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.

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.
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.
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.

Business, Compliance

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.

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Business, Compliance, Financial Services, IT

How Can Traditional Financial Services Respond to FinTech Disruptors?

Ankur Gupta, Sr. Product Marketing Manager, Reltio

Traditional financial services companies (FSIs) such as credit unions, banks, credit-card issuers, insurance providers, and stock brokerages face several challenges ranging from ageing IT Infrastructure and legacy systems to inconsistent customer experience, increasing regulatory risk, and fierce competition from FinTech disruptors such as Fundbox, SoFi, Plaid, Blend, and the like. Traditional financial players need to fully harness the power of data to improve customer experience, IT & business operations efficiency, and regulatory compliance.

1. Embrace the Cloud

Cloud infrastructure delivers both economic value and business agility. It helps companies scale on-demand usage and quickly connect with value-added business partners to improve customer experience and functionality. The rapid adoption of public cloud by some of the largest SaaS applications in the world such as salesforce.com, Workday, Netsuite and others, has proven that companies across all industries believe their data is safe in the cloud. Strikingly, many regulators are adopting the public cloud for their applications, indicating their comfort with the ability of cloud vendors to meet regulations. A cloud-based Self-Learning Data Platform provides fine-grained, attribute-level, visibility of who searched for, who looked at, and who modified what data. In addition, it undergoes regular third-party security penetration testing to detect and close any security holes. Read this white paper Cloud Data Management and Applications – Why It’s Safe and Secure to learn more.

2. Leverage Agile Data Architecture

A Self-Learning Data Platform offers a technology architecture that is better aligned to the lines of business and capable of responding to FSIs’ future business models. For example, it can participate in customer journeys—such as shopping, travel, and vacation—as opposed to product-level processes, account opening, and onboarding. The multi-domain meta model allows for agile changes to support business needs. In addition, one doesn’t have to spend several months to upgrade the system. With no fixed schema and a NoSQL storage layer, data model is quickly configured and changes can be made in minutes to adapt to evolving business needs.

3. Drive Operational Excellence

A Self-Learning Data Platform helps financial enterprises continuously organize all data about their customers, products, compliance, and regulations, as well as business transactions, into a reliable and secure data foundation. Once data is organized, enterprises provision the consistent and clean data to all operational and analytical systems to improve operational efficiency thus reducing operational cost and cost of compliance. For example, it can help financial companies smoothen operations for customer onboarding, transaction monitoring, and regulatory compliance reporting. Likewise, it helps facilitate faster and better risk rating, credit, or coverage decisions through a better understanding of the applicant.

4. Deliver Superior Customer Experience

Data silos across departments, products, and geographies lead to inconsistent customer information across different business units within a financial organization. It leads to disjointed customer experience, raised costs of serving customers, and reduced share of wallet. A Self-Learning Data Platform provides detailed views of clients with complete profile, interaction and transactional data converged from multiple internal and external sources. It allows to deliver consistent information across internal teams, across channels (mobile applications and e-commerce platforms and so on) and throughout the client journey to deliver great customer experiences and frictionless banking. In addition, financial institutions can understand the total worth of the household to make relevant offers rather than making decisions based on individual disconnected accounts owned by different family members. The Self-Learning Graph, for example, can help group consumers into family units based on an individual consumer’s social connections, locations, and purchased products.

5. Meet All Regulatory and Compliance Needs

Maintaining regulatory compliance is a top and ongoing concern for financial services companies. Inability to stop data breach and meet ever-changing regulatory requirements result into downgrades in regulatory ratings and sometimes punitive action by the regulatory agency. Also, it inhibits FSIs’ ability to leverage new business models that necessitate sharing customer data outside of the FSI relationship. A Self-Learning Data Platform provides comprehensive auditing and tracking features to guarantee compliance by managing a historical trail for any data merged or updated. It allows FSIs to respond to a growing number of legislative and regulatory initiatives including Comprehensive Capital Analysis and Review (CCAR), Foreign Account Tax Compliance Act (FATCA), Know Your Customer (KYC), and new regulations such as GDPR by providing accurate and timely compliance reporting.

