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TigerGraph 2019 Predictions: Big Data Matures with Analytics, BI Visualizations and Explainable AI

Industry executives and experts share their predictions for 2019.  Read them in this 11th annual VMblog.com series exclusive.

Contributed by Dr. Yu Xu, CEO, TigerGraph

Big Data Matures with Analytics, BI Visualizations and Explainable AI

The past year has been a major one for graph databases. We have seen enterprises continue to adopt real-time big graphs, as well as leverage big data analytics in the cloud, as I previously predicted.

More and more businesses recognize the fact that they require modern data management solutions that are able to find patterns, linked data, in their data. Traditional solutions, such as BI tools looking into data lakes is just the start of the journey to pattern matching and AI.

So what's in store for 2019? Here are my top three predictions below. Once again, I believe graph analytics will have a key part in shaping what's to come. 

Focus on Big Data and Analytics

The coming year will bring about a new focus on big data and analytics. This will be marked by organizations shifting away from building massive data lakes to extracting as much value possible from them. Both enterprises and governments will closely consider how to best leverage their multi-million dollar investments in Hadoop data lakes to maximize true value.

Tapping into connections within the data - such as finding patterns or relationships between customers, suppliers, products and locations - organizations can create new applications built on the insights. This can include new features for machine learning and AI. Key to achieving this is using technology like graph analytics to promote better business outcomes. More organizations will derive value from their data to help meet the bottom line - whether it's by increasing revenue, improving risks and operational efficiency, informing areas such as marketing and upsell opportunities, and much more.

BI Visualizations Tools Come Into the Forefront

In 2019, organizations will truly begin to unleash the power of their connected data to obtain deeper business insight. The bar will become lower for professionals to conduct business analytics over their big data, made possible by growing adoption and advancement of graph BI visualization tools like Tableau.

These new graph BI visualization tools allow users to ask complex questions visually via clicks, drag and drops in a browser. The result is being able to more easily gain insight into areas such as how a group of entities are connected, how many users communities exist, who influential users are, etc. All these questions center on discovery, exploration and clustering, and cannot be expressed using BI tools on tabular data, but can be easily asked visually via a graph BI tool.

The End of Black Box AI

We will also see the birth of Explainable AI - AI whose actions can be easily understood by humans. More and more enterprise and government organizations are demanding visibility into how AI applications arrive at their answers. Explainable AI is the key to achieving this, by logically explaining how bias and decisions are achieved.

Explainable AI requires features with well-defined business logic that influence the outcomes - this is where graph-based analytics will become a first class citizen of the AI and analytics mashup in 2019. The ability to understand hubs of influence - whether it's from customers, professionals, bloggers and more - and the community around those hubs is becoming a key differentiator and driver for most businesses. It will surely continue as explainable AI progresses.

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About the Author

 

Dr. Yu Xu is the founder and CEO of TigerGraph, the world's first native parallel graph database. Dr. Xu received his Ph.D in Computer Science and Engineering from the University of California San Diego. He is an expert in big data and parallel database systems and has 26 patents in parallel data management and optimization. Prior to founding TigerGraph, Dr. Xu worked on Twitter's data infrastructure for massive data analytics. Before that, he worked as Teradata's Hadoop architect where he led the company's big data initiatives.

Published Tuesday, January 29, 2019 7:26 AM by David Marshall
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