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Thank You to our Customers & Partners. Reflecting on A Most Wonderful Data-driven Year

As 2014 draws to a close, I can’t help but reflect on how fortunate Reltio has been. We’ve been blessed with the confidence entrusted to us by customer and partners alike. From multi-billion dollar enterprises to emerging startups who rely on us to help them improve the reliability of their data and deliver relevant information to their business teams, to partners who view us as a key part of their future strategy.

We have developed friendships with both IT and business teams. Through our belief that all stakeholders in the information chain must collaborate together to enrich data. Along the way, we have been selected over a wide variety of excellent products, all with their merits but each missing elements that made them unable to meet the needs of our customers and partners.

Reltio was founded upon the belief that organizations need reliable, relevant access to information at their fingertips. Turning their data into information and knowledge assets in the most efficient way. Shattering the traditional notion that IT must combine multiple technologies to manage different types of data, and that business users must purchase standalone tools to do their own analysis.

Reltio manages all data types including multi-domain master data, transaction and interaction data, third party, public and social data. Data is fused into a new breed of data-driven applications that business teams love to use every day. We pride ourselves in helping enterprises obtain a complete understanding of their business, by combining operational and analytical silos so teams can continuously collaborate to improve the reliability of information. They receive recommendations relevant to their goals and can take immediate action, all within the same application. This occurs in a closed-loop cycle that continuously assesses and publishes outcomes to demonstrate value and refine recommendations for better results. 

Here’s how we were compared to many of the tools and technologies we encountered in 2014:

  • Master Data Management Only Tools – MDM tools who have been around for 10+ years such as Informatica MDM (formerly Siperian) and even a new breed of cloud MDM offerings continue to see Master Data Management as a separate siloed discipline, requiring complex IT infrastructure, processes, leading to months and years of design and implementation, before “quality” data can be made available to business users. Often the delivery latency of the information leads to stale data. Meanwhile outside of the MDM framework, business users generally have access to new and often better insights, with more up to date information. Unfortunately they are unable to provide this feedback through existing MDM tools in a collaborative and controlled manner, effectively wasting valuable intelligence and competitive advantage. Business and IT teams share a frustration that precise accountability of the investment made in MDM infrastructure and business outcomes has not been realized.
  • BI and Analytics Tools – Powerful and sophisticated visualization tools empower certain business users to get faster access to information on their own. Self service management and convergence of data at scale from multiple sources is possible, however the data is not guaranteed to be clean and accurate prior to analysis. Looking at data that may be incorrect, can lead to wrong conclusions faster. Many of these tools are designed for “non-data scientist” use, but they are still beyond the skill or patience of field teams to use in the context of their day-to-day operations. More importantly, these tools still require action to be taken in separate siloed operational applications, and they have no means to automatically correlate the results of actions back to the analysis performed.
  • Horizontal Packaged Business Applications – Applications such as CRM, ERP, HR and financials are widely adopted by many enterprise for core business processes to run their business. However the legacy design and process driven nature of these applications are holding the company back. Many end-users question why in the age of Facebook and LinkedIn, they are still stuck with manual data entry, jumping between applications to get the complete view they need, and having to sift through complex patterns of information. The next wave of competitive advantage comes from enterprises being able to deliver data-driven applications to every single one of their employees, with relevant insights and recommended actions specific to their industry, use case and job.
  • Big Data Infrastructure and Tools – Hadoop, HBase, Cassandra, Graph Databases, MapReduce, Spark and the continuous stream of new technologies designed to handle ever increasing volume, variety and velocity, have changed the way data can and should be managed. However stitching together all of the pieces together such as the concepts of mastering data, relationship discovery, and other disciplines, and genericizing it in a complete end-to-end platform to meet the business needs across an enterprise is a multi-year endeavor. Not all enterprises have the resources to “build” from scratch and to continuously keep up with an evolving landscape, which means ongoing maintenance, integration and testing.

Thank you again to everyone who we’ve worked with in 2014, and for those we’ve not yet had the opportunity to meet, we hope to be able to share our experiences with you in 2015. Please enjoy the short holiday video below.

Now We’re Really Cooking – Reltio Holiday Party Dec 2014

A team that cooks together, conquers the Modern Data Management and data-driven applications market together!

What a fun time, delicious food and great company (pun intended).



We were on fire!

We were on fire!

We flew the whole team and spouses in from all over the country for a night of unforgettable fun.

Secret Santa? Yes Please.

What will we do as an encore next year? One things for sure, we will certainly top this one.

