Top 10 Data and Analytics Trends for 2021

Content By: Gartner

Data and analytics leaders should consider leveraging these trends, from artificial intelligence to small data and graph technology.

When COVID-19 hit, organisations that used traditional analytics techniques realised many of these models were no longer relevant. Essentially, the pandemic changed everything, rendering a lot of data useless.

Consequently, forward-looking data and analytics teams are pivoting from traditional AI techniques to a class of analytics requiring less data.

“These data and analytics trends can help organizations and society deal with disruptive change, radical uncertainty and the opportunities they bring”

Transitioning from big data to small and comprehensive data is one of Gartner’s top data and analytics trends for 2021. These trends represent business, market and technology dynamics that data and analytics leaders cannot afford to ignore.

Data and analytics leaders should consider leveraging these trends, from artificial intelligence to small data and graph technology.

These data and analytics trends can help organizations and society deal with disruptive change, radical uncertainty and the opportunities they bring 

2021 Top Priorities for Data and Analytics Leaders

“These data and analytics trends can help organisations and society deal with disruptive change, radical uncertainty and the opportunities they bring over the next three years,” says Rita Sallam. “Data and analytics leaders must proactively examine how to leverage these trends into mission-critical investments that accelerate their capabilities to anticipate, shift and respond.”

Each of the trends fits under one of these three main themes: 

1. Accelerating change in data and analytics: Leveraging innovations in AI, improved composability, and more agile and efficient integration of more diverse data sources.
2. Operationalising business value through more effective XOps: Enables better decision-making and turning data and analytics into an integral part of the business.
3. Distributed everything: Requires the flexible relating of data and insights to empower an even wider audience of people and objects.

Trend No. 1: Smarter, more responsible, scalable AI

Smarter, more responsible, scalable AI will enable better learning algorithms, interpretable systems and a shorter time to value. However, organisations will require much more from AI systems and need to figure out how to scale the technologies.

Although AI techniques may rely heavily on historical data, historical data may no longer be relevant. AI technology must operate with less data via “small data” techniques and adaptive machine learning. These AI systems must also protect privacy, comply with federal regulations and minimise bias to support an ethical AI. 

Trend No. 2: Composable data and analytics 

Composable data and analytics aim to use components from multiple data, analytics and AI solutions for a flexible, user-friendly experience. Consequently, that will enable leaders to connect data insights to business actions. Gartner client inquiries suggest that most large organisations have more than one “enterprise standard” analytics and business intelligence tool.

Composing new applications from the packaged business capabilities promotes productivity and agility. Furthermore, composable data and analytics encourage collaboration, evolve the organisation’s capabilities, and increase access to analytics. 

Trend No. 3: Data fabric as the foundation 

As data becomes complex and digital business accelerates, data fabric is the architecture that will support composable data and analytics.

Data fabric reduces the time for integration design by 30%, deployment by 30% and maintenance by 70%. This reduction is because technology designs draw on the ability to use/reuse and combine different data integration styles. Furthermore, data fabrics can leverage existing skills and technologies from data hubs, data lakes and data warehouses. Additionally, introduce new approaches and tools for the future. 

Trend No. 4: From big to small and wide data 

Small and wide data solves several problems for organisations dealing with increasingly complex questions on AI and challenges with scarce data use. Comprehensive data enables the analysis of various small and varied unstructured and structured data sources to enhance contextual awareness. In addition, small data can use models requiring less data but still offer valuable insights. 

Trend No. 5: XOps 

The goal of XOps is to achieve efficiencies and economies of scale using DevOps best practices. In addition, it ensures reliability, reusability and repeatability while reducing the duplication of technology and processes and enabling automation. 

Furthermore, these technologies enable the scaling of prototypes and deliver a flexible design and agile orchestration of governed decision-making systems. Overall, XOps will allow organisations to operationalise data and analytics to drive business value. 

Trend No. 6: Engineered decision intelligence 

Decision intelligence is a discipline that includes a wide range of decision-making. This includes conventional analytics, AI and complex adaptive system applications. Moreover, engineering decision intelligence applies to sequences of decisions, grouping them into business processes and even networks of emergent decision-making. 

Therefore, this enables organisations to gain insights needed to drive actions for the business more quickly. When combined with composability and common data fabric, engineered decision intelligence opens up new opportunities to rethink or reengineer how organisations optimise decisions and make them more accurate, repeatable and traceable.

Trend No. 7: Data and analytics as a core business function

Business leaders are beginning to understand the importance of using data and analytics to accelerate digital business initiatives. Instead of being a secondary focus — completed by a separate team — data and analytics are shifting to a core function. However, business leaders often underestimate the complexities of data and end up missing opportunities. If chief data officers (CDOs) are involved in setting goals and strategies, they can increase consistent production of business value by a factor of 2.6X.

Trend No. 8: Graph relates everything 

Graph forms the foundation of modern data and analytics with capabilities to enhance and improve user collaboration, machine learning models and explainable AI. Although graph technologies are not new to data and analytics, there has been a shift in the thinking around them as organisations identify an increasing number of use cases. Furthermore, 50% of Gartner client inquiries around the topic of AI involve a discussion around the use of graph technology. 

Trend No. 9: The rise of the augmented consumer

Traditionally, business users were restricted to predefined dashboards and manual data exploration. Often, this meant data and analytics dashboards were limited to data analysts or citizen data scientists exploring predefined questions.

However, Gartner believes that, moving forward, these dashboards will be replaced with automated, conversational, mobile and dynamically generated insights customised to a user’s needs and delivered to their point of consumption. This shifts the insight knowledge from a handful of data experts to anyone in the organisation. 

Trend No. 10: Data and analytics at the edge 

As more data analytics technologies begin to live outside of the traditional data centre and cloud environments, they’re moving closer to the physical assets. This reduces or eliminates latency for data-centric solutions and enables more real-time value.

Shifting data and analytics to the edge will open opportunities for data teams to scale capabilities and extend impact into different parts of the business. It can also provide solutions for situations where data can’t be removed from specific geographies for legal or regulatory reasons.