Gartner recently performed a deep dive into the top trends that will define the world of data through 2020 and beyond. Anyone working in big data – and all Pepperdata users – should be very interested in what they have to say. What does this coming year hold for the world of big data analytics? Here’s what Gartner had to say.
By 2020, augmented analytics will be a dominant driver in the big data industry. Augmented analytics automates the discovery of insights, and makes it available to businesses for optimal decision making. While augmented analytics is fast and powerful, it requires increased data literacy across the organization to be successful.
Augmented Data Management
This trend impacts all of enterprise data management: databases, data quality, metadata management, data integration, and metadata management. With the exponential growth of data today, vendors will need to leverage the capabilities of AI and machine learning to automate data management processes.
Blockchain in Data and Analytics
Blockchain technologies are still only used in cryptocurrency systems, and they have yet to mature to production level scalability. But this technology could potentially address two data and analytics challenges: providing lineage of assets and transactions, and providing transparency for complex networks of participants.
Commercial AI and Machine Learning
The increased use of commercial artificial intelligence (AI) and machine learning (ML) will accelerate production deployments and drive more business value. Currently, open-source platforms have become the primary source of innovation in algorithms and dev environments for AI and ML. But commercial vendors, though slow to respond to the trend at the start, are now offering enterprise features necessary to scale both. In 2020, automation frameworks will allow data scientists to create their own data pipelines that are close to production-ready.
Gartner predicts that more than half of major business systems will incorporate continuous intelligence by 2022. This has long been sought by organizations, and now it is finally practical to implement such systems because of advancements in the cloud, streaming software, and the Internet of Things (IoT).
A data fabric is a custom-made design orchestrating a combination of data integration approaches to provide reusable data services, pipelines, semantic tiers, or APIs. Deriving value from analytics investments is fairly easy when you have such frictionless data sharing and access.
Gartner predicts that the application of graph processing and databases will double annually over the next few years, accelerating data preparation and enabling more complex and adaptive data science.
Natural language processing (NLP) and Conversational Analytics
By 2021, this trend will boost analytics and business intelligence adoption from 35% to 50% for employees. Business people now have an easier way to ask questions and get data and insights with NLP, while conversational analytics enables the verbal communication of such questions.
Persistent Memory Servers
This trend willhelp businesses extract more practical and actionable insights from their data. Currently, most database management systems (DBMS) use in-memory database structures. However, new server workloads demand faster processing, faster storage, and massive memory. Although it may take years to modify current software to take advantage of this trend, DBMS vendors are now experimenting with persistent memory.