Why Life Sciences Must Go Beyond Master Data Management (MDM)

Many of the team here at Reltio formed the nucleus of Siperian (acquired by Informatica in 2010), the leading on-premises MDM tool widely adopted by life sciences companies. Back in 2005, master data management  (MDM) was just taking shape and companies used MDM primarily to improve Siebel CRM data quality before upgrading and migrating their on-premises systems. Back then Siperian was preferred by many to the “seamlessly integrated Siebel Universal Customer Master (UCM)” offering, proving that best-of-breed solutions can be superior to integrated offerings that are designed for a single primary purpose.

One of the biggest issues we faced with Siperian (now Informatica MDM) was defining a relational life sciences data model that could capture not only the basic attributes of healthcare professionals and organizations, but represent real-world  HCP-to-HCP, HCO-to-HCO and HCP-HCO relationships. Also thrown in for good measure was an emerging need to master product data, product hierarchies, groups and baskets for pricing and competitive analysis, and to feed product information (PIM) systems.

At Siperian we admittedly struggled with basic hierarchy management and performance issues with merge and especially unmerge. While we preached multi-domain and coined the term Universal MDM, we were never completely successful with standalone product master data management, let alone bringing together both customer and product data into a single consolidated Siperian Hub.

Back then, the best databases we had to model and store life sciences entities and their relationships were the likes of Oracle, DB2 and SQL Server. Cloud and big data technologies such as graphs, columnar stores, HBase (on Hadoop) and Cassandra simply weren’t available.

Fast forward to the present, the MDM landscape remains more or less unchanged despite a quantum leap in technology. 

  • Informatica MDM is still an on-premises solution with many of the same challenges we faced while at Siperian

  • Cloud-based customer master only offerings focused on improving CRM data quality a limiting. Much like Siebel UCM did for Siebel CRM over 10 years ago

  • Customer and product masters are still supported through separate siloed hubs, even when built using the same tool. In fact, Gartner continues to publish separate customer and product magic quadrants as if to re-enforce this fact

  • Master data must still be delivered to data warehouses or operational data stores in order for business users to get a promised “complete view”

  • MDM systems and tools built on 1990s relational database technologies continue to hinder the ability to model real-world many-to-many-to-many relationships that graph technologies are designed for

For the most part, life sciences companies are no closer to getting basic affiliation management functionality, or their dream of an all encompassing key account management application as they are hindered by legacy MDM tools. Even a new wave of cloud-based MDM solutions do not make things any better. The good news is that life sciences companies can avoid a new kind of MDM (“Making Da-same Mistake”).

The most popular consumer facing applications today such as LinkedIn and Facebook have shown that business facing data-driven applications can be cloud-based, handle multiple data domains, manage structured, unstructured, master, transactional, activity and social data. Companies should expect complete end-to-end “modern data management” instead of relying on recurring “next generation master data management” promises that remain unfulfilled.

CDO Summit in Houston Showcases Data Management Innovation

I recently had the pleasure of presenting at the Chief Data Officer Summit in Houston on the topic of “Bringing Data-driven Applications to the Enterprise”. I believe the feedback I received from the audience as well as several other sessions I attended continues to reinforce where the industry is going. 

  • Sebastian Gass, Regional Mgr. IT North America Exploration & Production, Chevron Corporation spoke passionately about siloed data as a major challenge in his opening keynote “Data-Driving and Enabling Chevron’s 2020 IT Strategy”
  • Joseph Gollapalli, Analytics Leader, GE Oil & Gas presented the impressive architecture of GE’s Industrial data lake designed to bring together data across hundreds of data sources for analysis, and a resulting user friendly data-driven application that they were able to build that used recommended actions to keep parts in stock
  • Tom Kunz, Data Manager Downstream at Shell provided an in depth look at the rigor of measurement and continuous improvement cycle that Shell engages in from a master data management and data quality perspective

These separate sessions touched upon the need for reliable data, relevant insights and recommended actions in enterprise data-driven applications for the enterprise.

Since the majority of the attendees were from the oil and gas and energy sectors, we presented and demoed our all-in-one Well Master Application bringing together master data, interaction data, 3rd party data and social data. With a “linkedin-style” UI and comprehensive data quality built-in.

All of the big data vendors were exhibiting their wares at the conference. Like oil prices, the popularity of technologies and tools will rise and fall. But one constant remains, everyone agrees we must reduce the time for business users see value. Spending millions piecing together infrastructure, big data technologies, MDM systems, analytics tools, and custom UIs is not the answer.

The Secret Sauce of LinkedIn, Facebook & Google Now Available for the Enterprise

What do consumer giants such as LinkedIn, Facebook and Google have in common?

Apart from being used daily (okay hourly) by all of us, and the subject of “I wish had invested in them early AND held their stock” regret, they all use proprietary versions of graph technology. It helps them continuously deliver relevant information through easy to use interfaces.

