A modern Master Data Management (MDM) Platform helps businesses manage data like leading digital companies, leveraging continuous data organization and recommended actions to measure and improve their operations. Here are the seven reasons why enterprises should invest in a modern MDM Platform to drive ongoing business value.
1. Reduced IT & Operational Cost
A leading PC & printer manufacturer & re-seller created a single global view of accounts. Reltio Cloud delivered an Account 360 solution to manage complex relationships and hierarchies among their business units, customers, and partners. It helped the client improve their territory alignment, multi-channel campaign execution, and field incentive compensation planning. Reltio has helped increase their data load performance by 577X and is expected to save them more than $12M over 5 years through improved IT and data management operations.
2. Increased Productivity
A top 5 global pharma reduced cost of ownership by $3.6 million per year, and improved user query response time by 10X. Reltio Cloud was deployed to replace the client’s 67 MDM on-premises instances with just 3 regional cloud tenants. It allowed them to execute multi-channel marketing campaigns and simplify capabilities for cross-market queries on healthcare providers (HCPs). With Reltio Cloud’s simple configurability and ease of use, the business teams were able to give its field sales reps the ability to submit updates through the Reltio UI via mobile phones, allowing them to contribute towards overall data quality.
3. Fast Path to Digital Transformation
A top used car retailer consolidated data from 155+ store systems in less than 15 weeks to drive omnichannel customer experience. Reltio Cloud supported a Consumer 360 solution, managing the client’s multi-domain data (customers, vehicle stores, accessories) in a holistic manner while integrating real-time predictive analytics with full visibility into all customer interactions. The Customer and Vehicle Master helped the client’s marketing team execute better targeted campaigns and allowed the merchandising department refine inventory acquisition targets based on more complex demand analysis.
4. Enabling Connected Experiences
A leading pet specialty retailer leveraged data to tackle Amazon Effect and transform into a customer-centric service company. Reltio Cloud delivered a Consumer 360 solution that supported a single view of pet parents across all the client’s brands, joint ventures, and multiple systems. Reltio’s unique approach to modeling relationships with graph technology, combined with big data science, enabled the client to unlock the critical intersections of interactions and information by creating a single repository of trusted and mastered pet parent profiles in real-time.
5. Built-in HIPAA Compliance
A leading health insurer created a single view of their members across multiple systems to execute a member-centric omnichannel strategy. Reltio Cloud enabled a Member 360 solution, providing visibility into the entire touch-point history for each member and optimizing member experience while meeting HIPAA compliance requirements. It helped the client do segmentation, targeting, and direct-to-consumer (DTC) campaigns with higher precision. With Reltio’s cloud-based platform, flexible modern data architecture, and agile deployment the client delivered lower TCO and quicker time to value for their IT, sales, and marketing teams.
6. Speed to Value
An American diversified global insurer created a holistic view of their brokers for better segmentation, compensation elasticity and retention analysis, all in four weeks. Reltio Cloud for Account 360 helped the client create a single view of their brokers (firms and captive agents) for effective commission management. Reltio’s Self- Learning Graph capability enabled the client uncover relationships among brokers, distribution channels, products, employers and insured employees to identify top brokers and find upsell and cross-sell opportunities.
A global colocation provider ensured the ‘Right to be Forgotten” for their customer contacts with an implementation that took twelve weeks. Reltio Cloud for Account 360 helped the client create a single view of their enterprise customers with built-in audit, data lineage, and workflows. It ensured ongoing purging of all traces by customer entity type in support of data erasure, including the removal of any attribute, historical activities made by individuals and activity logs on the Reltio Platform. In addition, Reltio Cloud’s Self-Learning Graph enabled them to reveal relationships among people, products, locations, and consent types.
Top Life Sciences Companies Share Their “Data Organization & Digital Transformation” Journey
The “Healthcare & Life Sciences (HCLS)” track at Modern Data Management Summit 2018 (#DataDriven18) invited industry leaders who shared their thoughts on themes such as data-driven strategies to excel commercial operations in life sciences, collaborative data management and curation across global regions, account-centric approaches, compliance initiatives, and omnichannel engagement with patients.
