Respected analyst firm Constellation Research's founder, chairman and Principal Analyst Ray Wang and VP and Principal Analyst, and former columnist for InformationWeek, Doug Henschen collaborated on an objective profile of Reltio. The report states "Reltio is unique in blending master data management and machine learning capabilities into a PaaS that is purpose built for data-driven applications and services."
Ovum has always believed that MDM is most effectively implemented, not as an integration tool, but as an application that ingrains domain knowledge about how specific industry sectors organize data. Reltio is applying that approach to big data, taking it a step further by applying machine learning to guide users and help them make decisions based on their discoveries about how people, products, and/or other entities interrelate.
Machine Learning needs reliable and relevant data and better context surrounding transactions, interactions, operations, and decisions. In this video interview Michele Goetz, Principal Analyst at Forrester Research discusses how Machine Learning will prepare and master your data at scale so you can go from data-driven to insight driven.
We sat down with Pallab Deb, VP & Head – Analytics at Wipro to go over how data and analytics are the foundation for digital transformation that any enterprise wants to embark on.
“GDPR is simply echoing the skepticism that exists around the use of the AI and ML blackbox, which places complete trust and autonomous decision making on data management and insights. Even experienced IT professionals whose careers are focused on automation and reducing manual effort have conveyed that they feel more comfortable being able to see suggestions and a pattern of consistent success before accepting AI/ML-generated actions unilaterally with no intervention,” said Ramon Chen, CPO at Reltio.
The key is to get data “organized in a manner that ensures it can be reconciled, refined and related, to uncover relevant insights that support efficient business execution across all departments, while addressing the burden of regulatory compliance,” said Ajay Khanna, VP of Reltio.
AI and ML can be used in multiple ways to harness the power of data, and each brings its own qualities and applications. It can be overwhelming to choose, with huge possibilities of implementation approaches. Read the article and learn the six effective ways to attain quantifiable benefits from AI and ML.
AI is seen as a positive addition to the workplace. It also likely to stay as a positive influence for the medium and even long term. Read the six reasons why.
This video presentation by Upwan Chachra, Principal Product Manager at Reltio, discusses how mastered data brought together with big data scale interactions and transactions, into a single environment, such as Apache Spark on demand, quickly enables agile, closed-loop insights and action to provide faster Time to Analytics (TTA), and more relevant and accurate information through best of breed machine learning tools and technologies.
This video presentation by Anastasia Zamyshiyaeva, Chief Architect at Reltio, discusses how modern data architecture can help your enterprise have flexible data model, mitigate risks, and deliver maximum business value from your data assets.
Want to learn how data organization and machine learning deliver improved business outcomes? Watch this explainer video.
An industry panel moderated by Ramon Chen, CPO, Reltio and featuring Michele Goetz, Forrester, Pallab Deb, Head of Analytics, Wipro, and Manish Sood, CEO Reltio, discusses the current state of AI and Machine Learning. It covers how MDM and data organization is foundational. What ML means for industry regulation such as GDPR and more.
Reltio is highlighted in Forrester's Now Tech report on Machine Learning Data Catalogs. Enterprise architecture (EA) professionals can use this report to understand the value they can expect from MLDC and various provider so they can select vendors based on functionality and role affinity.
In our digital economy, the most successful companies know how to harness their data and learn from it. Be it Google, Airbnb or Amazon, the learners in the digital economy are leading the industry with new revenue models, innovative business processes and more effective customer engagement. Get an inside look into how to evolve into a Self-Learning Enterprise to improve your business.
"With bad data, machines learn nothing," said chief product officer Ramon Chen, in an interview, discussing the impact of poor quality data on machine learning systems.
Self-Learning Enterprises recognize that data is at the heart of every decision, and more data requires better organization. They organize data collected from various sources into a reliable data foundation. Use of Self-Learning Data Platform with built-in advanced analytics with Machine Learning and Artificial Intelligence promises discovery of new relationships, better insights, and intelligent recommendations to make better business decisions, with a firm focus on improving data and decision quality.
Attend DD18 and come see the sessions by DataRobot and Reltio which will feature Reltio Cloud, using Reltio IQ, feeding the DataRobot automated machine learning platform, with organized, reliable data in the form of consumer retail profile and transaction data sets, receiving back upsell IQ scores and recommendations, which are integrated back in context into individual consumer profiles.
Insights-driven businesses sync data, analytics, and optimization capabilities across their enterprise. They do this at unprecedented scale, unleashing a level of decision making and innovation that provides them with sustained differentiation in their markets. This report guides CIOs through Forrester's insights-driven business assessment to gauge how well their organizations perform the essential activities they need to be insights driven and helps them discover which activities they must strengthen in order to progress.
