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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.

How CDOs & CIOs are Driving Digital Transformation

Ajay Khanna, VP of Marketing, Reltio

I recently got an opportunity to present at MITCDOIQ Symposium in Cambridge, MA. Here is the outline of my presentation where I discussed how today’s CIOs and CDOs are driving digital transformation across their enterprises. It discusses the key drivers for digital transformation and how Modern Data Management is helping them with their initiatives. You can now download my slides from the event from this page.

Today’s business landscape more dynamic than ever. New revenue models, stringent regulations and high customer expectations are forcing organizations to evolve or face being overrun by more nimble competitors.

CDOs and CIOs of established business are looking to digital transformation as a key initiative. But what exactly does digital transformation entail? At its core, any digital transformation requires clean and consistent data, reconciled across systems and channels. An enterprise-wide data management foundation that ensures real-time access to reliable data of all types at scale and is non-negotiable. Data access must be democratized across all groups and divisions so that teams can get a 360-degree view of customers, products, organizations and more.

However, it’s not just about disconnected siloed analytics. It’s about the next generation of operational data-driven applications that allow frontline business users to gain relevant insight and intelligent recommended actions so they can achieve their goals. This session explores how some of the largest companies in the world are transforming themselves using the same modern data management technology used by Internet giants such as Amazon, Facebook, LinkedIn and Google.

The presentation covers the following topics:

  • Changes in business environment and need for agility

  • Digital transformation drivers

  • Digital transformation examples

  • Data-driven digital transformation with Modern Data Management

Please fill out the form below to download the presentation slides:

Turning Customer Data into Actionable Insights

Ajay Khanna, VP of Marketing, Reltio

This week I got an opportunity to present at DBTA’s 2017 Data Summit conference. The topic of my discussion was “Turning the Customer Data into Actionable Insights.” All enterprises want to understand their customers better so they can engage the right customer, at the right time, with the right offer, via the customer’s preferred channel. The objective seems simple, but is quite hard to deliver if you do not have access to reliable data. Large volumes of data are being collected, but the data is scattered across multiple systems. There is no single source of truth across functional groups like sales, marketing and support. Different channels have their own version of the truth. Therefore, the customer experience remains disconnected, and customer insights are quite shallow.

The presentation covered how we can get to personalization at scale using Modern Data Management. The following aspects were covered:

Establishing a Reliable Data Foundation

To Make this experience more connected, we must bring the customer data together and then use that data for meaningful consumer insights and intelligent recommendations.
Start with connecting to all required data sources – internal systems (CRM/Marketing Automation etc.), external systems, social streams if needed as well, and enrich it with third-party data subscriptions as needed. Match, merge and clean the data to create a single, reliable source of truth of your customer profiles. Modern Data Management lets you identify potential matches and overlaps of the profiles. It helps to compare and contrast similar profiles and then automatically consolidate to create operational values using survivorship rules.

Please fill out the form below to download the presentation slides:

Uncovering and Understanding Relationships

The next important step is to reveal the relationships between the data entities. This where the graph technology helps us understand relationships – with a Commercial Graph (similar to LinkedIn or Facebook) you can relate customer profiles with products, accounts, family members and locations. You can establish many-to-many relationships between these data entities to understand where customers shop, what the products of interest are and who can influence their decisions. Uncovering relationships using graph technology helps you with identity resolution, finding influencers in the customer segment, or group individuals into a household and develop targeted campaigns. For B2B customers, you want to see the organizations and business units connected to it, key stakeholders and users of your products, or even contracts associated with various entities.

Single Source of Reliable Consumer Data for Operations and Analytics

Once you have the reliable data foundation, you can provision the data to all customer applications and channels for the connected experience. Moreover, you can provide the data to analytics systems to gain deeper insights about:

  1. Relationships: Modern Data Management lets you utilize predictive analytics and machine learning to guide users and provide intelligent recommendations, based on data and behavior. It helps with identity resolution, can suggest your new relationships and identify influencers (like LinkedIn.)

  2. Next-best-action: Recommendations like the next best offer to send to a customer, at the right time, using their preferred channel and identifying the key influencer to contact in an account and what to offer.

  3. Data quality: Recommendations to improve data quality by suggesting better matching rules, finding potential matches as you onboard new data sources and determining profiles with poor data quality and wrong addresses.

With Reliable Data, Relevant Insights and Recommended Actions enabled by Modern Data Management, we can understand the customer better and provide more connected experiences.