Gartner releases Top 10 Trends in Data and Analytics for 2020

OCT 19: Gartner released the Top 10 Trends in Data and Analytics Report for 2020 .

“In the face of unprecedented market shifts, data and analytics leaders require an ever-increasing velocity and scale of analysis in terms of processing and access to accelerate innovation and forge new paths to a post-COVID-19 world”

Check, check, check... ✔

Process Tempo checks off several of the trends included in this report, confirming how well the company is positioned in this space both now and in the future. Some of the notable relevant trends include:

Trend 3: Decision Intelligence

"By 2023, more than 33% of large organizations will have analysts practicing decision intelligence, including decision modeling.

Decision intelligence brings together a number of disciplines, including decision management and decision support. It encompasses applications in the field of complex adaptive systems that bring together multiple traditional and advanced disciplines.

It provides a framework to help data and analytics leaders design, compose, model, align, execute, monitor, and tune decision models and processes in the context of business outcomes and behavior.

Explore using decision management and modeling technology when decisions need multiple logical and mathematical techniques, must be automated or semi-automated, or must be documented and audited."

Trend 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. Using the existing usage and workload data, an augmented engine can tune operations and optimize configuration, security and performance.

Data and analytics leaders should look for augmented data management enabling active metadata to simplify and consolidate their architectures, and also increase automation in their redundant data management tasks."

Trend 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.

Data and analytics leaders need to prioritize workloads that can exploit cloud capabilities and focus on cost optimization and other benefits such as change and innovation acceleration when moving to cloud."

Trend 7: Data and analytics worlds collide

"Data and analytics capabilities have traditionally been considered distinct capabilities and managed accordingly. Vendors offering end-to-end workflows enabled by augmented analytics blur the distinction between once separate markets.

The collision of data and analytics will increase interaction and collaboration between historically separate data and analytics roles. This impacts not only the technologies and capabilities provided, but also the people and processes that support and use them. The spectrum of roles will extend from traditional data and analytics roles in IT to information explorer, consumer and citizen developer as an example.

To turn the collision into a constructive convergence, incorporate both data and analytics tools and capabilities into the analytics stack. Beyond tools, focus on people and processes to foster communication and collaboration. Leverage data and analytics ecosystems enabled by an augmented approach that have the potential to deliver coherent stacks."

Trend 10: Relationships form the foundation of data and analytics value

"By 2023, graph technologies will facilitate 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.

For example, as the world scrambles to respond to current and future pandemics, graph technologies can relate entities across everything from geospatial data on people’s phones to facial-recognition systems that can analyze photos to determine who might have come into contact with individuals who later tested positive for the coronavirus.

Consider investigating how graph algorithms and technologies can improve your AI and ML initiatives

When combined with ML algorithms, these technologies can be used to comb through thousands of data sources and documents that could help medical and public health experts rapidly discover new possible treatments or factors that contribute to more negative outcomes for some patients.

Data and analytics leaders need to evaluate opportunities to incorporate graph analytics into their analytics portfolios and applications to uncover hidden patterns and relationships. In addition, consider investigating how graph algorithms and technologies can improve your AI and ML initiatives."

Read the full report here.