The Healthcare and Life Sciences track this year included twelve sessions and panels supported by 20+ speakers. Here are some of the key highlights and takeaways from the sessions:
Jamie Yates, Master Data Management Leader, AstraZeneca along with Mona Rakibe, Director Product Management, Reltio presented on AstraZeneca’s “Customer Master Journey: From Global To Local”. Jamie discussed the best practices employed in the AstraZeneca Global Customer Master deployment and provided insights into how modern data management capabilities such as collaborative data management and curation across geographies helped them break functional silos and cross-enterprise boundaries, thus leading to sales effectiveness, data steward productivity and overall data quality. Read more.
Peter Bell and team from Eli Lilly participated in a panel on “Improving Digital Customer Experiences at Eli Lilly”. They talked about how they leveraged modern data management technology to get rid of duplicate customer identities and create an enterprise-wide customer profile across commercial and clinical investigation departments. This helped them not only identify high-value customers but also improve customer experience across various digital touchpoints. Moreover, connected digital omni-channel systems allowed them continuously improve products, processes, information, and patient outcomes, resulting in increased sales. Watch the full panel discussion.
John Hayes and team from Sanofi along with Kanthan Manickam from ZS Associates discussed about how life sciences landscape has changed dramatically over the last decade, requiring companies to change the way they think about customers and develop new go-to-market strategies and commercial capabilities. They talked about how Customer 360 built on top of the Self-learning Data Platform supported them with account-centric approaches, digital marketing, compliance initiatives, and ingesting non-traditional data sources. Read more.
Vincent Ciro, Executive Director, Commercial Intelligence, Allergan and William Gurney from IQVIA talked about how modern data management helped them deliver global insights. Allergan was tasked with modernizing and standardizing their global business intelligence (BI) reporting platform. With disparate systems across 36 countries and 3 regions, in addition to mastering customer data, they had to manage transactional data, generate insight, offer data on-demand and support 24×7 business operations. Allergan utilized modern data management capabilities to achieve this ambitious goal and the solution went live in 16 weeks! Watch the full video presentation.
Vivian Wu and Bernie Tucker from AbbVie participated on a panel “Multi-domain Enterprise Data Management at AbbVie“. They discussed the evolving data strategy at AbbVie, various data management use cases, and lessons learned along the way. They talked about how they leveraged Self-learning Graph capabilities to uncover and manage relationships between HCOs and HCPs to support their compliance and account management initiatives. Watch the full panel discussion.
Nirav Mehta, Senior Director, Enterprise Architecture – Medical Devices IT, Johnson & Johnson and Ram Chakravarti, Associate Partner, McKinsey & Company shared their perspectives on a large-scale data-centric digital transformation. They emphasized the strategic importance of getting the data house in order as poor data can derail any business strategy and digital transformation initiative.
Maria Perkins and team from Optum had a panel discussion on “Data Strategies to Optimize Health Services” where they discussed how healthcare providers are leveraging modern data management to meet changing customer expectations while improving the internal business processes. Read more.
Beata Puncevic, SVP Engineering and Chris Williams, Data Engineering Leader, Healthgrades shared their Consumer 360 journey and how they built a data platform to meet their business needs, evolve rapidly to offer diverse set of products, and operate at large scale. Using modern data management capabilities, they were able to integrate consumer data from disparate data sources and link unknown with known to uncover consumer journey. They created single view of patients and their households, providers, and hospitals to create new offerings such as Consumer Data As a Service, Provider Data As a Service, and Facility Data As a Service. Watch the full video presentation.
Dr. Uli Chettipally, Co-founder & CTO of CREST Network, Kaiser Permanente, Anshul Agarwal, Principal, ZS Associates, and David Rosner, Principal, Deloitte participated on a panel “Will the Rise of the Patient Follow the Rise of the Consumer?”. Patients today expect the same experience as they get from a retailer or a bank and want to be more involved in their care. They expect all relevant parties like providers, payers and pharma to collaborate and recommend the best treatment options. Changing business models, customer expectations and newer regulations necessitate the adoption of Self-learning Data Platform to meet ever-increasing patient expectations serve them better. Watch the full panel discussion.
John Wollman, Chief Innovation Officer, HighPoint Solutions presented on “Applying Advanced Analytics to Detect Medicaid Overpayment”. Pharmaceutical manufacturers often overpay state Medicaid agencies for purchases in the form of duplicate discounts. This session examined the pervasive problem of duplicate discounts and identified how AI/ML and MDM can work seamlessly to enable new and advanced types of analyses such as duplicate discount detection.
