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Master Data Management: Perspective 2018

From the Desk of Manish Sood, CEO, Reltio Inc.

The MDM market and landscape has gone through multiple changes over the last 10+ years.

As we near the end of 2018, I’d like to thank our customers and partners that have been on the forefront of this revolution:

  1. Reltio’s rapid growth, as evidenced by our transparent publication of our audited revenues via the Inc 5000 & Deloitte awards marks us as one of the fastest growing private companies in the world, and the only MDM vendor on either list.
  2. Our unique Google-like search and LinkedIn-like filtering capabilities for business users further differentiates us from other MDM vendors.
  3. As the only native Cloud MDM vendor our customers and partners reap the benefits of innovation and rapid availability of new functionality for years to come.

Reltio is a 100% customer and partner first company, and we pride ourselves on our transparency and focus to constantly do better. Due to our rapid growth we have received feedback that we need to continue to improve our product capabilities, as well as our interactions with the goal of eliminating friction between us and our customers.

Throughout 2018, we have responded to feedback to improve customer satisfaction. Notably we launched a three prong effort:

    1. Reprioritized engineering resources
      We tasked 95 developers and quality assurance employees to burn down customer problems faster than before, and the team has recommitted to product quality with more robust quality processes.
    2. Increased technical customer support and visibility
      We increased our investment in Customer Support so that we can respond more quickly to issues immediately after they arise. Shortly we will implement a new solution that promises to improve customer support ticket visibility.
    3. Improved direct customer communications
      We continue to conduct quarterly business reviews with customers, and have expanded the scope of these reviews to include additional Reltio executives to surface areas where we can be a better partner.

What are the results of these new initiatives?

I am happy to report early progress in customer satisfaction. In November 2018, we surveyed our customers and 95% stated that they would recommend Reltio to other companies while 100% surveyed stated they intend to renew their Reltio subscriptions. We are not finished, but our re-commitment to customer delight is already yielding dividends.

Summary

I’m happy to see Reltio’s stellar growth and the recognition we’ve received over the course of 2018. A quote from a October 2018 Bloor report appropriately encapsulates our efforts to align with our customer’s vision and goals

“Three years ago we wrote that Reltio was several steps ahead of the market. Based on our latest assessment of their execution metrics related to product, technical and business innovation, and the growing adoption by some of the largest enterprises in the world, the gap may have even widened.”

Bloor Report

Once again, to our customers and partners, thank you for a wonderful 2018. We look forward to working even more closely with you in 2019. For those who are new to Reltio, please give us a call. We will assist you anyway we can to help clarify options and best practices about MDM, your Customer 360 project, or your enterprise digital transformation initiative.

Warm Regards,

Manish Sood, CEO, Reltio Inc.

Why Master Data Management and Machine Learning Go Hand in Hand

Ramon Chen, Chief Product Officer, Reltio

Reltio’s inclusion in the The Forrester Wave™: Machine Learning Data Catalogs Q2 2018, by Michele Goetz with Gene Leganza, Elizabeth Hoberman, and Kara Hartig, Forrester Research, June 2018, sparked (pun intended) several questions. Such as why was Reltio included, how did we receive such strong marks, and why were we the only Master Data Management (MDM) vendor in the Wave?

The simple answer is that the Wave’s qualification criteria includes several key areas in which Reltio is naturally strong. As the only Master Data Management platform recognized in the Wave we believe our core MDM capabilities contributed to our strong showing. In fact, Reltio had already been included, together with 23 other excellent companies, in Forrester’s Now Tech: Machine Learning Data Catalogs, Q1 2018 report preceding the Wave.

That report outlined key 3 characteristics of Machine Learning Data Catalogs (MLDC):

1. Interpret, define, classify, link, and optimize the use of disparate data sources

Reltio is used by companies globally to define logical business schemas, capture and discover relationships through the Reltio Self-Learning Graph, suggest ongoing improvements, and to organize and bring siloed data together across the enterprise to meet their business objectives. This continuous reliable data foundation feeds better operational execution, predictive analytics, and sets them up to evolve towards a self-learning enterprise.

