When COVID-19 broke out, companies using traditional analytics techniques (which rely heavily on large amounts of historical data) realized something important: Many of these models were no longer useful. Basically, the pandemic changed everything, and a lot of data became unproductive.

In turn, forward-thinking data and analytics teams are shifting from traditional artificial intelligence (AI) techniques that rely on “big” data to a kind of analytics that requires less data, or “small” and more. varied.

“These trends in data and analytics can help companies and society cope with disruptive change, radical uncertainty and the opportunities that come with it.”

The transition from big data to small, big data is one of the top data and analytics trends for 2021. These trends represent business, market and technology dynamics that analytics and data managers cannot afford to ignore.

These data and analytics trends can help organizations and society cope with disruptive change, radical uncertainty and the opportunities they bring over the next three years. Those responsible for data and analytics must proactively examine how to take advantage of these trends in mission-critical investments that accelerate their abilities to anticipate, change and respond.

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1 Each of the trends corresponds to one of these three main themes:

Each of the trends corresponds to one of these three main themes:

Accelerating data and analytics change: Leverage AI innovations, improve composability, and more agilely and efficiently integrate more diverse data sources.
More effective implementation of business value through XOps – Enables improved decision making and makes data and analytics an integral part of the business.
All distributed: requires the flexible relationship of data and perspectives to empower an even wider audience of people and objects.

Trend No. 1: smarter, more responsible and more scalable AI

A smarter, more responsible and more scalable AI will improve learning algorithms, interpretable systems and reduce evaluation time. Organizations will start to need a lot more from AI systems and will have to figure out how to scale the technologies, something that until now has been a challenge.

Although traditional AI techniques can rely heavily on historical data, considering 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 using “small data” techniques and adaptive machine learning. These AI systems must also protect privacy, comply with federal regulations, and minimize bias to support ethical AI.

Trend No. 2: composable data and analytics

The goal of composable data and analytics is to use components from multiple AI, analytics and data solutions for a flexible, easy-to-use and usable experience that will enable decision makers to connect data insights to business actions. Most of the customer inquiries suggest that most large organizations have more than one “company standard” business intelligence and analytics tool.

Composing new applications from each other’s packaged business capabilities promotes productivity and agility. Composable data and analytics will not only foster collaboration and develop your organization’s analytical capabilities, it will also increase access to analytics.

Trend No. 3: the data fabric as a basis

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

Data fabric reduces integration design time by 30%, implementation by 30%, and maintenance by 70% as technology designs take advantage of the ability to use or reuse and combine different integration styles of data. Additionally, data fabrics can leverage existing skills and technologies in data centers, data lakes, and data warehouses, while introducing new approaches and tools for the future.

Trend No. 4: from big data to small and big data

Small and big data, as opposed to big data, solves a number of problems for organizations facing increasingly complex questions about AI and the challenges posed by scarce data use cases. Big data, leveraging “X-analysis” techniques, enables analysis and synergy of a variety of small and varied (large), unstructured and structured data sources to improve contextual insight and decisions. Small data, as the name implies, has the ability to use data models that require less data, but still provide useful information.

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 duplication of technology and processes and automation is allowed.

These technologies will allow prototype scaling and offer flexible design and agile orchestration of governed decision-making systems. Overall, XOps will enable organizations to put data and analytics to work to drive business value.

Trend No. 6: designed decision intelligence

Decision intelligence is a discipline that includes a wide variety of decision making, such as conventional analytics, AI, and complex adaptive systems applications. Engineered decision intelligence is applied not only to individual decisions, but also to decision sequences, grouping them into business processes and even emerging decision-making networks.

This enables organizations to more quickly obtain the information they need to drive action for the business. When combined with composability and a common data fabric, engineered decision intelligence opens up new opportunities to rethink or redesign how organizations optimize decisions and make them more accurate, repeatable, and traceable.

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

CIOs are beginning to understand the importance of using data and analytics to accelerate digital business initiatives. Rather than being a secondary focus (completed by an independent team) data and analysis are becoming a central function. However, CEOs often underestimate the complexities of data and end up missing out on opportunities. If the Chief Data Officers (CDOs) are involved in setting goals and strategies, they can multiply the consistent production of business value by 2.6 times.

Trend No. 8: graphs relate everything

Graphs are the modern database and analytics with capabilities to enhance user collaboration, Machine Learning models, and explainable AI. Although graph technologies are not new to data and analytics, there has been a shift in thinking around them as organizations identify an increasing number of use cases.

Trend No. 9: the rise of the empowering consumer

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

Moving forward, these dashboards will be replaced by automated, conversational, mobile, and dynamically generated information, customized to user needs and delivered to the point of consumption. This shifts knowledge of information 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 traditional cloud and data center environments, they move closer to physical assets. This reduces or eliminates latency for data-centric solutions and enables you to get more value in real time.

Moving data and analytics to the edge will open opportunities for data teams to expand capabilities and extend their impact to different parts of the business. It can also offer solutions for situations where data cannot be removed from certain geographic areas for legal or regulatory reasons.

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