6. Expedite Post M&A Requirements

Mergers and Acquisitions (M&A) continue as financial institutions struggle with the costs of regulation and strive for growth in a more competitive marketplace. A Self-Learning Data Platform provides built-in mergers and acquisitions capabilities for powerful pre-merger analysis and accelerated post-merger integration. It helps expedite post M&A requirements by onboarding, rationalizing, and delivering customer, product, employee, and other critical business data to support consolidated financial reporting, risk management, cross-sell marketing campaigns, and other post M&A business needs.

Adoption of a cloud-based Self-Learning Data Platform can enable traditional financial services companies to deliver faster, affordable, and more personalized products and services in real time that meet the requirements of regulators while also better serving the needs of their customers.

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Business, Life Sciences

Patient 360: Molecule to Market

Ankur Gupta, Sr. Product Marketing Manager, Reltio

The rise of the Chief Patient Officer and the “P–suite” emphasizes a commitment to a culture around patient-centricity across life sciences companies. Patients are becoming increasingly demanding and taking greater control of their own healthcare decisions. They expect all relevant parties like pharma, providers, and payers to collaborate and recommend the best treatment options.

It is essential for a pharma company to know their patient throughout the drug discovery, development, and commercialization process. Every department across a pharma company can contribute toward and benefit from complete patient understanding. Some of the use cases are:

1. Patient-centric Drug Discovery and Development

Recruiting and retaining the right patients, and capturing all interaction and transaction events during clinical trials are vital to continuously develop new diagnostics and treatments. Patient-centric clinical operations lead to improved clinical trial outcomes, reduced patient exposures to drug adverse events, and faster drug discovery.

Today, reliable data, relevant insights and recommended actions via machine learning can be combined into one, single cloud application, delivering analytical intelligence and operational execution. Such cloud based Patient 360 data-driven application helps pharma companies derive meaningful patterns from an ever-expanding volume of patient health data and incorporate those insights into the drug development processes.

A Patient 360 application built upon a self-learning data platform delivers reliable, and up-to-date 360-degree views of patients, and their relationships with providers, healthcare organizations, caregivers, payers, plans, products and places, driving seamless omnichannel patient experience and improved health outcomes.

2. Personalized Corporate and Marketing Communications

Pharma companies are increasingly seeing more value in reaching out patients more personally and directly to improve patient loyalty and brand recognition. They want to execute direct-to consumer (DTC) drug advertising campaigns, deliver educational insights (such as medical information and pharmacovigilance) to inform patient decision-making and behaviours, and encourage patients to contribute their medical data to help advance medical knowledge.

A true Patient 360 data-driven application helps with prospect identification, capture, synchronization to CRM, and segmentation and targeting of existing customers and prospects in various life-stages. As part of the patient centric approach, brand-focused marketing is juxtaposed with the creation of content that supports a patient’s journey through disease progression. In addition, the Self-Learning Graph helps solve the problem of “householding” by grouping patients into family units by uncovering relationships. This patient-centric approach helps pharma companies to gain “profitable share” in competitive markets by informing their ‘pricing and contracting’ strategy and identifying treatable patients.

3. Superior Patient Experience with Full Compliance in Place

Pharma companies can add far more value to patients by executing adherence programs such as tracking drug usage and benefits. Likewise, they can run affordability programs to help patients stay on therapy (e.g. by creating apps to educate patients or by reminding them about medications). However, to drive such initiatives, one needs to collect and use large amounts of sensitive health-related data of patients. A modern data organization platform helps you respect and protect patient HIPAA and data security concerns. In addition, it helps you be GDPR compliant and allows patients to provide granular consent for sharing their data.

The data forms a key part of the insight needed to create better products and services, better engagement, adherence, and relationships with patients. Changing business models, expectations of “patient of one” and newer regulations will accelerate the evolution of pharma and healthcare. The transition will not be easy, but building a reliable Patient 360 with ability to pivot around pharma, provider, and payer is the first step towards patient-centricity.

