The following is the Top 10 data and analysis trend in 2021 From Gartner recommended by recordtrend.com. And this article belongs to the classification: Gartner, big data.
When COVID-19 was wreaking havoc, companies that used traditional analytical techniques and relied heavily on historical data were aware of one important thing: many of these models were no longer useful. The epidemic has changed almost everything, making a lot of data useless.
The forward-looking data and analysis team is shifting from traditional artificial intelligence technology relying on “big” data to “small” data with less quantity but more diversity.
One of the top ten data and analysis trends released by Gartner in 2021 is to shift from big data to small and wide data. These ten trends are business, market and technology trends that data and analysis leaders must pay attention to.
Rita sallam, vice president of distinguished research at Gartner, said: “these data and analysis trends can help business institutions and society cope with the disruptive changes, great uncertainties and opportunities they bring in the next three years. Data and analysis leaders must actively study how to follow the trend and make key task investments to speed up their own prediction, transformation and response capabilities according to these trends. “
Each trend can be grouped into one of the following three themes:
Accelerate the transformation of data and analysis: use AI innovation, improved composability and agile and efficient integration of diversified data sources.
Achieve business value operations through more effective xops: optimize decisions and transform data and analysis into an integral part of the business.
Distributed entities (people and things): data and insight need to be flexibly linked to enhance the capabilities of more people and things.
Trend 1: smarter, more responsible, scalable AI
A more intelligent, responsible and scalable AI will optimize the learning algorithm, make the system more explanatory and speed up the value realization. Enterprise organizations will start to put forward more requirements for AI systems, and they need to be clear about how to expand the scale of technology. But so far, it’s still a problem.
Traditional AI technologies rely heavily on historical data, and the changes brought by COVID-19’s business environment make historical data ineffective. This means that AI technology must be able to run on less data through “small data” technology and adaptive machine learning. In order to be morally constrained AI, these AI systems must also protect privacy, comply with regulations, and minimize bias.
Trend 2: composable data and analytics
The assembled data analysis architecture uses components from multiple data, analysis and AI solutions to achieve a flexible, user-friendly and practical experience, enabling executives to connect data insights with business actions. According to Gartner customer inquiries, most large enterprises have more than one “enterprise standards” analysis and business intelligence tool.
Combining multiple business capability components into new applications promotes productivity and agility. Assembly data analysis can not only encourage cooperation and improve the analysis ability of enterprises, but also increase the use of analysis.
Trend 3: Data fabric as the foundation
With the increasing complexity of data and the accelerated development of digital business, data weaving architecture has become the infrastructure supporting assembly data analysis and its various components.
Data weaving can reduce 30% of integration design time, 30% of deployment time and 70% of maintenance time because it can use / reuse and combine different data integration methods in technical design. In addition, data weaving can not only use the existing data center, data lake and data warehouse technology and skills, but also add new methods and tools in the future.
Trend 4: from big to small and wide data
Facing the increasingly complex AI problems and the challenge of data use case scarcity, enterprises are replacing big data with small and wide data to solve many problems. With the “x analysis” technology, that is to use wide data to analyze various small and diverse (wide) unstructured and structured data sources and give play to their collaborative effect, so as to enhance situational awareness and decision-making. As the name suggests, small data refers to a data model that can use less data but still provide practical insights.
Trend 5: xops
The goal of xops (data, machine learning, model, platform) is to use the best practices of Devops to achieve efficiency and scale economy, reduce the duplication of technology and process and realize automation while ensuring reliability, reusability and repeatability.
This technology will extend the prototype and provide flexible design and agile coordination for the governed decision system. In general, xops will enable enterprise organizations to promote the realization of business value through the operation of data and analysis.
Trend 6: engineered decision intelligence
Engineering decision intelligence is a subject which includes traditional analysis, artificial intelligence and complex adaptive system applications. Engineering decision intelligence is not only suitable for single decision, but also for continuous decision. This technology can group decisions into business processes or even new decision networks.
With this technology, enterprise organizations can get the insights needed to drive business actions faster. When combined with assemblability and common data weaving architecture, engineering decision intelligence will bring new possibilities for reconsideration or redesign of enterprise organization decision optimization mode, and improve the accuracy, repeatability and traceability of decision.
Trend 7: data and analysis as a core business function
Business leaders are learning the importance of using data and analysis to accelerate digital business planning. Data and analysis is no longer just a secondary focus completed by an independent team, but becomes a core function. But enterprise leaders often underestimate the complexity of data, and finally miss the opportunity. If the chief data Officer (CDO) can participate in the formulation of goals and strategies, they can improve the continuous output efficiency of business value by 2.6 times.
Trend 8: graph related everything
Graph technology has become the basis of modern data and analysis, which can enhance and improve user collaboration, machine learning model and interpretable artificial intelligence. Although graph technology is not a new thing for data and analysis, the way of thinking around graph technology has changed with the increasing use cases found by enterprises. In fact, as many as 50% of Gartner customer inquiries about artificial intelligence involve discussions about the use of chart technology.
Trend 9: the rise of the audience consumer
Previous enterprise users were limited to predefined dashboards and manual data exploration. In general, only data analysts or citizen data scientists exploring predefined issues can use data and analysis dashboards.
However, Gartner believes that in the future, these dashboards will be replaced by automated, conversational, mobile and dynamically generated insights, and these insights will be customized according to user needs and delivered to users when they need to consume the data, so that anyone in the enterprise can obtain the insights and knowledge that only a few data experts can master.
Trend 10: data and analysis at the edge
The data analysis technology existing outside the traditional data center and cloud environment is increasing, and they are moving towards the object. This can reduce or eliminate latency and increase real-time value from data centric solutions.
By moving data and analysis to the edge, data teams will have the opportunity to expand their capabilities and extend change to different parts of the business. At the same time, it also solves the problem that data cannot be moved from a specific region due to legal or regulatory reasons.
Read more: Gartner: Magic Quadrant of big data analysis platform in 2018 Gartner: real time analysis and batch processing of big data in 2013 Gartner: 75% of enterprises will invest in big data in the next two years Data has become a new “gene” in the investment market Gartner: eight big data myths that CIOs should eliminate most Gartner: data shows that 42% of IT executives have invested in big data
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