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Fast-Track Your Data Monetization

Ramya Krishnan, Reltio

This time Janine was beaming.

I had a pleasant meet last month with Janine, when we had discussed How to get Data on Tap for all Your Data Woes. My old friend was super curious to learn more, as we met again this month for another cup of coffee.

“So nice to see you, Ramya! Thank you so much for the Data-as-a-Service guidance. We have more or less decided to go for it, and I need you to fill me on more details. Monetizing data – as you had mentioned last time.”

“It’s a smart way of tapping the potential of your data to create a new revenue source. You’ll wonder why you never thought about it!” I laughed as we sat down. “As you know Janine, DaaS works for streaming data in real-time from anywhere in the world and from any device, delivering consistent data quality and agility. Naturally you pay for accessing the data. Now suppose others pay you for accessing your data?”

“Oh!”

“With DaaS, you need not be a data consumer alone, you can be a data provider, too.”

“That is wonderful!”

“It is indeed. In the data economy, you have invested so much in creating a great asset, your enterprise data. How about licensing it for use by others? That is Data Monetization put simply.”

“That sounds really exciting. Being in Pharma, we consume huge volumes of data, but we also generate equally large data.”

“Trust me, data is the most exciting thing happening today, and it’s high time you participate in monetizing it. Reltio DaaS makes it very easy to fast track your data monetization. You already have quick access to datasets readily available from data tenants in the cloud. You have a single reliable source of data that is continuously updated and refined. Choose one or more from the multiple engagement models we provide, and you are ready to go.  The simplest is the Data Store-front where users can search and browse data, and download by paying.”

“Sounds amazing!”

“Another model is List Match Service, a stand-alone service to provide ad hoc matching, enrichment, and validation. You can also consider Data API Services, which are custom services for a particular data domain. For example, HCP credential verification in the Healthcare and Life Sciences vertical where you operate.”

“Lots of options, more than I had thought.”

“Yes. Moreover, to help you monetize your data quickly, we have a robust API platform for custom services and embedded experience. In fact, we can help you set up in all four models, including MDM Data Subscription, which is the data delivery to MDM tenants.”

“Let me discuss with our people which way we should go.”

“Right. If you choose to go with Reltio, I promise you don’t have to run around as we take care of everything. We help onboard new customers much faster and we can enrich data via list match service. Being integrated with Reltio’s modern and scalable platform for continuous data quality and delivery, you can quickly implement any changes to data. And guess what, we have a convenient pay-as-you-go billing method. As your customers increase, you can also look forward to reducing distribution and operating costs.”

“So I guess everything is set up!” Janine was happy with the thought of generating revenue from enterprise data. Who wouldn’t be?

“Just one more point. I suppose complete tracking is part of the service.”

“It is. You can track attribute level audit, history, and lineage of all data usage and changes. Track record-level and attribute-level consumption patterns. Track a variety of contributing data sources for ongoing updates and downstream data distribution. And you don’t have to worry about strong data security and privacy regulations. Reltio platform is HITRUST CSF-certified, and GDPR compliant.”

“Very comprehensive and competent. These coffee sessions are turning out to be amazing for me!”

Like Janine, several customers have benefited from Reltio’s fast-tracked data monetization, extending their quality data to drive revenue for the organizations. How about you?

    What’s your Data Confidence Number?

    Nalini Mohan, Senior Technical Product Marketing Manager, Reltio

    Can you put a number to the level of confidence and trust you have in your enterprise data and analytics? Can you take that number and make powerful decisions that can transform your enterprise? According to a study by KPMG and Forrester Research, “60 percent of organizations say they are not very confident in their data and analytics insights” and “only 10 percent believe they excel in managing the quality of data and analytics”. [1]

    Reltio’s latest release offers a breakthrough data confidence capability to cross this chasm of trust for enterprises that use Reltio Cloud for their analytics and operations. Reltio Data Quality Confidence Indicators are continuously calculated for all profiles in Reltio Cloud and presented to the user as actionable metrics. For the first time ever, business users have a consistent way of quantifying data quality for reliability and business value.

