literacy Project showed a direct correlation between the data literacy level of an organization and its performance across various KPIs such as gross margin, return-on-sales, return-on-equity, return-on-assets, etc. This is a key indicator on the dominating role that data literacy will play in the building of strategies imperative to the functioning of a business and its processes.

  • Augmented Intelligence: The integration of human and machine capabilities

There are two challenges across the information value chain that have been overlooked but require immediate attention and implementation. Firstly, the huge gap between the data created and the ability of humans to process all of it. Secondly, the gap between the high availability of analytical tools and the low adoption level within the enterprises needs to be actively acted upon.

Closing these gaps will only result in human empowerment, for AI will complement human capabilities by performing activities such as data gathering, data processing, data analysis, and insight generation. We believe people will not be removed from the equation, but instead their capabilities will be enhanced.


Other tech-led interventions like machine learning and telemetry will also harness the power of the system group to incrementally get better at identifying patterns and presenting relevant suggestions to further enhance the end-user experience. Such a man-machine integration will also set the foundation for true Augmented Intelligence (AI), which will drive greater value and benefits for the business landscape.

  • Reshaping centralized platforms by managing multi-cloud, hybrid, and edge as a continuum

Enterprise data today comes in from several directions, at different speeds, and in varying formats, and is stored across various data storage environments. Transferring all of this data to centralized cloud storage for analysis and processing is merely branding the same old bag of chips in a new packet, and also impacts the speed at which business value can be realized from enterprise data. Additionally, such hyper-centralization may leave organizations vulnerable to challenges such as vendor lock-in, governance issues, greater security risk, and higher back-end costs.


This approach of managing data on centralized cloud storage servers will change in 2019. Instead of aggregating enterprise data in one place, organizations will look to gain a centralized view of all their data regardless of where it is stored. This will drive a shift towards data analytics platforms which provide complete, real-time visibility into and management of enterprise data. Domain-leading players such as Qlik are already treating multi-cloud, hybrid, and edge computing as a data continuum instead of different data environments, driving enhanced enterprise value by unlocking data associations which had hitherto remained hidden.

  • Reshaping processes by embedding analytics into workflows

Up until now, business users have relied on dedicated analytics teams to answer the questions from their data. This linear, funnel-based approach hampers the speed at which raw information can be turned into actionable insights. The scope of data exploration and value realization is also limited, as the analyst often does not have the full context in which the data is being explored.


Users want analytics in their existing workflows to make insights more actionable, and they’re increasingly asking for insights in real time. This shift is being fueled by machine learning and AI, which provide contextualized insights and suggested actions. For example, when customers place orders for products online, intelligent applications will analyze patterns and transform processes like receiving, fulfillment, and invoicing to be more efficient and more effective.

  • Integration of visual, conversational, and presentation data technologies

Data often has several stories to tell, but traditional data analytics – with endless rows and rows of information – makes it hard for users to identify these stories trapped inside their data. Expect this to change in 2019. In the last three years, machine-driven data storytelling has emerged, offering narrations through natural language generation (NLG). Adding natural language query (NLQ) and natural language processing (NLP), often referred to as “conversational analytics,” will make this approach much more interactive and accepted.


The convergence of conversational analytics and data storytelling with presentation technologies will also aid the larger data literacy initiative. This will further boost performance and scalability, which are increasingly becoming a priority in the field of data and analytics.

Arun Balasubramanian Managing Director India Qlik Mr. Arun Balasubramanian, MD, Qlik India

By Mr. Arun Balasubramanian, MD, Qlik India