2. Reconcile policies across data use

Reltio Cloud’s built-in data security and privacy, regulatory, life-cycle, and data quality policies coexist to adapt data for multiple uses through a powerful audit trail, and role based access to data. This is critical in the face of evolving compliance measures such as GDPR. Flexibility and agility to ensure that you can track not just where the data originated, but how it’s being used and the outcomes it generates, is a critical component of any forward looking ML strategy.

3. Democratize data to the edge of business

Reltio is particularly well suited to meet this requirement through frontline business user facing data-driven applications and workflow and collaboration capabilities that come OOTB with Reltio Cloud. It allows teams to submit comments, suggestions, filter and easily segment information through a UI that’s as easy to use as Facebook and LinkedIn.

Data science teams are then able to use Reltio IQ, with Apache Spark to run their algorithms without the pain associated with cleaning and onboarding data in separate environments. This is increasingly important as enterprises deploy machine learning systems, with data scientists requiring relevant, curated data sources to train algorithms to improve results.

As this video illustrates, the true value comes from being able to synchronize ML-algorithm derived IQ scores back into master profiles as aggregate attributes. Making them available for segmentation by marketing, sales, and even data stewards and compliance teams. Teams can then continuously reconcile results to recommendations in a closed loop to self-learn and improve outcomes.

We are tremendously proud and honored to have been included in the MLDC Wave as it reflects our core belief that machine learning cannot be used in isolation from the overall data organization and management needs of the business.

Whatever your desired outcome, MDM forms the backbone of high quality, reliable data which allows ML to thrive.

ML in turn provides unique capabilities to improve and increase the efficiency of data quality, and enterprise data organization operations. Like the graphic I selected for this post, they go hand in hand, and are interconnected across all points of the data continuum and life cycle.

How You Can Prepare for the Upcoming AI and Machine Learning Revolution

Ramon Chen, Chief Product Officer, Reltio

I was honored to be invited to participate on a panel discussing the evolution of Machine Learning (ML) at the Thomson Reuters Emerging Tech Conference. The panel consisted of luminaries such as moderator Jonathan Weber, Global Tech Editor, Reuters News, Asif Alam Global Business Director, Thomson Reuters, and Vikram Madan Senior Product Manager, AWS, Machine Learning.

We covered a lot of topics including deep learning, neural networks, image recognition, reliable data foundation as an ML imperative, digital personal identities, the increasing value of enterprise data, how you should safeguard your private data, GDPR, closed-loop as the last mile in ML, how LinkedIn is an example of the next generation data-driven application, autonomous data management and machine learning for data matching and correlation, classification of different types of data, Gluon and the Microsoft – AWS partnership, how elastic cloud computing with unlimited processing power makes ML a reality, and more.

Here are some key takeaways from the panel:

  • Machine Learning requires a foundation of continuous reliable data to ensure that algorithms are acting on the right information. Generating reliable data is usually the task of master data management (MDM) tools, that blend and correlate profile attributes across disparate siloed sources and applications. However MDM itself cannot deliver the complete picture as it’s missing the critical set of interactions and transactions that complete the 360-degree view. Today’s modern data management platforms go beyond MDM, and beyond data lakes that have been largely ineffective by providing a seamless feed of reliable data to maximize the potential of machine learning.

  • One challenge for, not just machine learning, but advanced analytics in general has been the friction of synchronizing data models between operational applications and data sources, and downstream data warehouses and lakes that are being used as the data pool for analysis and ML. Today it is possible to eliminate that friction by seamlessly transitioning information into Spark on demand, so that machine learning can operate on the latest, most up-to-date data, without the need to wait for data model updates and changes which have traditionally hindered business agility.

  • Another critical element of making sense of the output from machine learning and advanced analytics is closing the loop and bridging the gap between insight, action and outcomes. Today’s insights are still siloed from the actual actions that business teams will eventually take based upon the data. Further the outcomes of any actions take are rarely correlated back to the originating insights. The added value of a continuous feed of reliable data, relevant insights and recommended actions generated from ML is to have a closed loop where users can contribute to data reliability, and provide data on the outcomes, implicitly through their actions, or explicitly through feedback responses, so that ROI can be tracked, and ML has the historical data to learn and improve

It was a fun night with the audience contributing to the discussion. The future for AI and ML is a bright one. Everyone agreed that in order for such initiatives to deliver true value, a reliable foundation of data must be established, in order to ensure success.