The Modern Data Management Summit, 2018 provided an opportunity for healthcare and life sciences (HCLS) companies to discuss the latest trends in data organization, AI/ML, and self-learning and how these trends impact evolving business needs. It also helped them witness how companies in other industries (such as CPG, Retail, Media & Entertainment etc) are leveraging modern data management to stay ahead of the game.
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.
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.
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
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
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.
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.
When was the last time you tried to access information and ended up spending an hour looking through multiple systems to find it? Even worse, when was the last time your data failed to give you the right information, at the right time to help you do your job?
In observance of Friendship Day, we would like to point out how the qualities of a lasting friendship have much in common with the effects of well-managed data upon everyday life:
A friend is someone who is there for you when they rather be anywhere else
Just as you expect your good friends to be dependable, reliable data is non-negotiable. It’s table stakes for any company who is looking to be data-driven.
How about a healthcare organization with siloed, duplicated hospital and patient data? You could probably use some support for improving data management efficiencies, and omnichannel engagement with your patients.
These sound like problems only a real friend could understand.
I get by with a little help from my friends
A real friend is always willing to lend a helping hand. Good data behaves similarly by providing you with consistent and contextual information that helps you with your daily activities.
Reltio’s data-driven applications combines reliable data, relevant insights and recommended actions into cloud applications. These enterprise data-driven applications give business users a consumer-grade experience, like LinkedIn, Google and Facebook.
Tailored to your industry and functional role, these data-driven applications can help you identify and reveal relationships among the people, products, places and activities you care about. They can also guide you with powerful visuals and intelligent recommendations that help you make better decisions as you complete tasks.
Workflow and collaboration capabilities also allow you to manage, curate and provide real-time feedback on data quality. These out-of-the-box workflow processes accept input and requests from business users, without using email. They also improve the accuracy and effectiveness of information through social collaborative voting, ranking and rating.
Everyone is a friend, until they prove otherwise
Your true friends have likely proven themselves to be trustworthy. Putting your data in the cloud involves a high level of trust in your data management provider.
Companies, like Reltio have gained the trust of their customers, through earning HITRUST CSF Certification status, for information security by the Health Information Trust Alliance (HITRUST).
A friend is someone who knows the song in your heart, and can sing it back to you when you have forgotten the words
Good friends always provide you with insightful information that you may not have thought of. Reltio’s Commercial Graph is like that.
Architected for today’s agile enterprise, it is built with a columnar and graph hybrid store to combine, relate and store an infinite number of attributes and relationships. Created for the purpose of delivering any type of data-driven application, for any business need.
It also handles operational and analytical functions within the same data-driven application. Covering every possible scenario, while giving you the ability to imagine your data from any perspective.
True friendship is like sound health; the value of it is seldom known until it be lost
The irreplaceable value of a true friend is not always apparent. True friendships, like sound health, need maintenance and continuous improvement to endure.
Data quality works similarly. One cannot tell whether data is valuable until it is analyzed. Oftentimes, the more maintenance and improvements that are made to the data, the more valuable the insights become.
Rather than mourn over lost opportunities from poor data, make sure your data scientists and analytical platforms have access to reliable data. Reltio Insights enables advanced analytics with machine learning, so that you can access accurate information for faster results.
Unlike analytics-only tools, Reltio’s bidirectional connectors bring aggregated insights back to master data profiles. Offering valuable recommendations for business improvement to the users of data-driven applications. Thereby making your data as reliable, consistent, trustworthy, insightful and valuable as your best friend.
How to Drive Value-based Care with Modern Data Management
How to Drive Value-based Care with Modern Data Management
Health is a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity (World Health Organization, 1948). The pace of change in healthcare is accelerating with growing demands from patients, regulators and technology optimists. A paradigm shift in care delivery along with the new pay-for-performance model requires healthcare organizations to modernize their data management. The ability to understand the complex and dynamic relationships among providers, organizations, patients, pharma, payers, locations and health plans is critical for driving the right care.
Reliable data is at the heart of driving digital transformation in healthcare. The quality of data impacts every decision made along the patient’s healthcare journey. When data about patients, physicians, employees, caregivers and care locations is incomplete and fragmented, it is unthinkable to manage the relationships among all these participants of the healthcare ecosystem, and derive meaningful insights from their day-to-day interactions. Moreover, with ever-changing regulatory requirements, newer treatment discoveries and personalized care models, it is becoming far more challenging to keep healthcare data clean, complete and current. All of these factors lead to stale data and information latency, directly impacting patients’ lives and providers’ business sustainability.
