By Rita Sallam, Distinguished VP Analyst, Gartner
From artificial intelligence to small data and graph technology, data and analytics leaders should think about leveraging these trends.
The pandemic changed everything, rendering a lot of data useless. Forward-looking data and analytics teams are pivoting from traditional AI techniques relying on “big” data to a class of analytics that requires less, or “small” and more varied.
Transitioning from big data to small and wide data is one of the Gartner top data and analytics trends for 2021. These trends represent business, market and technology dynamics that data and analytics leaders cannot afford to ignore.
These data and analytics trends can help organizations and society deal with disruptive change, radical uncertainty and the opportunities they bring over the next three years. 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 fit under one of these three main themes:
• Accelerating change in data and analytics: Leveraging innovations in AI, improved composability, and more agile and efficient integration of more diverse data sources.
• Operationalizing business value through more effective XOps: Enables better decision making and turning data and analytics into an integral part of business
• 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 shorter time to value. Organizations will begin to require a lot more from AI systems, and they’ll need to figure out how to scale the technologies — something that up to this point has been challenging.
Although traditional AI techniques may rely heavily on historical data, given how COVID-19 has changed the business landscape, historical data may no longer be relevant. This means that AI technology must be able to operate with less data via “small data” techniques and adaptive machine learning. These AI systems must also protect privacy, comply with federal regulations and minimize bias to support an ethical AI.
Trend No. 2: Composable data and analytics
The goal of composable data and analytics is to use components from multiple data, analytics and AI solutions for a flexible, user-friendly and usable experience that will enable leaders to connect data insights to business actions. Gartner client inquiries suggest that most large organizations have more than one “enterprise standard” analytics and business intelligence tool.
Composing new applications from the packaged business capabilities of each promotes productivity and agility. Not only will composable data and analytics encourage collaboration and evolve the analytics capabilities of the organization, it increases access to analytics.
Trend No. 3: Data fabric as the foundation
As data becomes increasingly complex and digital business accelerates, data fabric is the architecture that will support composable data and analytics and its various components.
Data fabric reduces time for integration design by 30%, deployment by 30% and maintenance by 70% because the technology designs draw on the ability to use/reuse and combine different data integration styles. Plus, data fabrics can leverage existing skills and technologies from data hubs, data lakes and data warehouses while also introducing new approaches and tools for the future.
Trend No. 4: From big to small and wide data
Small and wide data, as opposed to big data, solves a number of problems for organizations dealing with increasingly complex questions on AI and challenges with scarce data use cases. Wide data — leveraging “X analytics” techniques — enables the analysis and synergy of a variety of small and varied (wide), unstructured and structured data sources to enhance contextual awareness and decisions. Small data, as the name implies, is able to use data models that require less data but still offer useful insights.
Trend No. 5: XOps
The goal of XOps (data, machine learning, model, platform) is to achieve efficiencies and economies of scale using DevOps best practices — and to ensure reliability, reusability and repeatability while reducing the duplication of technology and processes and enabling automation.
These technologies will enable scaling of prototypes and deliver a flexible design and agile orchestration of governed decision-making systems. Overall, XOps will enable organizations to operationalize 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, including conventional analytics, AI and complex adaptive system applications. Engineering decision intelligence applies to not just individual decisions, but also to sequences of decisions, grouping them into business processes and even networks of emergent decision making.
This enables organizations to more quickly gain insights needed to drive actions for the business. When combined with composability and a common data fabric, engineered decision intelligence opens up new opportunities to rethink or reengineer how organizations optimize 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 is 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 organizations identify an increasing number of use cases. In fact, as many as 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 restricted 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 customized 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 organization.
Trend No. 10: Data and analytics at the edge
As more data analytics technologies begin to live outside of the traditional data center 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.