    Data organized in Reltio Cloud is now the only data available to business users with a confidence indicator. Enterprises need quantitative metrics to gauge data quality. Reltio is the first to bring those to market, along with updates for scalability, connectivity, security and more, offering significant opportunities for many data-driven business initiatives. Reltio’s new data quality and ranking capability is also valuable for data stewards since it automatically prioritizes work by identifying data that is high in business value but low in quality. Data stewards can now focus their time and efforts for the greatest business impact, increasing productivity, aligned to business value.

    Have you Reltio-d your data today?

    It is an increasing imperative for enterprises to think about trusted analytics as a strategic way to bridge the gap between decision-makers, data scientists and customers to deliver sustainable business results. Reltio customers and users understand that it is important to “Reltio” their data before trusting it enough for customer 360 views, analytics, and machine learning, and for meeting data privacy and compliance regulations.” Reltio’s new data quality and ranking capability is also valuable for data stewards since it automatically prioritizes work by identifying data that is high in business value but low in quality. Data stewards can now focus their time and efforts for the greatest business impact, increasing productivity, aligned to business value.

    • Data Quality Indicators – A machine learning-based Data Quality IQ (DQIQ) score grades the quality of data in each profile, and a Reltio Rank highlights the importance and relevance of a profile compared to other profiles. Metrics are continuously updated and persisted as profile attributes, making them searchable and segmentable. DQIQ is being progressively rolled out to customers.
    • Customizable IQ Scores – Together with Reltio’s system-generated DQIQ score, customers can also leverage Reltio IQ, a separately licensed module, to customize their own DQIQ variants, and to create an unlimited number of IQ scores and attributes. Examples include Churn IQ, Upsell IQ, and Compliance IQ.
    • Relevance-Based Matching – Reltio Cloud also added a relevance-based match rule allowing users to define a match rule and associate an action to be performed based on the relevance score of the match pair for continued data quality improvement.

    The latest release (Reltio 2018.3) from Reltio builds upon its speed-to-value by making it even easier for business users and data stewards to onboard data through self-service. Reltio Data Loader allows any user, with the appropriate authority, to load unlimited volumes of data through an intuitive UI. Users can visually map, transform and load any amount of data, as well as monitor progress without having to write code. Organizations can now match external files against existing records in the tenant using smart matching, delivering instant actionable results.

    With over 85 million real-time API calls a day and 4 billion profiles continuously being organized, Reltio continues to invest in platform performance. Upgraded services preemptively take action to handle spikes during extreme traffic loads. They also continuously monitor data to detect anomalies across distributed repositories, and will initiate self-healing actions autonomously. Fine-Grained Role Management , Flexible Data Privacy and Protection and Continuous Compliance enhance the very powerful feature sets offered in this release.

    Visit the Reltio booth at the Modern Data Management Summit 2019 and test drive Reltio 2018.3 and its breakthrough Data Confidence features.

    [1] Building Trust in Analytics

    Reltio Supports Apple CEO Tim Cook’s Clarion Call for Stronger US Customer Data Privacy Laws

    Manish Sood, CEO & Founder, Reltio

    Apple CEO Tim Cook’s call for a U.S. privacy law, similar to GDPR (General Data Protection Regulation), is appropriate and timely. Five years ago this initiative would have been too big of a burden for companies because customer data is siloed throughout organizations in dozens or hundreds of separate systems. But today modern data management solutions that include technologies like machine learning empower organizations to implement data governance and privacy initiatives at scale, and are an essential part of their overall Customer 360 data strategy.

    GDPR has irreversibly changed the landscape for single customer view data projects for companies doing business in Europe. And there are lessons we can learn from businesses that have gone before us. Early efforts to comply with data mandates at the application level have come up short. Upholding GDPR principles, as companies have quickly realized, requires an enterprise data layer approach to PII (Personal Identifiable Information).