Meeting today’s goals of patient-centricity, lowering readmission rates, and ensuring adherence to medication requires complete patient understanding. A Modern Data Management Platform helps blend all patient profile information, including EHR/EMR, lab results, omnichannel interactions and transaction and claims and reimbursement information into a single, easy to use data-driven application, helping organizations to provide better and personalized care. Not only does it help improve provider satisfaction by streamlining the claims payment process and identifying fraudulent claims, but it also enables segmentation of members and predictive modeling for personalized care and effective cost management. Moreover, its underlying graph technology provides new insights into patient’s support structure by unfolding patient’s relationships with caregivers, payers and family members.
Maintaining patient trust is the cornerstone for building a successful healthcare ecosystem. As patient privacy and compliance are of paramount concern, healthcare organizations need comprehensive auditing and tracking features to guarantee Health Insurance Portability and Accountability Act (HIPAA) compliance. Complete lineage and historical trail for any data matched, merged, or updated is required. A fine-grained clickstream analysis can be used to alert the admin about abnormal data viewing patterns by application users for possible information breach or theft. Thus, with the proper data protection strategies in place, providers can share sensitive patient data securely, within and across the organization.
Healthcare organizations are responsible for maintaining a patient-centric focus that ensures adherence to medication and treatment, timely and continued patient engagement to provide relevant health information and regular internal reporting on the quality and cost of the healthcare delivered. To make sure these objectives are met, reliable data and up-to-date information, through a Modern Data Management Platform, must be available at the fingertips of all the concerned stakeholders via data-driven applications, in a compliant fashion. Only then, will they be able to translate the vast volumes of data into a meaningful information asset that can be used to drive quality patient care.
All in all, a Modern Data Management Platform lays the foundation for achieving: Right Living,wherepatients are encouraged to play an active role in their own health by making the right choices about their lifestyle; Right Care,where patients receive the most timely, appropriate treatment available; Right Provider, where they have strong performance records and can achieve the best outcomes; Right Value,where providers and payers continually look for ways to improve value, while improving healthcare quality; and Right Innovation, where allstakeholders focus on identifying new therapies and approaches to healthcare delivery.
Top HCLS Organizations Leverage Modern Data Management for Realizing Their True 360 Vision
Top HCLS Organizations Leverage Modern Data Management for Realizing Their True 360 Vision
The healthcare and life sciences track at Modern Data Management Summit 2017 invited industry leaders who shared thoughts on contemporary industry themes such as data-driven strategies to improve commercial effectiveness in life sciences, democratization of master data management (MDM) helping pre-commercial companies with faster product launches, insights-driven patient engagement in healthcare and real-time data enrichment with data as a service.
Jamie Yates, Leader Master Data Management at AstraZeneca, discussed collaborative approaches for better data stewardship. It was interesting to learn how AstraZeneca allowed its field sales representatives act as data stewards and granted them direct involvement into the Health Care Master. Reltio Cloud’s robust configurability with no need for tedious customizations and ease of use (UI inspired by consumer data-driven applications, such as LinkedIn and Facebook) made it possible.
Maria Perkins, Vice President, Growth Technologies & Proposals at Optum, led a panel on “Using Data to Drive Optimal Sales of Health Services.” She and her team at Optum discussed how they had an extremely rigid and expensive legacy MDM and didn’t have a true Account 360 view of their complex and diverse B2B customers. To realize the benefits of cloud computing in the MDM space, Optum chose to partner with Reltio rather than going with a legacy MDM vendor. Reltio Cloud helped Optum understand the needs of diverse accounts, offer customized products and services, integrate unlimited number of data sources and do white space analysis. Their sales operations team also expressed enthusiasm over leveraging Reltio Product 360 for a single view of their complex product line that resulted from a series of acquisitions and diverse product offerings for providers, payers, prescribers, patients and so on.
Abhi Parab, Managing Director at PCGI Systems, presented a biopharma case study illustrating data-driven strategies to improve the quality of business insights and effectiveness of commercial processes. What their client needed was to resolve complex customer hierarchies and relationships, and integrate changing third party and internal data sets to deliver a true customer 360 view to the right business units and data stewards. He discussed how their client used Reltio Cloud to address these challenges by leveraging its commercial graph, data as a service (DaaS) and integrated workflow capabilities.
