Top Trends in Data and Analytics for 2021 by GARTNER

Distributed everything — data, people and devices — is accelerating. Using graph techniques to uncover connections in combinations of diverse data at scale enriches data management, analytics, AI and machine learning (ML), and enables innovation. Leveraging distributed D&A that resides in edge computing environments, while giving every distributed user dynamic insights, represents new opportunities for competitive differentiation and operationalizing business value.

Distributed Everything

Trend 8: Graph Relates Everything

Analysis by: Afraz Jaffri, Ankush Jain, Jim Hare, Pieter den Hamer

 

SPA: By 2025, graph technologies will be used in 80% of data and analytics innovations, up from 10% in 2021, facilitating rapid decision making across the enterprise.

 

Description:

Graph technologies encompass a wide variety of solutions that work with data represented as a set of nodes and edges instead of tables, rows and columns. It allows us to find relationships between people, places, things, events, locations etc. across diverse data. This structure intuitively models relationships between entities and can capture business knowledge, making it easier to perform queries and answer questions. In addition, modeling data as a graph opens up new analytical insights through the use of graph algorithms.

Graphs are forming the foundation of many modern data and analytics capabilities. Increased understanding and collaboration with business users, organizing and preparing data for downstream processes, uncovering hidden insights, improving ML model creation and providing explainable AI are just some of the uses driven by different graph technologies and techniques.

 

Why Trending:

• Complex business problems require contextual awareness and understanding the variable nature of connections and strengths across multiple entities, such as organizations, people or transactions. Critical business questions that used to take months to answer can now be solved in minutes.
• Graphs form the foundation of modern D&A, with capabilities to enhance and improve user collaboration, ML models and explainable AI. The recent Gartner AI in Organizations Survey demonstrates that graph techniques are increasingly prevalent as AI maturity grows, going from 13% adoption when AI maturity is lowest to 48% when maturity is highest.
• No- and low-code tools that enable visual exploration and interaction with a graph are enabling insights to be found without the need for graph query languages.
• Improved, scalable and lower-cost processing options, including cloud-based services and dedicated hardware, are making graph analytics and databases prime candidates for accelerated adoption.
• Knowledge graphs can form a key component of data fabrics and give structure to images, audio, video and natural languages. They do so by exposing metadata and business rules, enabling data scientists to quickly identify and use the data they need while preserving context and representing all forms of data in a standard queryable format.

Implications:

• A change in thinking and development of a “graph mindset” are taking place as more organizations identify use cases that graph techniques can solve. Up to 50% of Gartner inquiries on the topic of AI involve discussion of the use of graph technology.
• The number of products that incorporate graph technology will increase. Within these products, the use of graphs may or may not be visible to end users, resulting in duplication and redundancy. There needs to be an understanding of when an underlying graph model can be exposed, and how multiple graphs can be combined.
• AI solutions will evolve from being based on one type of model, or ensemble of models, to being made of composite models, with graph techniques playing a prominent role. The use of graph techniques will require a broader and deeper set of data science and AI skills with specialist roles, such as graph engineer and ontology manager, appearing, as well as existing roles in data science teams becoming proficient in graph techniques.
• Graph technology underpins the creation of richer semantic models that can enhance augmented analytics models, as well as the richness of conversational analytics. Organizations that use graphs and semantic approaches for natural language technology projects will have less technical debt than those that do not.

Actions:

• Complement traditional analytics with graph technology when the primary business questions are about the relationships between data rather than data values themselves.
• Deduce actual relationships among data in multiple data stores and identify enforced and implied relationships in the data across the organizational silos by taking advantage of graph-enabled data and metadata management capabilities..
• Examine business processes that have a high potential for optimization through the use of graph techniques and algorithms by creating a conceptual graph domain model for the process and testing scenarios that graph algorithms could solve.
• Identify potential use cases that can be simplified or accelerated with graphs for ML by evaluating existing models that require intensive data preparation and feature selection workflows.

Fonte: https://www.gartner.com/doc/reprints?id=1-2596RUH7&ct=210219&st=sb&aliId=eyJpIjoiV1BjeVpiejVqUHJYWFA0diIsInQiOiJteG4xMnFxUEE4UjFlXC9mVXRBbnV3UT09In0%253D