Top 10 Data and Analytics Technology Trends for 2019

Top 10 Data and Analytics Technology Trends for 2019

Foto: Shutterstock

Augmented analytics, continuous intelligence and explainable AI are among the top trends in data and analytics technology that have significant disruptive potential over the next three to five years, according to Gartner. The resarch company recommends that data and analytics leaders talk with senior business leaders about their critical business priorities and explore how the following top trends can enable them.

Trend No. 1: Augmented Analytics

Augmented analytics is the next wave of disruption in the data and analytics market. It uses machine learning and AI techniques to transform how analytics content is developed, consumed and shared. By 2020, augmented analytics will be a dominant driver of new purchases of analytics and BI, as well as data science and ML platforms, and of embedded analytics. Data and analytics leaders should plan to adopt augmented analytics as platform capabilities mature.

Trend No. 2: Augmented Data Management

Augmented data management leverages ML capabilities and AI engines to make enterprise information management categories including data quality, metadata management, master data management, data integration as well as database management systems (DBMSs) self-configuring and self-tuning. It is automating many of the manual tasks and allows less technically skilled users to be more autonomous using data. It also allows highly skilled technical resources to focus on higher value tasks.

Trend No. 3: Continuous Intelligence

By 2022, more than half of major new business systems will incorporate continuous intelligence that uses real-time context data to improve decisions. Continuous intelligence is a design pattern in which real-time analytics are integrated within a business operation, processing current and historical data to prescribe actions in response to events. It provides decision automation or decision support. Continuous intelligence leverages multiple technologies such as augmented analytics, event stream processing, optimization, business rule management and ML.

Trend No. 4: Explainable AI

AI models are increasingly deployed to augment and replace human decision making. However, in some scenarios, businesses must justify how these models arrive at their decisions. To build trust with users and stakeholders, application leaders must make these models more interpretable and explainable.

Trend No. 5: Graph

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. The application of graph processing and graph DBMSs will grow at 100 percent annually through 2022 to continuously accelerate data preparation and enable more complex and adaptive data science.

Graph data stores can efficiently model, explore and query data with complex interrelationships across data silos, but the need for specialized skills has limited their adoption to date, according to Gartner. Graph analytics will grow in the next few years due to the need to ask complex questions across complex data, which is not always practical or even possible at scale using SQL queries.

Trend No. 6: Data Fabric

Data fabric enables frictionless access and sharing of data in a distributed data environment. It enables a single and consistent data management framework, which allows seamless data access and processing by design across otherwise siloed storage. Through 2022, bespoke data fabric designs will be deployed primarily as a static infrastructure, forcing organizations into a new wave of cost to completely re-design for more dynamic data mesh approaches.

Trend No. 7: NLP/ Conversational Analytics

By 2020, 50 percent of analytical queries will be generated via search, natural language processing (NLP) or voice, or will be automatically generated. The need to analyze complex combinations of data and to make analytics accessible to everyone in the organization will drive broader adoption, allowing analytics tools to be as easy as a search interface or a conversation with a virtual assistant.

Trend No. 8: Commercial AI and ML

Gartner predicts that by 2022, 75 percent of new end-user solutions leveraging AI and ML techniques will be built with commercial solutions rather than open source platforms. Commercial vendors have now built connectors into the Open Source ecosystem and they provide the enterprise features necessary to scale and democratize AI and ML, such as project & model management, reuse, transparency, data lineage, and platform cohesiveness and integration that Open Source technologies lack.

Trend No. 9: Blockchain

The core value proposition of blockchain, and distributed ledger technologies, is providing decentralized trust across a network of untrusted participants. The potential ramifications for analytics use cases are significant, especially those leveraging participant relationships and interactions. However, it will be several years before four or five major blockchain technologies become dominant. Until that happens, technology end users will be forced to integrate with the blockchain technologies and standards dictated by their dominant customers or networks.

Trend No. 10: Persistent Memory Servers

New persistent-memory technologies will help reduce costs and complexity of adopting in-memory computing (IMC)-enabled architectures. Persistent memory represents a new memory tier between DRAM and NAND flash memory that can provide cost-effective mass memory for high-performance workloads. It has the potential to improve application performance, availability, boot times, clustering methods and security practices, while keeping costs under control. It will also help organizations reduce the complexity of their application and data architectures by decreasing the need for data duplication.

More from category

Pureplay Digital Companies Will Process $14 Trillion in B2B Payments by 2023

Pureplay Digital Companies Will Process $14 Trillion in B2B Payments by 2023

24 May 2019 comment

New data from Juniper Research found that B2B transactions processed by pureplay digital operators will reach $14 trillion by 2023, up from $6.7 trillion in 2018.

Commercial Performance Brings EMEA PC Market Close to Stability in 1Q19

Commercial Performance Brings EMEA PC Market Close to Stability in 1Q19

24 May 2019 comment

The EMEA traditional PC market declined YoY in 1Q19 for the second consecutive quarter, but at a softer rate (-2.7%) and totaling 17.0 million units, according to IDC.

Businesses Will Spend $1.2 Trillion on Digital Transformation This Year

Businesses Will Spend $1.2 Trillion on Digital Transformation This Year

23 May 2019 comment

Enterprises around the world are making significant investments in the technologies and services that enable the digital transformation of their business models, products and services, and organizations, according to IDC.