Reltio Cloud Self-Learning Data Platform integrates enterprise data organization, advanced analytics, machine learning, and intelligent data-driven business applications at petabyte-scale
- 2018 will be the year of AI and Machine Learning
- Enterprise data organization, not management, will be the new rallying cry
- Data-driven organizations will expect to measure outcomes
- Multi-cloud will be the new normal
- Companies will execute offensive data-driven strategies, and should expect to get defence for free
Tarun Batra, CEO, LumenData, talks about how the movement towards artificial intelligence and machine learning relies on a Modern Data Management platform that is able to correlate large amounts of data, and provide a reliable data foundation for machine learning algorithms to deliver better business outcomes.
John Wollman, Executive Vice President & Chief Innovation Officer, HighPoint Solutions discusses how Reltio provides best-of-breed master data management (MDM), coupled with the ability to perform rich analytics upon large volumes of transactional claims data through Reltio Insights.
Are you interested in bringing machine learning and predictive analytics to your enterprise? Many prospective users don't know where to start. Ramon Chen, chief product officer for Reltio, recently spoke with us about this challenge.
Maxim Lukichev, Lead Data Scientist, Reltio, talks about Reltio Insights and how it seamlessly leverages Reltio Cloud Modern Data Management Platform as a Service to power reliable advanced analytics and machine learning, to feed data-driven applications.
Some thoughts on recent trend where martech companies have shown a considerable amount of interest in acquiring smaller independent AI firms
Businesses large and small are being lured in by the potential of artificial intelligence (AI), machine learning (ML), deep learning and cognitive computing, while others are still trying to figure out how to tell them apart.
Chief Data Officer, Chief Experience Officer and now there's a new kid in the C-suite: The Chief Artificial Intelligence Officer.
Chitra Varma, Commercial Leader, Information Management Solutions, QuintilesIMS, talks about the value of on-demand, real-time Data as a Service (DaaS), and how modern data management is helping life sciences companies to have an end-to-end solution for driving their critical business operations and decisions.
Reltio Lists Our Top 5 Predictions for Analytics, Data Management and AI for 2017
Maintaining data reliability is a resource-intensive uphill task for many organizations. Companies often spend too much effort on data reviews and cleanup, but seldom seem to catch up. Most of the time, teams don’t even know what the issues are, how to look for them, and how to solve them. They just know that the data is dirty, and like a sitting on a ticking time bomb, we wait for the disaster to happen.
The sheer volume and variety of data requires the assistance of highly skilled, very scarce “data scientists”, it makes things even worse when the data is not reliable and you are essentially gambling with your insights.
Big data should be leveraged not only to respond to inquiries but also to proactively deliver information. The system should be able to make recommendations based on all the information that is available with a continuous feedback loop to improve them with each interaction.
Data is a company’s most important asset, and it’s constantly growing. Taking mandated compliance and turning it into an opportunity to personalize, delight and exceed customer expectations would fuel innovation reliably and responsibly.
In 2017, we saw several of our top predictions come true. AI and analytics M&A vendor activity accelerated, while cloud and data security has gained further importance with the General Data Protection Regulation (GDPR) now in motion. How will data management evolve in 2018?
Consumer brands are facing unsteady growth, tightening profit margins, and complex regulations. Adopting these 7 data-driven habits in 2018 would help them reverse the digital curse and stay ahead of competition.
Experts in Data Management and Machine Learning from Thomson Resuters, Amazon Web Services, and Reltio discuss the future of AI at the Nasdaq Entrepreneurial Center.
Can you prove the ROI of your data management efforts? Are you able to conquer the gap between your analytical insights and operational execution? If the answer is no to one or more of these questions, you must rethink (and reinvent) your data management philosophy.
It's easy to be seduced by the "black magic" of technology that can solve a variety of your business challenges by just asking Watson, Einstein, Siri, Alexa or even HAL, and other "humanizing" names. Beyond the hype, here are 3 critical ingredients that must be considered in order to be successful.
This year’s Big Data Innovation Summit 2017 in San Francisco tackled weighty themes to help attendees avoid costly mistakes from inaccurate data and attain best practices for harvesting data with high potential to name a few.
Hadoop, HBase, Cassandra, Graph Databases, MapReduce, Spark and the continuous stream of new technologies have changed the way data can and should be managed. However stitching together all of the pieces required to have a complete end-to-end offering and support a wide variety of business needs across an enterprise is a complex undertaking. A Modern Data Management Platform helps you keep up with this evolving technology landscape.
One of the most important issues for customers will be the trust in the data and in the results from a cognitive system. A cognitive system is only as good as the data that is ingested. Data needs to be analyzed so that the meta data is understood. This data needs to be refined so that its meaning is clear and the data itself is truth worthy. After all, the value of a cognitive system is that it creates an environment where industry experts can trust a cognitive computing system.