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Business, Compliance, Consumer Brands, Healthcare, IT, Retail, Technology

Top Data Management Predictions for 2018

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?

Here are a few predictions and perspectives from industry experts to learn from and be smarter this year:

The Promise of Artificial Intelligence (AI) and Machine Learning (ML) Continues on

There have been repeated predictions over the last couple of years touting a potential breakthrough in enterprise use of AI and ML. This year is no different as the potential benefits from adding some kind of intelligent AI/ML layer to software emboldens more organizations across industries to adopt these technologies.

Daniel Hong, VP Research Director at Forrester Research predicts that having a successful AI-driven customer service or sales program will depend on the processes that support a blended AI approach. Humans will play a critical role in the ongoing optimization of AI.

Enterprise data-driven applications with a Modern Data Management foundation can blend customer data into one place, so that marketing, e-commerce, customer service and sales teams can get visibility into their customers’ preferences, behaviors, product interests and channel choice.

ML and predictive analytics are leveraged to suggest next-best-actions for sending relevant and timely information to customers and finding opportunities for up-sell and cross-sell. Insights like churn propensity, life-time-value, preferences and abandonment rates can be delivered to relevant teams, along with recommended actions that allow them to capitalize on this information.

GDPR will be Top of Mind for Many Organizations

Bart Willemsen, Research Director at Gartner predicts that by the end of 2018, more than 50 percent of companies affected by the GDPR will not be in full compliance with its requirements.

Effective May 25, 2018, the European General Data Protection Regulation (GDPR) will force organizations to meet a standard of managing data that many won’t be able to fulfill. They must evaluate how they’re collecting, storing, updating, and purging customer data across all functional areas and operational applications, to support “the right to be forgotten.” And they must make sure they continue to have valid consent to engage with the customer and capture their data.

Meeting regulations such as GDPR often comes at a high price of doing business, not just for European companies, but multinational corporations in an increasingly global landscape. Companies seeking quick fixes often end up licensing specialized technology to meet such regulations, while others resign themselves to paying fines that may be levied, as they determine that the cost to fix their data outweighs the penalties that might be incurred.

With security and data breaches also making high-profile headlines in 2017, it’s become an increasingly tough environment in which to do business, as the very data that companies have collected in the hopes of executing offensive data-driven strategies, weighs on them heavily, crushing their ability to be agile.

Customer-obsessed, Data-driven Retailers will Thrive

Sucharita Kodali and Brendan Witcher from Forrester Research predict that retail will grow.

“Bolstered by strong consumer confidence, not only will total US retail sales grow, but digital will impact more than half of this $4 trillion market. But retailers will need to be nimble and innovative to grow, right-sizing their store networks and real estate footprints and testing everything from Target-style flexible small-scale formats to service stores (think Nordstrom Local) and urban distribution centers – and more.”

— Source: “Predictions 2018: Customer-Obsessed, Data-Driven Retailers Will Thrive In 2018” Forrester Research November 14, 2017

With the Cloud Infrastructure as a Service (IaaS) wars heating up, players such as Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure continue to attempt to outdo each other on all vectors including capabilities, price, and service.

To avoid being “Amazoned,” some retailers have even adopted a non-AWS Cloud policy. For most, however, it’s about efficiency and cost. Multi-cloud means choice and the opportunity to leverage the best technology for the business challenges they face.

Today’s Modern Data Management PaaS are naturally multi-cloud, seamlessly keeping up with the best components and services that solve business problems. Acting as technology portfolio managers for large and small companies who want to focus on nimble and agile business execution, these platforms are democratizing the notion of multi-cloud for everyone’s benefit.

The Deck will be Cleared for Accelerated Enterprise Digital Transformation

Joe Pucciarelli, Serge Findling and Michael Jennett from IDC predicts by 2019, 60% of CIOs will complete infrastructure and application re-platforming using cloud, mobile, and DevOps, clearing the deck for accelerated enterprise digital transformation.

The business landscape is changing like never before. New revenue models, new competition, newer regulations and exceeding customer expectations are forcing organizations to rethink how they do business.