Eric Letts, VP Master Data Management at HighPoint Solutions, talked about the democratization of MDM in life sciences (across different departments and systems e.g. CRM, sales order management, contracts and pricing and R&D) and how Reltio Cloud played an important role in breaking down these system silos. He further discussed how Reltio Cloud’s capabilities, such as solid starting party model (not starting from scratch), quick configuration and prototyping and user-friendly interface enabled many of their biopharma clients to build out new commercialization capabilities in much less time as compared with traditional MDM solutions that may take several months or even years.
John Busalacchi, Center of Excellence Lead – Information Management at QuintilesIMS, provided valuable insights into the evolution of enterprise information management (EIM) in the life sciences industry and how it would influence the future of customer engagement in healthcare. He emphasized the need for actionable insights in operational as well as analytical applications, and the importance for having a current golden record of all customers at all times. Once a reliable data foundation is established with continuous data quality management, marketers can confidently use data-driven applications to segment customers, based not only on channel and message preferences, but any attribute available.
Philippe De Smedt, Principal Architect at Kaiser Permanente, spoke about best practices and approaches towards innovative data governance and how to translate challenges, such as the proliferation of data from multiple heterogeneous data sources and disintegrated cross-domain and unstructured data to opportunities. He ran through his work in developing the three key elements to effective data governance – business drives it, IT enables and supports it, executive management sponsors it.
The first-ever Modern Data Management Summit (2017) provided a platform for healthcare and life sciences (HCLS) organizations to discuss the latest trends in data management and how these trends impact evolving business needs. It also helped them witness how companies in other industries are leveraging modern data management to stay ahead of the competition. If you could not make it this year, click the button below to request recordings of the selected sessions from DataDriven17. We look forward to seeing you next year at DataDriven18!
The big data conundrum is one that bedevils most industries, but none more than life sciences. Because of the high stakes of healthcare, there is a great responsibility to get things right, and to pursue continual improvement, ideally with the proper use and analysis of data. In addition, life sciences companies are under more scrutiny and regulation than ever. With more data to collect than any other industry: disease states, scientific studies, individual patient info, clinical results and more, all of that data needs to be turned into actionable information in an increasingly complex digital world.
Undertaking a big data strategy really means that a company is ready to become more “data-driven.” Simply put, that means using more sources of data in order to gain relevant insights to make better decisions and take actions that yield better outcomes. Most successful companies are right more often than not – that’s why they are able to thrive and have a growing business. However, the competitive and regulatory landscape dictates that companies need to “be right faster” and a strategy that incorporates big data requires a serious look at their data management technologies, practices and how they can truely leverage new sources of data.
Many companies think big data is a research project, and mistakenly spawn or create a team tasked with testing and evaluating the latest “free” open source technologies such as Hadoop. It’s a cliche, but you can’t select a big data technology, then find a problem to solve. To be successful you must be aligned to a business problem that needs to be addressed. If that problem has a hard deadline, even better! Nothing spells focus like working towards a milestone, and the expectations of frontline business teams that want value immediately. Fortunately with the right modern data management platforms, many business challenges can be solved in weeks, not months or years.
Data governance and stewardship for example is a must. Most companies forget the discipline of trusted, secure and reliable data when they embark upon their big data strategy. The refrain of “the type of data we are capturing in big data projects don’t need governance” is a slippery slope. Big data lakes will turn into big data swamps if rigor, process and data quality are not applied. Worse, the insights that are derived from unreliable data are worth less than having no data at all.
At Reltio, we’ve seen our customers use data from traditional third-party vendors, and also bringing together public data sources from CMS.gov, Pubmed, clinicaltrials.gov, as well as social media data from Linkedin and Facebook. IoT (internet of things) data, which has the extreme big data volumes at velocity has so far been less of a concern for life sciences companies. In healthcare, health monitoring devices such as the Apple Watch will start to deliver information for physicians from patients that may eventually become part of their care. The trick is to bring capabilities together in a single platform, where data can be correlated, made reliable and for insights to be derived.
For the most part companies have been “playing” with big data technologies, using Hadoop, NoSQL databases, data scientist visualization tools. A lot, and I mean a lot, of money has been spent on pilots and trials. While there have been some successes, for the most part many companies are still immature in their use of these technologies. There are many reasons for this including the IT skills and expertise required to implement new big data tools, and the complexity of integrating data with traditional approaches and applications. Without a singular focus on the desired business outcome, and actual data-driven business applications that are mobile, collaborative and easy to use by frontline sales, marketing and compliance teams, companies will continue to see limited success with big data.
