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 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.
To monetize data assets through data marketplaces, data and analytics leaders should establish a fair and transparent methodology by defining a data governance principle that ecosystems partners can rely on.
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.
Outside of limited bitcoin and smart contract use cases, ledger database management systems (DBMSs) will provide a more attractive option for single-enterprise auditing of data sources. By 2021, Gartner estimates that most permissioned blockchain uses will be replaced by ledger DBMS products.
Data and analytics should position blockchain technologies as supplementary to their existing data management infrastructure by highlighting the capabilities mismatch between data management infrastructure and blockchain technologies.
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.
This article has been updated from the June 9, 2020 original to reflect new events, conditions and research.