Here are the top 10 technology trends that data and analytics leaders should focus on as they look to make essential investments to prepare for a reset.
Trend 1: Smarter, faster, more responsible AI
By the end of 2024, 75% of enterprises will shift from piloting to operationalizing AI, driving a 5X increase in streaming data and analytics infrastructures. Within the current pandemic context, AI techniques such as machine learning (ML), optimization and natural language processing (NLP) are providing vital insights and predictions about the spread of the virus and the effectiveness and impact of countermeasures. AI and machine learning are critical realigning supply and the supply chain to new demand patterns.
Trend 2: Decline of the dashboard
Dynamic data stories with more automated and consumerized experiences will replace visual, point-and-click authoring and exploration. As a result, the amount of time users spend using predefined dashboards will decline. The shift to in-context data stories means that the most relevant insights will stream to each user based on their context, role or use. These dynamic insights leverage technologies such as augmented analytics, NLP, streaming anomaly detection and collaboration.
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.
Trend 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. Data and analytics leaders use X analytics to solve society’s toughest challenges, including climate change, disease prevention and wildlife protection.
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.
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.
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.
Trend 8: 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. These marketplaces and exchanges provide centralized availability and access (to X analytics and other unique data sets, for example) that create economies of scale to reduce costs for third-party data.
Trend 9: Blockchain in data and analytics
Blockchain technologies address two challenges in data and analytics. First, blockchain provides the full lineage of assets and transactions. Second, blockchain provides transparency for complex networks of participants.
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.