AI Spread, Natural Language Control to Lead Data & Analytics Trends Post-Virus

Gartner compiled a list of top 10 data and analytics (D&A) technology trends to help organizations prepare for work demands in the post-pandemic world, with estimates showing the shift within three to four years.

Rita Sallam, research vice president at Gartner, said the acceleration of better AI tools and rapid integration will be caused in part by the COVID-19 crisis. The research group predicts that by the end of 2024, 75% of businesses will shift from introducing to widely using artificial intelligence (AI), driving a “5 times increase in streaming data and analytics infrastructures.”

“To innovate their way beyond COVID-19, data and analytics leaders require an ever-increasing speed and scale of analysis in terms of both processing and access to succeed,” Sallam said in the report summary.

Gartner’s data and analytics report predicts AI for data crunching will be faster and better by 2024. (Source: Wikimedia Commons)

Machine learning (ML), optimization and natural language processing (NLP) are providing already central to tracking and predicting the spread of the virus and the effectiveness and impact of countermeasures. Other “smarter” AI techniques such as reinforcement learning and distributed learning are creating more adaptable and flexible systems to handle complex business situations; for example, agent-based systems that model and assist complex systems.

The rest of the summary of predictions include:

2. Decline of the Dashboard

Dynamic data stories with more automated and consumer-oriented experiences will replace visual, point-and-click authoring and exploration. Users will spend less time on dashboards and more on augmented analytics or Natural Language Processing (NLP), means that the most relevant insights will stream to each user based on their context, role or use.

3. Decision Intelligence

By 2023, more than 33% of large organizations will have analysts practicing decision intelligence, including decision modeling. Decision intelligence brings together several disciplines, including decision management and decision support. It provides a framework to help data and analytics leaders design, model, align, execute, monitor and tune decision models and processes in the context of business outcomes and behavior.

4. X Analytics

Gartner coined the term “X analytics” to be an umbrella term, where X is the data variable for a range of different structured and unstructured content such as text analytics, video analytics, audio analytics, etc. During the pandemic, AI has combed through thousands of research papers, news sources, social media posts and clinical trials data to help medical and public health experts predict disease spread, capacity-plan, find new treatments and identify vulnerable populations. X analytics combined with AI and graph analytics will play a key role in identifying, predicting and planning for natural disasters and other crises.

5. Augmented Data Management

Augmented data management uses ML and AI techniques to optimize and improve operations. It also converts metadata from being used in auditing, lineage and reporting to powering dynamic systems. Augmented data management products can examine large samples of operational data, including actual queries, performance data and schemas. An augmented engine can tune operations and optimize configuration, security and performance.

6. Cloud is a Given

By 2022, public cloud services will be essential for 90% of data and analytics innovation. As data and analytics moves to the cloud, data and analytics leaders still struggle to align the right services to the right use cases, which leads to unnecessary increased governance and integration overhead. The question for data and analytics is moving from how much a given service costs to how it can meet the workload’s performance requirements beyond the list price.

7. Data and Analytics Worlds Collide

Data and analytics capabilities have traditionally been considered distinct entities and managed accordingly. Vendors offering end-to-end workflows enabled by augmented analytics blur the distinction between the two markets. The collision of data and analytics will increase interaction and collaboration between historically separate data and analytics roles. This impacts the technologies, capabilities and people that support and use them.

8. Growth of Data Marketplaces and Exchanges

By 2022, 35% of large organizations will be either sellers or buyers of data via formal online data marketplaces, up from 25% in 2020. Data marketplaces and exchanges provide single platforms to consolidate third-party data offerings and reduce costs for third-party data.

Bitcoin is one of the blockchain companies that could be impacted by ledger database management systems (DBMS).

9. Blockchain to Decline in Data and Analytics

Blockchain technologies address two challenges in data and analytics. First, it provides the full lineage of assets and transactions. Second, it provides transparency for complex networks of participants. Outside of limited bitcoin and smart contract use cases, ledger database management systems (DBMSs) will be a more attractive option for single-enterprise auditing of data sources. By 2021, Gartner estimates that most permitted blockchain uses will be replaced by ledger DBMS products.

10. Relationships Key to Data and Analytics Value

By 2023, graph technologies will create rapid contextualization for decision making in 30% of organizations worldwide. Graph analytics is a set of analytic techniques that allows for the exploration of relationships between entities of interest such as organizations, people and transactions. It helps data and analytics leaders find unknown relationships in data and review data not easily analyzed with traditional analytics.