Data and analytics are becoming even more critical to future market success, Gartner’s annual analysis reports.

As businesses fight to emerge from the shadow of the COVID-19 pandemic, data and analytics leaders are playing a critical role in response, recovery and preparation for a post-pandemic reset.

According to Rita Sallam, Distinguished VP Analyst, Gartner, data and analytics leadership in this operating environment requires “an ever-increasing velocity and scale of analysis in terms of processing and access”.

Here are Gartner’s top technology trends to focus on the essential investments as we prepare for a market reset.

Trend 1 – AI that is smarter, faster & more responsible

As we reach the end of 2024, 75% of businesses will have shifted from piloting to operationalising artificial intelligence (AI). This will drive a five-fold increase in data streaming and analytics infrastructure.

Smart AI techniques are driving more adaptable and flexible systems for handling challenging business situations, including modelling and simulating complex systems.

The power of AI has been especially telling mid-pandemic, where AI techniques such as machine learning (ML), optimisation and natural language processing are generating critical insights and predictions about the virus’s spread

Edge devices – providing entry points into the core networks of enterprises or service providers – are driving investment in new chip architecture, which is accelerating AI and ML models and workflows. Businesses no longer need to rely on centralised systems that require high bandwidths. Ultimately, scalable AI solutions with higher business impact might be predicted as a result.

To protect against poor decisions, responsible AI will enable model transparency that will result in greater trust in adoption, better human-machine collaboration, and alignment of organisational decisions.

Trend 2 – The dashboard will decline

Dynamic data stories are the way of the future, with more automated and consumerised experiences to replace visual, point-and-click editing and exploration. The most relevant insights will be streamed to users based on context, role or use. With this, the amount of time users spend on predefined dashboards is expected to decline.

Gartner recommends data and analytics leaders regularly evaluate their analytics and business intelligence approaches.

Keys to success in this new technology ecosystem will include augmented analytics, natural language processing, streaming anomaly detection, and collaboration.

Trend 3 – Decision intelligence

Decision intelligence is expected to be a practical focus of more than 33% of large organisations by 2023. This sees the merging of numerous disciplines, including decision management and decision support, and the field of complex adaptive systems.

Decision modelling provides a support framework for data and analytics leaders, as they design and test decision systems in the context of business outcomes and behaviour.

Exploration using decision management and modelling technology when decisions need multiple logical and mathematical techniques must be automated, or documented and audited.

Trend 4 – X analytics

Data and analytics leaders are increasingly using ‘X analytics’ to solve the world’s toughest problems, such as climate change, wildlife protection and disease prevention.

X analytics is an umbrella term coined by Gartner, where X is the data variable for a range of varying unstructured and structured content, such as text, video and audio.

X analytics combined with AI are expected to play a key role in the identification and prediction of, and planning for natural disasters and crises in the future. With tracking the spread and potential treatments for COVID-19, X analytics has been combined with AI to comb through high volumes of research papers, news sources, social media, clinical trials data and public health records.

Cloud vendors are expanding X analytics capabilities with innovations including image, video and voice analytics. Gartner recommends data and analytics leaders explore capabilities available with existing cloud vendors as well as smaller disruptive startups and cloud service providers.

Trend 5 – Augmented data management

Leaders will look to augmented data management to simplify and consolidate operational architectures. This should involve enabling active metadata and increasing automation in excessive data management tasks.

Making use of ML and AI, augmented data management optimises and improves operations. It also converts metadata from auditing, lineage and reporting to power up dynamic systems.

Augmented data management products are able to examine large operational data samples, such as actual queries, performance data and diagrams. An augmented engine draws on existing usage and workload data to finetune operations, optimising configuration, performance and security.

Trend 6 – Workflows on the cloud

Leaders will prioritise workloads that can exploit cloud capabilities and focus on optimising cost optimisation in your move to the cloud.

Gartner predicts by 2022, public cloud services will be critical for 90% of data and analytics innovation.

The big question for data and analytics leaders will be balancing cost with the capacity to meet the workload’s performance requirements. Aligning service and use cases correctly is a challenge, which results in unnecessary governance and integration overhead.

Trend 7 – Colliding worlds of data and analytics

Gartner predicts an accelerated convergence between data and analytics that will bring about new technologies and capabilities, but also create new IT roles such as information explorer, and consumer and citizen developer.

Traditionally, data and analytics capabilities have been seen in silos – considered and managed as if they were distinct entities.

This has been disrupted by end-to-end workflows enabled by augmented analytics, that see data and analytics as colliding with increased interaction and collaboration.

Leaders should respond to this trend by incorporating both data and analytics tools and capabilities into the analytics stack. Beyond tools, the focus will be on the people and processes that will foster strong communication and collaboration. Use an augmented approach across your entire data and analytics stack to bolster your data process coherently.

Trend 8 – Data marketplaces and exchanges

Leaders should establish a fair and transparent approach to monetising data assets through data marketplaces. Defining data governance standards that ecosystem partners can rely on will be key to this.

By 2022, sellers or buyers of data via formal online data marketplaces will make up 35% of large organizations, up from 25% in 2020.

Data marketplaces and exchanges are single platforms that consolidate third-party data offerings. This provides centralised availability of and access to data sets, creating economies of scale and reducing costs for third-party data.

Trend 9 – Blockchain to supplement data and analytics

Leaders should work to position blockchain – the recorded cryptocurrency transactions linked across peer-to-peer networks – as supplementary to their current data management infrastructure.

Blockchain addresses two data and analytic challenges. It provides the entire lineage of assets and transactions, as well as transparency for complex participant networks.

By 2021, Gartner predicts ledger database management systems will replace most permissioned blockchain uses. Ledger database management systems will be a more attractive way of single-enterprise auditing data sources. Less common will be bitcoin and smart contract use cases.

Trend 10 – Relationships underpinning data and analytics’ value

By 2023, graph technologies will facilitate rapid contextualisation for decision-making in 30% of organisations worldwide. Leaders will use graph analytics to reveal hidden relationships in data and provide new capabilities for reviewing data.

Graph analytics is a set of techniques that enable exploration of relationships between entities of interest, such as organisations, people and transactions.

Mid-pandemic, analysis from graph technologies has helped with contact tracing, drawing on data sources such as geospatial data on people’s phones and photos with facial recognition systems.