It is estimated that in 2020, augmented analytics will be the main driver of new purchases for business intelligence and analytics.
Augmented analytics refers to the use of technologies such as, AI and machine learning to help with data insights and preparation, augmenting how people use data in various BI and analytics platforms. It also automates many other aspects of machine learning and BI.
But augmented analytics is just one of the many emerging data technologies that have been identified to handle major data analytics challenges in the near future.
1. Augmented Analytics
Augmented analytics effectively automates the discovery of important insights to optimise the decision-making process. It’s very effective and does this in a fraction of the time compared to a manual process. It also reduces reliance on machine learning, data science, and analytics experts.
2. Natural Language Processing (NLP)
NLP allows an easier way to ask questions about big data and receive valuable insights. But this is taken even further with the use of conversational analytics where questions are verbally answered rather than with text. This is similar in a way to what Google has done for the Internet.
3. Augmented Data Management
With data growing at a very fast pace and technical skills being in short supply, there is a need for organisations to automate data management. Artificial intelligence and machine learning are added to make data processing easier and more time-efficient as teams can focus on other valuable tasks.
4. Commercial Machine Learning and AI
Machine learning and AI are currently dominated by open source platforms, which also leads the way in terms of development and innovating algorithms. Commercial businesses were somewhat slower to respond to this market but now taps into the open source ecosystem. These platforms also provide enterprise features necessary to scale machine learning and AI, including transparency, integration, reuse and model management.
5. Blockchain in Data Analytics
Blockchain is a technology that addresses various challenges in data analytics. First, it provides transparency for a complex network, and secondly, it provides the lineage of transactions and assets. However, it’s important to note that blockchain has limited data management capabilities, so it is not a stand-alone datastore. the technology hasn’t matured as yet, to be on the level that real-world production environments need.
According to Gartner, organisations need to identify technology trends and prioritise them. Instead of reacting to new technologies as they emerge, it’s important for organisations to educate themselves and engage with others about their priorities and where they can use data analytics to their advantage.