Gartner: Data analysis helps build a new equation for business value

Data analysis capabilities have increasingly become a business capability, so in 2022, Gartner proposed the concept of "building a new equation of business value" in response to data analysis trends.

What is the "new equation"? Sun Xin, senior research director at Gartner, said that more departments of enterprises need data analysis to realize more value, bring more thinking about business models, and better help enterprises achieve digital transformation.

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Gartner Senior Research Director Sun Xin

According to this concept, this year Gartner The major data and analytics trends released by data and analytics (D&A) leaders in the enterprise in 2022 are divided into three major themes: stimulating enterprise vitality and diversity, enhancing people capabilities and decision-making, and institutionalizing trust.

Stimulate enterprise vitality and diversity

Adaptive artificial intelligence system: Gartner proposed the "AI engineering" initiative, which is expected to be implemented by 2026 In 2017, enterprises used AI engineering methods to implement adaptive artificial intelligence systems, which can more effectively help enterprises operate more AI models, 25% more than those without this initiative.

Data-centered artificial intelligence: Data-centered artificial intelligence will continue to develop, and it will expand into more and more disciplines, so enterprises need a more robust Data management model to complete the ability of AI operations.

Metadata-driven data weaving: If enterprises can better utilize data weaving into metadata to manage data sources, they can effectively reduce the tedious data management work in the past.

Always share data: More and more companies will consider sharing data in a way that can be governed, focusing on how to discover more relevant data through automated means, using open OpenData ways to explore more of the possibilities of your own data.

Empowering people and decision-making

Context-rich analytics: To provide insights relevant to decision makers, data and analytics leaders must Provides contextually rich analytics created using business module components.

Business module assembled data and analysis:The past technology may be a form of solidified, single software, but future technology will use more assembled technologies to complete applications of construction.

Decision-centered data and analysis: Enterprises need more and more people who can make suggestions and plans based on data analysis for corporate decisions at a higher level. Gartner proposed a decision-making intelligence model to help enterprises manage the decision-making chain from a top-level design perspective.

Insufficient personnel skills and literacy: Enterprises need to allow users to tell the business results after using data analysis, so as to influence more people.

Institutionalization of trust

Interconnected governance: Establish a cross-organization, cross-business function, and even cross-region virtual data and analysis governance layers to achieve cross-enterprise governance outcomes. Many companies in China will consider establishing a "chief data officer" office. The office will have a data governance committee. The governance committee will cooperate with some legal departments to implement "internet governance" at a higher-level virtual layer.

AI risk management: Many companies are more driven by supervision and compliance and are doing some model governance, so they are completely passive when making AI models. Gartner hopes that enterprises will pay attention to trust risks and security management for AI governance.

Manufacturer and regional ecosystem: When enterprises establish their own data analysis ecosystem, they should pay more attention to the compatibility between manufacturers.

Expansion to the edge: Data and analytics activities are increasingly performed on distributed device servers and gateways outside of data centers or public cloud infrastructure.

Nowadays, enterprises have a lot of data, but these data have not been activated. Enterprises often passively implement data analysis projects and behaviors, and do not take the initiative to bring out the potential value of the data. Sun Xin believes that how to enable more and more users to make decisions based on data has become a challenge for enterprises at this stage. Data analysis on the cloud has become a first choice. At the same time, enterprises also hope to use some "self-service" tools to allow Business users make decisions faster.

Gartner also made a relatively bold prediction in this year’s forecast. More and more data analysis activities will start with digital office software. When business demands are raised, data analysis will be completed in digital office software. And based on data analysis, some business actions can be completed in digital office software, completing the closed loop of data analysis.

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