Is your organization ready for what’s next?

Gartner’s Top 10 Data and Analytics Technology Trends set the stage for innovation over the next five years. Organizations must prepare for the substantial impact of these disruptive technologies across industries and adapt or risk losing competitive advantage.

According to Donald Feinberg, vice president and analyst at Gartner, “the very challenge created by digital disruption — too much data — has also created an unprecedented opportunity. The vast amount of data, together with increasingly powerful processing capabilities enabled by the cloud, means it is now possible to train and execute algorithms at the large scale necessary to finally realize the full potential of AI.”

Gartner selected ten technologies that data and analytics leaders should begin to incorporate into their roadmaps and strategies moving forward.  Three technologies – augmented analytics, continuous intelligence, and artificial intelligence – form the basis for the Top 10 Data and Analytics Technology Trends for 2019.

1. Augmented Analytics

Augmented analytics leverages automated machine learning (ML) and artificial intelligence (AI) techniques to change the way the content is created, used, and shared. By 2020, augmented analytics will be by far a dominant driver of new purchases of analytics and BI, embedded analytics, and data science and machine learning platforms.

Augmented analytics is one of the significant disruption waves in the data and analytics market, therefore plan to adopt augmented analytics as part of your data and analytics strategy.  This technology will enable the delivery of more advanced insights across your organization.  Also, explore opportunities to take advantage of augmented analytics to complement existing analytics and BI, data science initiatives, and embedded analytic applications.

2. Augmented Data Management

Augmented data management makes enterprise information management self-configuring and self-tuning. The addition of automated service-level management and machine learning will reduce by 45% the data management manual tasks through to the end of 2022.

Consider leveraging augmented data management in your organization to automate many of the manual tasks that allow less technical users to be more autonomous using data, and skilled technical resources to focus on higher value tasks.

3. Continuous Intelligence

Gartner defines continuous intelligence as a design pattern in which real-time analytics integrates within business operations. Continuous intelligence prescribes actions in response to events by processing historical and current data.

Over 50% of significant new business systems will incorporate continuous intelligence with real-time context data to improve decision-making by 2022.

Consider incorporating continuous intelligence in your analytics and BI initiatives to take advantage of multiple technologies – augmented analytics, event stream processing, optimization, business rule management, and ML – to make smarter real-time decisions.

4. Explainable AI

Although AI models are increasingly deployed to replace and augment decision making, most of these need more interpretation and explanation for building trust with stakeholders and users.

In ML and data science platforms, explainable AI works better because it generates an explanation of models automatically with improved accuracy, attributes, model statistics and features in natural language.

5. Graph Analytics

Graph analytics allows modeling, exploration, and querying data with complex interrelationship across data silos or entities of interest (organizations, people and transactions).

Gartner predicts the application of graph processing and graph database management systems will grow at 100 % annually through 2022.

Although graph analytics promises to accelerate data preparation and enable more complex and adaptive data science, these technologies face some challenges that will limit their adoption. Organizations will need specialized skills to take advantage of the opportunities that come with graph analytics.  Moreover, asking questions across complex data is not always possible or practical at scale using complex SQL queries.

Despite the challenges, the significant potential for disruption means you should probably begin evaluating graph analytics, even if you don’t aggressively adopt this technology in the next few years.

6. Data Fabric

Data fabric is an emergent technology that enables frictionless access and sharing of data in a distributed data environment. The primary advantage is represented by allowing seamless data access and processing by design across otherwise siloed storage.

Data fabric will be deployed primarily as a static infrastructure through 2022.  However, organizations choosing to completely re-design for more dynamic data mesh approaches should know this change comes at a cost.

7. Natural Language Processing / Conversational Analytics

By 2020, half of the analytical queries will be generated via search, natural language processing (NLP) or voice, or will be automatically generated.

Much of the focus on NLP and Conversational Analytics comes from the need to analyze complex combinations of data and make analytics accessible to everyone in the organization. These technologies will allow analytics tools to be as easy as a search interface or a conversation with a virtual assistant which are the strong points of adoption.

8. Commercial AI and ML

To be able to provide their customers the enterprise features necessary to scale and democratize AI and ML – project and model management, reuse, transparency, data lineage, and platform cohesiveness and integration – commercial vendors have now built connectors into the open source ecosystem.

Gartner predicts that by 2022, two-thirds of new end-user solutions leveraging AI and ML will include commercial rather than open source platforms.

9. Blockchain

Blockchain and distributed ledger technologies are data sources that provide transparency, reduce friction across business ecosystems, and could lower costs.  Blockchain provides trust in untrusted environments eliminating the need for a trusted central authority.

Although the ramifications for analytics use cases are significant, it will take a few years before blockchain technologies become dominant. Until then, technology users will have to integrate their existing data and analytics infrastructure with the blockchain standards and technologies imposed by their dominant networks or customers. Unfortunately, this might come at a high cost that outweighs the benefits.

10. Persistent Memory Servers

According to Gartner, persistent memory represents a new memory tier between NAND and DRAM and flash memory – in other words, a cost-effective mass memory for high-performance workloads. By decreasing the need for data duplication, new persistent-memory technologies can help organizations reduce the complexity of their applications and data architectures.

Other key benefits include improved application performance, availability, boot times, clustering methods and security practices while keeping costs under control.

As we look toward the future, the proven benefits of these emerging technologies and trends are just too compelling for most organizations to ignore. Moreover no matter your business, your organization needs to understand the market ahead and embrace the new technologies to prepare for the next phase of innovation and growth.

More detailed information on the Top 10 Data and Analytics Technology Trends for 2019 are on Gartner’s website.

To learn more about how to use data and analytics for competitive advantage, please visit DataClarity Analytics and Data Science platform microsite.