    Virtually all companies have customer data scattered across multiple networks and lines of business — the only way to manage this data sprawl so that customer data privacy compliance is assured is to discover, organize and control all customer data from internal, external and third-party sources. If that sounds like a tall order, the good news is that we at Reltio have already developed and implemented best practices that include the essential ingredients for ensuring customer data privacy for some of the largest global enterprises by:

    • Identifying all systems managing customer data

    • Blending different types of data

    • Understanding data ownership

    • Identifying data shared outside the organization

    • Maintaining data lineage across all customer attributes

    • Managing different types of consent, and their sources

    • Providing customers a way to make data-related requests

    • Deploying processes for required data access, change and deletion

    • Implementing a mechanism in place for timely reporting of a data breach

    Whether it’s correlating omni-channel transactions to customer master records and understanding, to determining how each customer is related to other members of a particular household, to whether those disparate household members have consented to their data being used and for what purpose. Maintaining data lineage to data sources and tracking to downstream applications so we know from where a customer profile attribute originated and which applications are using it is critical. This is the power of technology that enterprises already have to ensure customers are protected under the right to be forgotten stipulation. (As an aside, the right to be forgotten is a bit of a misnomer. The regulation actually stipulates that a company is required to retain evidence that they ‘forgot’ a customer. Make sure you pick a vendor who understands these nuances or your efforts may be undermined.)

    Under GDPR, individuals are entitled to data erasure, which means that at their request, all traces of their information must be purged, including legacy transaction data that might reside in activity logs. In sum, this mandates a comprehensive customer profile with a 360-degree view that can accommodate data-change requests and the ability to generate privacy compliance reports fast.

    Organizing data with a modern data management platform for growth strategies like new revenue models, improved customer experience, or other initiatives results in clean, reliable data with built-in customer data privacy compliance.

    A comprehensive customer-centric data management strategy that delivers data privacy capabilities is built on four pillars:

    • Consolidated profiles: Organizations need the ability to collate all data from internal, external, third-party and social sources. At the same time, they must have the power to trace and maintain data lineage across all attributes. This sweeping level of visibility is invaluable in the event of a data breach.

    • Managing relationships: Graph technologies play a unique role here — they offer a deeper and more accessible understanding of relationships between stores, locations, channels and types of consent. It also helps to trace the adult consent for capturing the data of a minor.

    • Data change requests: This is a critical requirement for GDPR compliance, and companies need to step up. Built-in workflows in the systems used should accommodate all customer data change requests, deletion requests, review requests and more.

    • Data as a Service with traceability: Drawing from third-party sources is a major benefit, but GDPR compliance requires tracking which attributes came from where.

    Those that view a U.S. privacy law as a regulatory burden rather than an opportunity have not yet internalized what it means to be a customer-driven company. Your customers’ privacy is already table stakes today and is part of your responsibility as you serve them. Compliance towards protecting customers’ privacy is a journey not a destination – get the right platform in place today so you can meet any regulation or concern that they might have. You’ll find that it will improve your brand, and customer loyalty, and that’s just good business.

    I’m proud that Reltio has been a pioneer in empathizing, understanding, anticipating and embracing customer privacy concerns, and that we’ve embedded product capabilities into our Reltio Customer 360 solution. I completely agree with Tim Cook’s call for a similar U.S Privacy law and as Reltio customers can attest they are already ahead of the game, with Reltio governing their sensitive customer data at scale throughout their organization using master data management, graph, and machine learning technologies.

    Why Reltio is All-in on Salesforce Customer 360, and the Adobe, Microsoft, SAP Open Data Initiative

    Manish Sood, CEO & Founder, Reltio

    Last week we saw some exciting announcements on the subject of Customer 360 and customer engagement. First Adobe, Microsoft, SAP and announced a new partnership: the Open Data Initiative, the intention to create a single data model for consumer data that is then portable between platforms. Then Salesforce announced Customer 360 at Dreamforce 18, to help brands identify who their customers are and how they previously interacted with them. The single customer 360 ID will enable companies to access customer information through Salesforce’s various applications including Sales Cloud, Service Cloud, Marketing Cloud, and Commerce Cloud.