What are graphs good for and how do they work? In a nutshell, graphs are able to make sense of large datasets, by quickly determining how different data points are related or how similar they are.  They allow “real-world” relationships between people, products and their activities to be captured and analyzed. Much has been written about the growing popularity of graph databases and techniques and how they are being used.

LinkedIn has its Economic Graph, Facebook the Social Graph and Google their Knowledge Graph. They have all developed proprietary graph capabilities to give them the flexibility to extend and scale beyond the limitations of traditional graph databases.

@Reltio we have followed their lead because we believe that ever changing and evolving market, business and regulatory conditions continue to dictate that enterprises must rapidly adapt. This means business users need applications that they can rely on everyday to help guide them so they can meet those challenges head-on.

While off-the-shelf graph databases are powerful, it still boils down to how you build contextual applications on top of the stored data. Not everyone has the means or appetite to purchase a graph database and continuously update and feed data into it AND custom develop, manage and maintaining business applications on top to use the data.

We developed the Reltio Self-Learning Graph to work under the covers as part of the Reltio Cloud, with a key ingredient of reliable data. Enterprise data needs to be trusted and of higher quality than consumer information. By fusing together reliable master data, relevant big data insights and recommended actions, we believe business users can finally have enterprise-grade data-driven applications with the same agility, flexibility and ease-of-use of their consumer counterparts.



The Future of Software: Enterprise Data-driven Applications

Data-driven is an over-used term these days, leading people to ask, “What is data-driven? I thought all my apps had data in them?”

@Reltio we believe that data-driven applications such as LinkedIn have shown how applications can truly combine relevant insights and recommended actions, into a single application that blurs the lines between analytical visualization and operational execution. 

When you use LinkedIn, you don’t think about what functions you should be using to analyze the data, or how you can connect to a peer that LinkedIn has surfaced as a recommended action. Whether your goal is to find a new job, connect with a potential partner or prospect, you easily and seamlessly accomplish tasks within the application, everything is in one place, with a wealth of data to guide you. 

It’s no wonder that leading venture firms believe that a new generation of enterprise data-driven applications will be revolutionary:

Amidst the consumerization of IT, the successful enterprise applications are going to be the ones that empower and appeal to users and not necessarily IT. Thus, these user-centric applications need to deliver value directly to the end user. In order to do so, new enterprise software need to be fundamentally “data-driven” … Today, only a few truly data-driven consumer applications exist; the enterprise sector as a whole has been lagging.

— The last mile in Big Data: How data driven software will power the enterprise | ACCEL PARTNERS

Traditional SaaS and on-premise software will be around for a long time and these vendors will add more data intelligence to their offerings. They will be joined however, and possibly threatened by, a new generation of nimble and innovative next generation SaaS companies that will combine data and domain expertise to add massive business value to their customers. These Data Driven Solutions represent the future of software.

— 8 Ways To Build And Use The New Breed Of Data-Driven Applications | VENROCK via Forbes.com

If everything goes as expected, virtually every category of enterprise applications will be transformed by the insights automatically derived from a multitude of data sources …
… Business users increasingly want the same level of sophisticated, quick insights powered by smarter software.

— Data-Driven Applications: The Next Generation of Big Data | REDPOINT VENTURES

One critical component that is missing from the discussion is that of reliable data. For business users to trust a new wave of enterprise data-driven applications the data must be complete and accurate. Separate siloed data management solutions feeding applications are not enough, since information needs to cross multiple domains and be available in real-time.  @Reltio our enterprise data-driven applications come complete with a built-in data management foundation, allowing you to rapidly deploy solutions to meet your biggest challenges.

A New Twist for Master Data Management – Graph Technology

Many organizations have tried master data management as a means for reconciling disparate information systems. While some projects have succeeded, many have struggled with any number of organizational, technical or process-oriented challenges. Lately, a novel technique of using graph technology offers promise.  

Reltio’s CEO Manish Sood joined this episode of DM Radio to discuss the approach, together with several experts  including Aaron Zornes of the MDM Institute and Christophe Marcant of Stibo Systems.

Freedom to Choose … Your Data

Patients and payers are gaining influence over healthcare decisions, physicians. Other prescribers and healthcare organizations remain central to driving high-quality, cost-effective care. Understanding the decisions they make and how these decisions impact patient outcomes – is vital. As the business adjusts to these changes, it is important to understand who these customers are and how best to engage with them.

Many companies struggle to keep their provider, payer and patient master records in order. It’s an ongoing monumental task that requires constant evolution of data management practices that IT organizations are challenged to handle due to the constantly evolving nature of the data and data sources.

Help has presented itself in a few forms. There have been solutions mainly focused on improving the quality of customer data for CRM.