Digital transformation is one of the key initiatives for many organizations looking for ways to leverage digital technologies, become agile, more productive and above all, provide a connected digital experience for their customers. For digital transformation to succeed, a solid data management foundation is a must.

Today’s Modern Data Management Platforms as a Service (PaaS) seamlessly powers data-driven applications, which are both analytical and operational, delivering contextual, goal-based insights and actions, which are specific and measurable, allowing outcomes to be correlated, leading to that Return on Investment (ROI) Holy Grail, and forming a foundation for machine learning to drive continuous improvement. As an added bonus, multi-tenant Modern Data Management PaaS in the Cloud, will also begin to provide industry comparables, so companies can finally understand how they rank relative to their peers.

Digital will Disrupt Siloed Healthcare Ecosystems

Kate McCarthy and Alex Kramer from Forrester Research predict that digital will disrupt siloed healthcare ecosystems in 2018.

“For those who have alarm fatigue from all the times disruption has been predicted for our industry, but never came to fruition, this time is different. All the drivers for change remain –cost, quality, regulatory — but unlike times past, the disruption is already underway … And as cool as this is on its own, it is the proof the industry needed to confidently step forward and build digital experiences to engage our customers the way other industries have for more than a decade.”

— Source: “Healthcare Predictions 2018” Forrester Research December 18, 2017

With the emergence of IDNs, ACOs and MCOs, the approach to healthcare is evolving. The focus is on overall well-being and quality of life, rather than a one-time treatment. This requires a new patient-centric approach, complete understanding of the patient’s needs, behaviors and preferences, and focus on building long-term relationships.

In this changing healthcare environment, a modern approach to data management that enables complete understanding of patients, physicians and other partners across all clinics and facilities, while guaranteeing HIPAA compliance is necessary.

Whatever the industry or business need, most enterprises will need to first focus on IA (Information Augmentation): getting their 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.

Why organizing data in 2018 should be the top priority for organizations of all sizes

Business, Commercial Graph, Consumer Brands, CPG, IT, Retail

7 Habits CPG Brands Should Adopt to Reverse the Digital Curse

Ankur Gupta, Sr. Product Marketing Manager, Reltio

1. Focus on Data Quality

Given the vast volume and variety of data that CPG companies manage, ensuring the accuracy and reliability of data is critical. All digital transformation and personalization efforts would fail if data underneath is of poor quality, siloed and delayed. Using machine learning within modern data management platform not only helps determine and improve data quality but also enriches the data with relevant insights and provides intelligent recommended actions for data quality and operational improvements. For example, if you are running a campaign for a major product launch, you can eliminate consumer profiles with low data quality (DQ) scores.

2. Be Agile with Multi-model Data Management

Using legacy tools built on relational databases are too rigid and inflexible, making it difficult to support the dynamic needs of a modern business. For example, adding new data sources or attributes to the customer profiles can result in costly data migration projects. Another challenge is the inability to manage the relationships between various data entities, such as people, products, organizations and places. Modern data-driven CPG brands prevent big data indigestion by using a multi-model, polyglot storage strategy to store and efficiently manage the right data in the right storage. It helps them deliver faster and higher business value from their varied data assets.

3. Leverage the Power of Multi-domain

With “single domain” Master Data Management (MDM), each data entity type has its own unique data store and business logic. On the other hand, a Modern Data Management Platform manages multi-domain (customer, products, stores, suppliers) master data along with transaction and interaction data, third-party, public and social data. Its graph technology makes it easy to describe and visualize complex, many-to-many relationships among customers, products, stores and locations for faster and reliable decision-making. For example, with the help of a graph, CPG brands can rapidly traverse links between consumers, products, purchases, and ratings to make personalized recommendations. They can also tell if the visitors and shoppers browsing their website are from the same household or not.