    The announcement is great news for the industry, and I’m excited to share today that Reltio will be supporting both initiatives. This means that our customers, who are already in production using Reltio Cloud for Customer 360, as well as any future customers and partners, will be able to use Reltio to take full advantage as both initiatives evolve while benefiting from significant additional capabilities.

    Standardizing on a common data model, and having a single Customer 360 ID is important, but such efforts have historically been limiting. Common IDs and tight coupling of apps can solve some immediate problems, could provide a quick fix for small organizations but in the long run, can create new islands of data that can create challenges for larger enterprises. Reltio will help both initiatives by continuously unifying and organizing all data within their respective models while making it universally accessible for collaboration and consumption across the enterprise. Our philosophy has always been that standalone customer profile data reconciliation is insufficient without the benefit of understanding relationships across more than just the customer domain. Unlocking the value in relationships across multiple domains such as customers, households, stores, locations, products, parts, suppliers is essential to a true Customer 360, and Reltio delivers this at an enterprise scale.

    The Open Data Initiative announcement mentions a data model with over 50 different open source schemas stored in a data lake. These schemas require companies to stitch together the relationships across attributes, schemas, and profiles. Reltio helps this effort through logical metadata that combines all 50+ schemas into one unified canonical model and view of the customer data while ensuring continuous data quality that prevents a traditional data lake from turning into a data swamp.

    We started Reltio on the idea that customer data will always be distributed across dozens of application sources with different formats; that companies will keep on adding new sources; that understanding relationships across all data types, not just customer profiles are important; that agility is key, and that future-proofing from changes in technology investments is critical. It’s the reason why we adopted a multi-cloud, multi-model data storage architecture, rather than an arguably easier approach of committing to a single NoSQL, RDBMS, or a graph database. For over seven years, we’ve been delivering on the promise of the complete customer 360-view, through a foundation of trusted, reliable data, powered by open, next-generation modern Master Data management (MDM). We’ve delivered this by not just creating a better MDM in the cloud, but creating a true 360-view that includes master data, relationships across all data entities and correlating omnichannel interactions, transactions. But we didn’t stop there. We architected a platform that brings together analytical insight and operational execution using advanced analytics and machine learning to continuously correlate insight, action, and outcomes in a closed-loop, to extract the maximum business value out of data.

    Our customers – which include eight of the 10 largest life science companies, two of the top 10 cosmetics companies in the world, a global hotel and hospitality group, a U.S.-based healthcare insurer, one of the largest U.S.-based pet retailers, one of the largest mortgage providers – rely on Reltio for their digital transformation and customer engagement initiatives. From globally harmonizing customer 360-views across 140+ countries to bring together customer data from hundreds of millions of organizations, to real-time personalization, marketing segmentation of millions of consumers, Reltio continues to deliver on the promise of Customer 360.

    Speed, performance, and scalability are key. Over 4.3 billion mission critical master profiles, representing well over 1 Petabyte of storage under management in Reltio Cloud, is being accessed by business teams globally to the tune of over 85 million real-time API calls per day. As these numbers are growing by the second, our multi-cloud platform supported on both AWS and GCP provides the elastic and scalable horsepower to meet demanding workloads, and the aggressive growth objectives of global enterprises. They look to their “Reltioed” data as the trusted source for their analytics and machine learning (ML) initiatives, compliance requirements such as GDPR, and next-generation customer engagement and experiences.

    Reltio partners, including Salesforce, Mulesoft, IQVIA, AWS, GCP, Accenture, Cognizant, Wipro, ZS Associates, and Dun & Bradstreet, have all been part of successful Reltio for Customer 360 deployments, which bring together data across applications and sources in multiple Salesforce, Adobe, Microsoft, and SAP environments.

    These are exciting times for enterprises looking to transform themselves and leapfrog the competition. But it’s not easy, with a shifting landscape of technology choices, continuously evolving regulations such as GDPR, CCPA (California Consumer Privacy Act), and the need to operate at scale most cost-effectively. To companies considering how the Salesforce and Open Data Initiative announcements might impact their plans, please reach out to us to learn more about our support of both initiatives, and how we can get you on a path to future-proofed Customer 360 in a matter of weeks. To our current customers who have placed their faith, and most importantly their critical customer data assets into Reltio, I’d like to say thank you, and assure you that the best is yet to come.