While useful and incrementally more efficient, these solutions are not for everyone. The life sciences industry has a wide variety of quality data providers, MedPro, LexisNexis Enclarity and HMS, DarkMatter2BD, IMS, Cegedim and others, each delivering unique value. If time and effort of acquisition and integration were not an issue, most would prefer not to be “locked-in” by a single data source.

A better alternative might be a dynamic and open data-as-a-service (DaaS) offering that allows companies to choose from the excellent range of data providers, pre-integrated and accessible through simple point and click. This on demand access allows for the business user to directly search for and acquire the data they need across multiple vendors, often previewing before purchase, and dynamically on boarding and combining the data with their own. They can even provide feedback and updates to the vendor seamlessly, often in exchange for credits. The third party provider gets higher quality data, which benefits other customers consuming the information, so everyone wins.

IT, who often have been saddled with, and wrongly pointed to as the bottleneck, due to the need to have them perform tedious ETL uploads, and constant verification of changed data between batches, are now free to focus on more pressing matters. 

@Reltio we believe DaaS is an essential part of a modern data management platform which in turn should be built into data-driven applications. With this, IT can not only be more efficient, but also more compliant by tracking update and consolidation of all attributes across all sources, internal and external. This gives them full visibility as to how the data procured, is benefiting business users in their day-to-day goals.

With the dramatically lower cost of ownership and efficiency, now combined with choice, higher data quality and greater compliance and visibility. That would be freedom of choice worth voting for.

Is MDM the Appetizer and Affiliation Management the Banquet?

Understanding the relationship between the vast number of Healthcare Professionals (HCP), the practicing locations and Healthcare Organizations (HCO) where they may see a patient is often the most critical – and most daunting – component of managing healthcare customer data. One might argue that performing master data management to generate reliable data for accurate matching is merely an appetizer compared to the potential banquet of affiliation management.

Markets have evolved to become more restrictive, and corporate-owned facilities increase in size and influence, understanding professional and organizational relationships and their structure is crucial for go-to-market applications. Industry sources estimate that 30% of healthcare professionals change practice locations every year, which further complicates providing current, accurate customer information – whether on a single physician or a convoluted institution. Some key questions that an affiliation management solution would need to answer include:

  • Who the key practitioners are?
  • Where do they practice?
  • How to map influence and competitive assessment?
  • What value should be placed on practice locations at the group level up through the organizational hierarchy when rolled up?
  • What commercial opportunities can be identified at the corporate, subsidiary and local level

In their effort to bridge this information gap, most Life Sciences organizations try to leverage pieces of customer and affiliation information from multiple third party data feeds and source some of this information from their salesforce, without the necessary tools that easily allow business users to understand and leverage insights to execute timely actions from such data.

Almost all organizations also make the mistake of using master data management tools merely to improve the quality of their CRM applications to try to solve their problem. Many traditional customer-focused MDM solutions are unable to handle the complexity of real-world affiliations.

It’s clear that affiliation management is an area that has not been addressed well to date. Now if only there was an enterprise data-driven application for that …

Same Tune Different Song: Leveraging Client Connections in Wealth Management

In my third and final post around how financial institutions can use a configurable data management platform to deploy data-driven applications to understand complex relationships and identify new opportunities, I’m turning the spotlight on wealth management.

Wealth managers can also benefit from data-driven applications to build detailed client profiles, model complex client-to-client and advisor relationships. Their focus would be to assess client lifetime value, increase client loyalty, augment share of wallet, and grow assets under management. Such a data-driven application would allow them to:

  • Build flexible client profiles to manage issuers, investors and syndication groups and track product, industry, region, and investment preferences

  • Aggregate and roll up accounts along household and relationship hierarchies to gain insight into client holdings 
  • Model relationships, including complex organizational structures and professional and personal relationships, to gain insight into centers of influence and six degrees of separation
  • Track all client interactions including call reports, phone calls, meetings, emails, voice notes, financial statements, letters of intent, and pitch books
  • Implement client loyalty programs to manage proactive interactions with top tier clients and analyze performance against service level targets
  • Aggregate sales and asset data at the firm, branch, and advisor-level to target future sales, service, and marketing activities
  • Automate activity plans to improve the efficiency of standard business processes such as account openings, know your client (KYC) updates, and client reviews
  • Automate service requests to ensure timely follow ups and instill advisor loyalty

Data-driven applications have the potential to transform how enterprises in all industries can better serve their customers and uncover new opportunities. The examples I cited in my three posts about financial services are examples of the same capabilities being applied to different use cases through simple configuration. @Reltio our belief is that we can apply data-driven applications to solve the most complex and challenging business problems.