4. Uncover New Business Models

“Servitization” of products is commonly seen in consumer categories such as music (iTunes and Spotify) and books (Amazon Kindle) but also in business services such as Xerox moving from photocopiers to document services. Historically, CPG companies have been resistant to the move from products to services. Their relationship with their consumers has often been mediated via retailers. Modern data-driven CPG brands often bypass retailers and sell directly to customers (DTC). For example, Dollar Shave Club is offering a monthly subscription to deliver razors and other personal grooming products by mail. This gives them the opportunity to engage directly with their customers, to collect interaction data, and to expand their digital footprint.

5. Explore New Data Partnerships

Data is an enabler of innovation. To keep up with the rapid pace of digital transformation, CPG brands need to develop a culture of collaboration and pursue intra and extra-industry partnerships. They need to recognize that many new entrants are not simply additional competitors. Instead, they represent possibilities for completely new types of business models that over time will blur traditional distinctions between retailers and manufacturers.

6. Augment Decision Management with Artificial Intelligence (AI)

Data-driven CPG companies look at AI through the lens of three business capabilities: automating business processes, gaining insight through data analysis, and engaging with customers and employees. They constantly innovate and disrupt by embracing new technologies to meet the high expectations of consumers. A Modern Data Management Platform coupled with Machine Learning enables contextual information and helps consumer brands answer high-impact business questions such as – Will my customer buy this product or not? Is this review written by a customer or a robot? Which category of products is most interesting to this customer? And so on.

7. See GDPR Compliance as an Opportunity to Improve Customer Experience

CPG brands will be required to be more transparent about how they use consumer data. New regulations like GDPR and increased oversight has important implications in terms of regulatory compliance, product development and marketing messages. Moreover, there are increasing consumer demands for transparency on how companies perform when it comes to sustainability and corporate social responsibility as well as where products are made. A Modern Data Management Platform as a Service (PaaS) helps you create a complete consumer profile with full data lineage, governance, and workflows to continuously manage consumer rights and consents.

Consumer brands are facing unsteady growth, tightening profit margins, complex regulations, and growing competition from lower cost private label brands. Adopting these seven habits would help them reverse the digital curse, achieve hyper-personalized customer engagement, and stay ahead of competition.

Assess Your Data Readiness for Digital Transformation

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Business, Commercial Graph, Compliance, Technology

10 Ways to Prevent Your Data from Being a Turkey this Thanksgiving and Beyond

Ramon Chen, Chief Product Officer, Reltio

Here are 10 ways you can prevent your data from being a turkey at Thanksgiving and Beyond

10. Gobble up your data, but make sure it’s more than just well done and reliable

9. Visualize your data, but know it’s just dressing without recommended actions

8. Pluck out relevant insights using a feast of machine learning algorithms, from an open ecosystem

7. Remember there’s always more than meats the pie (chart)

6. Prevent big data indigestion using a multi-model, polyglot storage strategy

5. Expect compliance to be handled by your platform, GDPR is the gravy not the main course

4. Don’t just save dessert room for data-driven applications, they’re part of a complete meal

3. Go multi-cloud, it will ensure every meal is always less filling and tastes great

2. Stop tryptophan-ing over leftover legacy MDM tools, you don’t have the space or time

1. Give thanks to your teams who are data-driven and helping your company be right faster.
If not send them on a field trip to learn how.

Happy Thanksgiving to you and your family!

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

2. Gain Brand Loyalty and Attract New Customers

3. Invest for the Future

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

1. Embrace the Cloud

2. Leverage Agile Data Architecture

3. Drive Operational Excellence

4. Deliver Superior Customer Experience

5. Meet All Regulatory and Compliance Needs

6. Expedite Post M&A Requirements

1. Patient-centric Drug Discovery and Development

2. Personalized Corporate and Marketing Communications

3. Superior Patient Experience with Full Compliance in Place

1. Focus on Data Quality

2. Be Agile with Multi-model Data Management

3. Leverage the Power of Multi-domain

4. Uncover New Business Models

5. Explore New Data Partnerships

6. Augment Decision Management with Artificial Intelligence (AI)

7. See GDPR Compliance as an Opportunity to Improve Customer Experience

Here are 10 ways you can prevent your data from being a turkey at Thanksgiving and Beyond

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