The recent global pandemic has left many organizations in an uncertain and fragile state. It is therefore a fundamental requirement for enterprises to keep pace with data and analytics trends in order to bounce back from the crisis and gain competitive advantage.
From crisis to opportunity, the role of data and analytics is expanding and becoming more strategic and critical. Society in general is becoming increasingly more digital, complex, global with ever growing competition and emancipated customers. Massive disruption, crisis and the ensuing economic downturn are forcing companies to respond to previously unimaginable demands to resource optimize, reinvent processes, and rethink products, business models and even their very purpose.
It is therefore obvious that Data & Analytics is central for enterprises navigating their way out the devastating effects of these crisis, however, the lack of trust and access to data has never been a greater challenge.
Success at scale for maximum business impact with data & analytics depends more than ever on building a foundation of trust, security, governance, and accountability.
We share in this article, the current Data & Analytics trends to help your business thrive:
#1 – The use of new AI techniques
By the end of 2024, 75% of enterprises will shift from piloting to operationalizing AI, driving 5X increase in streaming data and analytics infrastructures.
Within the current context, AI techniques such as machine learning, optimisation and natural language processing are providing vital insights and predictions about the spread of the virus and the effectiveness and impact of countermeasures. With the more commercial use of AI, organizations are discovering new and smarter techniques, including reinforcement learning and distributed learning, interpretable systems, and efficient infrastructures that handle their own complex business situations.
#2 – Less Dashboards
By 2025, data stories will be the most widespread way of consuming analytics, and 75% of stories will be automatically generated using augmented analytics techniques.
Today, business employees struggle to know what insights to act on because Business intelligence platforms are not contextualized, easily interpretable or actionable by the majority of users. Visual analytics and exploration will be replaced by more automated and customized experiences in the form of dynamic data stories. As a result to the shift to more dynamic, in-context data stories, the percentage of time spent on predefined dashboards will decline!
#3 – Decision intelligence
By 2023, more than 33% of large organizations will have analysts practicing decision intelligence including decision modeling.
A brief definition of decision intelligence is that it is a practical domain that frames a wide-range of decision-making techniques and integrates them to all critical parts of people, processes and technologies. It provides framework that brings traditional and advanced disciplines together to design, model, and execute and monitor decision models and processes in the context of business outcomes.</p>
The use of intelligent decision making will bring together decision management and techniques such as descriptive, diagnostic, predictive and prescriptive analytics.
#4 – Augmented Data Management: Metadata is the new black
By 2023, organizations utilizing active metadata, machine learning and data fabrics to dynamically connect, optimize and automate data management processes will reduce time to integrated data delivery by 30%.
The combination of colossal data volume, data trust issues and an ever increasing diversity of data formats is accelerating the demand for automated data management. In response, the potential to utilize metadata analytics poses a new solution to augmenting data management tasks. It is no secret that organizations need to easily know what data they have, what it means, how it delivers value, and whether it can be trusted. Metadata will emerge from a passive state to a highly active utilization state. Active utilization leverages cataloging, automatic data discovery by interpreting use cases and implies taxonomy and ontology that is crucial to data management.</p>
Through augmented data catalog, users can improve data inventorying efforts by significantly augmenting the otherwise cumbersome tasks of finding, tagging, annotating and sharing metadata.
#5 – Moving to the Cloud
By 2022, public cloud services will be essential for 90% of data and analytics innovation.
As Data Management accelerates its journey to the cloud, so will data & analytics disciplines. Cloud environments enable a more agile, fluid, diverse ecosystem that accelerates innovation in response to changing business needs that are not readily available in on-premises solutions. It also provides opportunities regarding cost optimization. It is expected to see offers such as “cloud first” capabilities, eventually become “cloud only” capabilities.
Gartner clients can read more in the report “Top 10 Trends in Data and Analytics, 2020.”