    Data Secrets to A Successful Drug Launch

    Ankur Gupta, Sr. Product Marketing Manager, Reltio

    Value from pharma should be measured in terms of clinical outcomes, patient satisfaction, and cost reduction. Using data, pharma companies can enhance value for patients along the entire lifecycle of a drug, from drug discovery to commercialization to end of exclusivity.

    From the perspective of business strategy, value delivery can be seen as a three-step process as illustrated by David Ormesher, CEO of closerlook, in his PharmExec.com post.

    • Value Creation (discovery)

    • Value Capture (commercialization)

    • Value Extraction (end of exclusivity)

    Discovery Phase: Value Creation via Data

    It is important to capture unique customer insight to inform drug innovation. The drug should be relevant (to an urgent disease burden) as well as differentiated (relative to alternate therapies). These two factors will largely determine market access, provider endorsement and patient acceptance for a new drug. However, departmental silos between medical affairs and commercial side of the business, and lack of access to quality data lead to incomplete understanding of competition and the market.

    A Self-Learning Data Platform goes beyond a traditional master data management (MDM) offering and brings together patient, provider, payer, and plan data from internal, third party, and public sources to cleanse, match, merge, un-merge, and relate in real time. Platform’s multi-domain data organization capability helps perform deeper analysis to better understand the needs of patients, providers, payers, and relationships among these players. A Self-Learning Data Platform breaks down silos among medical affairs, marketing, business intelligence and manufacturing, and helps develop a common understanding of customer data and market insight across all departments.

    Research indicates that 81% of future drug sales performance is determined by actions taken during clinical development and early commercialization phase. It’s even more critical for a pre-commercial pharma which is planning to bring its first drug to the market. Early adoption of a Self-Learning Data Platform helps a pre-commercial pharma develop future-proof commercial infrastructure and put up business processes to launch their first drug with safety, efficacy, and desired formulary placement in place. Read the pre-commercial pharma success stories about how they successfully launched their first drug with the help of a Self-Learning Data Platform.

    Commercialization Phase: Value Capture via Data

    A new product’s commercial performance during the first six months after FDA approval is often considered a very important indicator for how the product will do over the course of its patent life. During Value Capture or commercialization phase, the purpose of data is to build trust and respect via data-driven personalization and engagement. However, pharma companies are unable to recognize prescribers and patients consistently across multiple channels and touchpoints. They often fail to increase content speed to market in their customers’ preferred channel. This leads to negative Net Promoter Score (NPS), increased defection to competitors, and loss of revenue and market share.

    The more you know about your customers – the physicians who can write the product – and what they care about, the more you’re able to build an effective campaign around a new product. What you need – an out-of-the-box, data-driven affiliation management application, with built-in MDM, for managing all relationships within and across HCOs and HCPs to support commercial operations, identify the right key opinion leaders (KOLs), and understand their influence.

    A Self-Learning Data Platform helps you organize launch as a micro-battle (See the Infographic “Make Your Drug Launch Truly Take Off”, Bain Insights, September, 2017), gather continuous front-line feedback from sales reps before, during and after the launch, and make rapid adjustments as needed to the launch strategy. It helps you make quick decisions on messaging, targeting and marketing investments. Such platform powers reliable advanced analytics by enabling master data profiles and graph relationships to be seamlessly combined with real-time interactions and analyzed in Spark. For example, when a new drug is launched, it helps track sales performance compared to projections so that you can adjust strategies whenever needed.

    Read the success story of a French multinational pharmaceutical company that built Customer 360 on top of a Self-learning Data Platform to support their account-centric field operations and personalized engagement.

    Loss of Exclusivity Phase: Value Extraction via Data

    At the point when a drug loses its patent protection, its price typically drops quickly as generic competitors enter the market. During this phase, there is often enormous pricing pressure from competitive products and health insurers. In addition to these external pressures, there is also internal competition for attention and resources, usually from a promising new product.