Insights gained run the gamut across healthcare and life sciences and include true 360-degree views and inter-relationships between HCP, HCOs, IDNs, ACOs, MCOs, plans, payers, products, patients and all of their interactions. There are many macro-level conclusions that can be drawn about overall operating efficiency (in the case of commercial operations), and additional data for clinical trials (in the case of R&D). But ultimately the insights derived are only relevant to what can be done with them, and that use is relative to the role and business goals of each user.
For all of the data management technology and visualization tools invested in bringing together and processing big data, companies are typically left to their own devices, to draw their own conclusions from the insights, and then to act upon them. New data- driven applications are able to synthesize that information and provide suggestions or recommended actions to the frontline business users that are actionable occur daily in the consumer world. Take LinkedIn for example. It brings together vast quantities of data, and delivers suggestions to you. LinkedIn suggests jobs that are relevant to you and your experience. It doesn’t just say here’s a pool of jobs, and makes you go filter and search for the ones that are relevant to you. It understands complex connections and relationships, and shows you the best path to connect to people you don’t know. Business teams, such as sales and account managers in life sciences, need similar help in their day-to-day operations. But they are saddled with legacy CRM and process-driven applications that capture data, but do not offer recommendations and suggestions gleaned from processing large amounts of data and relating them together.
In another simple example, a data-driven application for a pharma sales rep should provide a recommended best path to connect with a key influencer in a formulary committee. Or it might guide a marketing professional to the best candidates for key opinion leaders (KOL) for events. As data-driven applications become more mainstream in our everyday lives as consumers, business users are coming to expect the same degree of capabilities in their day-to-day applications.
Contrary to popular belief, big data is more than just about size. We’ve all heard about the 3 Vs of volume, velocity and variety, but one key “V” not discussed often enough is veracity. Simply put, that means data quality. Data that is not cleansed and continuously managed cannot be related together for insights. For people, not seeing data in a shared central pool is often a problem. Siloed data, no matter the size, causes issues. Different perspectives of the same customer, product or organization mean collaboration is not possible. Shared insight is as valuable as insight derived from the volume of big data that is now available. From a process perspective, companies need to manage and secure their new-found big data. Having valuable insights is competitive advantage. Many companies simply do not have the compliance and regulatory controls to protect their own data, or meet mandated guidelines such as HIPAA.
In conclusion whether Big Data in life sciences is a blessing or curse depends on whether the organization repeats the same mistakes, or proceeds down the wrong path to obtain much desired insight. Mistakes to avoid include:
Not ensuring the data is reliable as a foundation. Either ignoring it or making it someone else’s responsibility. This is why master data management (MDM) is a siloed billion dollar industry that hasn’t yielded expected results. Most new data-driven apps have MDM built-in
Using visualization tools and business intelligence to analyze big data to derive one-time high-level macro insights, but not having an integrated strategy or technology to execute on those insights
Forgetting about the end business user. It’s great to get lots of data and process in, but time to value and putting it into the hands of the business user in mobile, easy to use applications is often last on the list, when it should be the first
Gathering all the data they can, just because they can. Relevant data and insights that yield recommended actions don’t mean capturing the entire universe. A data sourcing plan is critical to determining what data is relevant and how you are going to leverage it. It’s okay to start small, then increase to big data volumes. A modern data management platform offers the ability to incrementally add data sources, without having to re-architect and start over.
Not closing the loop or measuring the benefits once you obtain insights, and take action. Continuous monitoring and correlation of insights to action to measure ROI and to allow machine learning systems to use historical context to predict trends is one of the biggest gaps in siloed, disparate tools today. Modern data management platforms provide a complete integrated loop that delivers reliable data, relevant insights and recommended actions that support IT and deliver data-driven applications to business users.
Deliver personalized customer experiences with an agile, scalable and smart MDM.
Whether you’re a CIO in the Pharmaceutical Industry, Chief Customer Officer in Retail, or CMO in Healthcare, you’re trying to better understand your customers and deliver exceptional customer experiences at every touchpoint.
Innovative Global 2000 companies across industries are using Reltio Cloud to better understand their customers, personalize customer experiences, and accelerate their digital transformations.
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