Organizations worldwide are starting to infuse operations with data science's graph analysis approach. Use of graph databases can significantly reduce any support role played by data scientists. Information architects and analytics leaders should understand the costs and opportunities both present.
Impacts and Recommendations
- Standardizing on a graph analysis language will accelerate the sharing of data science discoveries and processing into operational applications, and cascading into reporting
- Graph databases will serve as the technology bridge between data science work and adding graph analysis to commonly used business intelligence solutions, allowing it to be accessed by less-skilled personnel
- Having a data science team or lab will drive new procedures in the enterprise architecture team, relative to converting data science into production
Gartner Recommended Reading
©2020 Gartner, Inc. and/or its affiliates.
All rights reserved.
Gartner is a registered trademark of Gartner, Inc. and its affiliates.
This publication may not be reproduced or distributed in any form without Gartner’s prior written permission.
It consists of the opinions of Gartner’s research organization, which should not be construed as statements of fact.
While the information contained in this publication has been obtained from sources believed to be reliable, Gartner disclaims all warranties as to the accuracy, completeness or adequacy of such information.
Although Gartner research may address legal and financial issues, Gartner does not provide legal or investment advice and its research should not be construed or used as such.
Your access and use of this publication are governed by Gartner’s Usage Policy.
Gartner prides itself on its reputation for independence and objectivity.
Its research is produced independently by its research organization without input or influence from any third party.
For further information, see
Guiding Principles on Independence and Objectivity.