    The business strategy during Value Extraction is to increase efficiency via operational excellence. The main cost now is sales and marketing. This is where digital can play a very strategic role. Digital sales and marketing through non-personal promotion can become an effective substitute for sales rep promotion. By replacing expensive personnel costs with lower cost digital channels, we can reduce overhead costs but still maintain market share.

    Read the success story of one of the oldest and largest global pharma that consolidated customer profile across all business functions to improve customer experience across all digital touchpoints, and better engage high-value customers.

    Successful pharma companies use data as a competitive weapon to develop new sources of differentiation, focus on building superior customer experiences and treat drug launches as a micro-battle. How did your last launch perform vs. expectations, and what were the reasons for under-performance or over-performance? Which interactions matter most for your target physicians, and do you provide a superior customer experience? What are the three largest internal challenges your launch team faces, and what would it take to eliminate them?

    Read more Pharma Commercial Success Stories


    These 3 GDPR Requirements You Must Support Today are Nothing Compared With What’s Coming

    Ramon Chen, Chief Product Officer, Reltio

    On May 25, 2018 GDPR (General Data Protection Regulation) went into effect. The primary objectives of the GDPR are to give control back to their EU citizens and residents over their personal data, to simplify the regulatory environment for international business, and to unify regulations within the European Union.

    GDPR is relevant for any organization doing business with EU citizens, regardless of where the company is based. Personal data includes a wide range of personal identifiers, from addresses and public information, to social profiles, images, IP information, device IDs and medical and financial details.

    Consumer personal data collected within your company is often distributed to multiple systems and organizations, resulting in duplication. Your organization may be considering master data management (MDM) solutions to address various data management needs including compliance challenges. Legacy MDM systems may comply with a small part of the regulation by managing profile data, but they also leave it to you to figure out how to manage the transaction and interaction information distributed across systems and channels.

    Complying with GDPR should be part of your day-to-day operations. One philosophy is that a Modern Data Management platform should organically support the key elements of GDPR by managing your customer’s profile information, lineage and succession through your day-to-day data management activities.

    1. RIGHT TO BE FORGOTTEN – GDPR guidelines require your organization to support your customer’s Right to be Forgotten and purge their records upon request. Your business will also need to support your customer’s request for a copy of their information in a portable format. Any GDPR solution needs to guarantee purging of all traces by customer entity type in support of data erasure, including the removal of any attributes and historical transactions made by individuals captured as part of their digital activities, which is outside of the scope of traditional legacy MDM tools.

    2. CONSENT MANAGEMENT – Your company must also support a provision to produce any proof of consent provided by your customer on request, and a way for customers to withdraw the consent. Explicit consent is required before information is collected, and adult consent is mandatory when the collection of data involves children below the age of 16 years. Any solution that supports the management and maintenance of rights and consents must have the ability to capture and store consent types. Graph technology provides a great way to store relationships so you can easily capture and prove that an adult provided consent regarding the collection of information for a minor.

    3. AUDIT & LINEAGE – The new GDPR legal framework requires your company to support the ability to demonstrate the deletion of your customer’s private information. built-in audit and data lineage to support accountability for your business to demonstrate compliance. Attributes must also traceable back through lineage to the internal and external data providers they came from. In the case of a change request, the request can be routed back to its original source.

    While there are many tools being offered to meet GDPR and other regulatory requirements, companies should use a Modern Data Management platform that supports both offensive (e.g. improve operational efficiencies, deliver better customer experiences) data strategies, and has defensive (e.g. maintain compliance, reduce costs) data strategies built-in.

    Facebook has stopped short of promising GDPR level data compliance for US users. If you are a US company, even if you have no EU data, you should consider implementing a Modern Data Management platform that gives you GDPR-ready capabilities. Imagine the branding and goodwill you’ll get with your customers when they realize that you are taking measures above and beyond (exceeding that of Facebook) to respect their privacy and data.

