Gartner Releases Ten Big data and Analysis Trends in 2023
Time:2023-06-15
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Gartner released ten Big data and analysis (D&A) trends in 2023. These trends can guide data and analysis leaders in predicting changes, transforming huge variables into new business opportunities, and thus opening up new sources of value for their corporate institutions.
Gareth Herschel, Vice President of Research at Gartner, stated, "Enterprise organizations need to obtain verifiable value on a large scale, and this demand has become the driving force behind these data and analysis trends. Chief Data and Analysis Officer (CDAO) And data and analysis leaders must understand the best methods that can drive data and analysis applications from relevant parties in their corporate institutions. This means they need more high-quality analysis and insights that take into account human psychology and values
At the Gartner Data and Analysis Summit, Gartner analysts proposed the top ten Big data and analysis trends that enterprises and IT leaders must participate in and incorporate into their data and analysis strategies (see Figure 1).
Figure 1 Ten Big data and Analysis Trend in 2023
Source: Gartner (May 2023)
Trend 1: Value Optimization
Most data and analysis leaders are striving to use business terminology to articulate the value they create for corporate institutions. In order to optimize value using a combination of data, analysis, and artificial intelligence (AI) from corporate institutions, they need to utilize a comprehensive set of value management capabilities such as value story telling, value stream analysis, investment ranking and prioritization, and business outcome measurement to ensure the expected value is achieved.
Herschel stated, "Data and analysis leaders must optimize value by establishing value stories, establishing clear connections between data and analysis initiatives and organizational key task priorities
Trend 2: AI Risk Management
With the increasing use of artificial intelligence (AI), enterprises are facing new risks that must be mitigated, such as Moral hazard, training data poisoning, fraud detection and avoidance. Managing AI risks is not just about complying with regulations, but effective AI governance and responsible AI practices are equally crucial for gaining trust from stakeholders and promoting the adoption and use of AI.
With the increasing use of artificial intelligence (AI), enterprises are facing new risks that must be mitigated, such as Moral hazard, training data poisoning, fraud detection and avoidance. Managing AI risks is not just about complying with regulations, but effective AI governance and responsible AI practices are equally crucial for gaining trust from stakeholders and promoting the adoption and use of AI.
Trend 3: Observability
Observability is a characteristic that helps understand the behavior of data and analysis systems and raises questions about their behavior.
Herschel stated, "Observability enables enterprise organizations to reduce the time required to identify the root causes of performance issues and make timely and cost-effective business decisions using reliable and accurate data. Data and analysis leaders need to evaluate data observability tools to understand the needs of key users and determine how to integrate these tools into the entire enterprise ecosystem
Trend 4: Data sharing becomes necessary
Data sharing is divided into internal (between departments or subsidiaries) and external (between parties not owned by or controlled by your corporate organization) data sharing. Enterprise institutions can "productize" data and analyze assets as a deliverable or shared product.
Kevin Gabbard, senior Chief research officer of Gartner, said: "Data sharing collaboration including external data sharing collaboration improves the value of data sharing by adding previously created reusable data assets. Data weaving design can be adopted to achieve a single data sharing architecture across internal and external heterogeneous data sources."
Trend 5: Data and Analysis Sustainability
In order to improve sustainability, data and analysis leaders not only need to provide analysis and insights for enterprise environmental, social, and governance (ESG) projects, but also must strive to optimize their processes, which may bring them significant benefits. Data and analysis, as well as AI practitioners, are increasingly aware of their growing energy footprint. Therefore, they began to adopt various new approaches, such as using renewable energy in (cloud) data centers, using more energy-efficient hardware, using small data and other machine learning (ML) technologies, etc.
Trend 6: Practical Data Weaving
Data weaving is a data management design approach that utilizes various types of metadata to observe, analyze, and recommend data management solutions. By aggregating and enriching the semantics of underlying data, and continuously analyzing metadata, data weaving can generate alerts and recommendations that humans and systems can execute. It enables business users to consume data with confidence and helps less skilled citizen developers master more comprehensive process integration and modeling capabilities.
Trend 7: Emerging AI
ChatGPT and generative AI are the "pioneers" of the upcoming emerging AI trend. Emerging AI will change the way most enterprises operate from the perspectives of scalability, versatility, and adaptability. The next wave of AI will enable businesses to apply AI to situations that are currently not feasible, making AI more universal and valuable.
Trend 8: Integrating and Composable Ecosystems
The data and analysis platform designed and deployed by the integrated data and analysis ecosystem achieves consistency in operation and functionality through seamless integration, governance, and technological interoperability. Ecology achieves composability by building, assembling, and deploying configurable applications and services.
A suitable architecture can improve the modularity, adaptability, and flexibility of data and analysis systems, enabling them to have dynamic scalability and become more streamlined and efficient, thus meeting the constantly growing and changing business needs, and evolving with inevitable changes in business and operational environments.
Trend 9: Consumers becoming creators
The time spent by users on predefined dashboards will be replaced by conversational, dynamic, and embedded user experiences that meet the immediate needs of specific content consumers.
Enterprise organizations can expand the adoption and impact of analytics by providing content consumers with the easy-to-use automation and embedded insight and conversational experience they need to become Content creation.
Trend 10: Humans remain key decision-makers
Not every decision can or should be automated. The data and analysis team is emphasizing decision support and the role of humans in automating and enhancing decision-making.
Herschel said, "If a business organization focuses solely on promoting decision automation while neglecting the role of humans in decision-making, it will become a data-driven organization without conscience and a fragmented mindset. Business organizations need to emphasize the integration of data and analysis with human decision-making in their data literacy plans
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