    Finally it won’t take long for the US and rest of the world to catch up, the State of California recent enacted The California State Assembly’s passage today of the California Consumer Privacy Act (CCPA) which has many elements of GDPR. My article listed three very basic GDPR requirements, there are certainly many many more. Regardless of the solution or tool you put in place today you know that many more regulations are coming. A Modern Data Management platform does the heavy lifting for you today, and protects you into the future, allowing you to focus on your business.

    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.

    Four Ways to Use GDPR as a Strategic Driver

    Ankur Gupta, Sr. Product Marketing Manager, Reltio

    Post May 25, 2018, per the General Data Protection Regulation (GDPR), companies with business ties to the European Union need to comply to GDPR standards. The cost of non-compliance is huge, but the regulation is meant to benefit individuals as well as businesses. Let’s look at what it can unlock for you and your brand if you approach it in the right way. What about being able to say that you are the safest enterprise in your marketplace when it comes to data? How about if you can not only reduce operational cost but can also create new revenue streams by being compliant to GDPR and other upcoming regulations?

    1. Replace Legacy Systems by Future Proof Cloud-based Applications

    When companies are taking steps to comply with GDPR, they are required to perform a ‘spring clean’ of their data, which can in turn lead to multiple efficiency gains. Organizing data improves the way firms carry out analytics and take business decisions. To comply with the regulation, companies must be able to illustrate the entire data flow – how data comes into the company; how they store and manage it; and how they treat it at end of life. This will encourage businesses to replace legacy systems by flexible cloud services to be more nimble and transparent especially when regulatory regime keeps evolving. In addition, most large enterprises have grown through M&A. Thus, they can look at GDPR as an opportunity to get rid of obsolete software and accelerate application retirement.

    2. Gain Brand Loyalty and Attract New Customers

    Companies can leverage GDPR to change the landscape from risk mitigation to improving their long-term competitive advantage. They can see early GDPR compliance as a competitive differentiator and position themselves as leaders of an emerging new normal. We trust those businesses who values our privacy beyond mere legal compliance. Thus, GDPR is an opportunity for businesses to get their data in order, get compliant and become consistently transparent with their customers. In a post-GDPR world, data sharing would be seen in the context of mutual respect and value exchange. It is an opportunity to re-connect your business with your current and potential customer base and start a new relationship based on mutual trust and responsible personalization.

    3. Invest for the Future

    The criticism that GDPR compliance might restrict innovations in AI ignores a subject’s right to privacy and consent. In fact, not being GDPR compliant would impose far more constraints on data collection and processing, slow down the ability to leverage innovations in AI and pay an opportunity cost such as market share losses in the future. Read this article for more details – Understanding GDPR and Its Impact on the Development of AI. In addition, in an era of data-driven innovation, business partners need to work together across the value chain. Data-driven innovation requires a clear understanding of the data to be collected and the reasons for collecting it. There are double opt-ins in such value chain: both partners need to be clear about what data they have about each other, and why. It’s very important that their data sharing practices are compliant with GDPR and other upcoming data regulations. As a first step to GDPR compliance, companies must define the scope of GDPR-relevant personal data that is collected, processed, and shared. Once a company identifies the scope of GDPR-relevant personal data, it should catalog all internal and external data sources that fall within this scope.

    4. Execute A Delicate Interplay of Offense & Defense Data Strategies

    In the post-GDPR era, personal data protection will become a data strategy issue. To comply, businesses need to have solid data organization and data governance in place. The GDPR gives companies the opportunity to holistically re-assess all their data, not just personal data. Data defense is about minimizing regulatory risk and preventing theft. Data offense focuses on increasing revenue, profitability, and customer satisfaction. Strong regulation in an industry (e.g. financial services or health care) would move the company toward defense; strong competition for customers would shift it toward offense. The CDOs and the rest of the leadership should see GDPR as an opportunity to establish the appropriate trade-offs between defense and offense to support company’s overall strategy. Read this blog post for more details – Is Your Data Strategy Defensive or Offensive? Why Not Both?.

    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.