Social Media, mHealth, Customer Insights, GoToMarket Strategies
With moderator MEGHAN LOPRESTO, Director, Customer Engagement Strategy, Boehringer Ingleheim
Our Panel of Experts:
Chief Marketing Officer, Reltio
Head of Big Data Strategy & Solutions, Cognizant
Digital and Data Strategy Lead, The Stem
Partner & Life Sciences Information Management Practice Leader, PwC
The big data conundrum is one that bedevils most industries these days, but none more than life sciences. We have a great responsibility to the health of the nation to get things right, and to pursue continual improvement, two goals that are assisted by the proper use and analysis of data. In addition, we are under more scrutiny and regulation than most, making the task even more complicated. Finally, we probably have more data to collect than any other industry: disease states, scientific studies, individual patient info, clinical results and more. Then we need to turn all that into actionable information in an increasingly complex digital world. That’s why we sought out people who can help us clarify our thinking about big data. Readers of Healthcare Sales & Marketing are not typically IT execs, so we’ve tried to frame this in a way that helps you in your particular situations. We wanted it technical enough to explain what some of the challenges and solutions are, but down-to-earth enough that you don’t need a Stanford degree to understand the content. I hope we have accomplished both.
What does it mean to have a “big data” strategy?
ONEIL REGO: Organizations sometimes get caught up in the words “big data,” but at the end of the day, it’s about having the right data. Instead of thinking about it as purely integrating large data sets, realize that it’s also about using information assets to answer some of the most pressing questions the business is trying to answer, and being able to analyze and assess the data to see if it is indeed a good fit to answer those questions.
IDO HADARI: True. Big data is a means to a goal. The goal is, in most cases, a better understanding of key elements in your market. This new means is essentially having the opportunity to gain a different understanding and fresh insights from the aggregation and analysis of massive amounts of data, previously inaccessible or difficult to uncover. The healthcare ecosystem has been generating different types of such massive datasets – genomic data, population-level data, clinical data and social health data to name a few – that potentially hold key insights and solutions to existing challenges. A big data strategy means that you have recognized the importance of these to your business, such as incorporating in-depth real-time insights from the billions of conversations taking place in the online universe (e.g. social networks, communities, blogs, forums, etc.) about patient and caregiver behavior, attitudes and decision-making relative to therapeutic areas, brands, treatment journey etc. in order to inform business strategy and on-going execution tactics.
GUSTAVO DELEON: A successful big data strategy is an extension of the corporation’s goals and objectives. This requires C-suite understanding of what the impact is of big data and the potential application of this technology to very specific business challenges. So there is typically an education process to increase executives’ awareness. The development of a big data strategy should be a fully-integrated business and IT joint venture. The real strategy for big data is to take the best of the new technologies that apply directly to a company’s business problems and embark on creating a solution.
DERRICK HUANG: As organizations develop their big data plans, they often jump too quickly to action and focus on how to integrate the myriad data sources they have at their disposal (customer info, transactions, internal inventory/operations, social media, etc) rather than investing upfront time to determine the right big data approach given their current business challenges. For example, if you have a high volume, commoditized product that is facing an inventory obsolescence problem, you should be focusing on internal operational and transactional data. On the other hand, if you’re dealing with surgical equipment that involves cross-selling consumables and upselling to other products, customer data and external market data (including competitive intelligence) is much more relevant. It’s important to pinpoint the problem and then focus on the right data sets that will give you the insights you’re looking for.
RAMON CHEN: 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 incorporate new sources of data.
How do you focus on the challenge you want to solve?
GUSTAVO DELEON: Our successful clients stay focused through clearly defined business use cases. A business case has three critical components: an identified business owner, a detailed statement of what is desired as an outcome, and the expected benefit – financial or otherwise. With a well-defined use case you can quickly compare new technology or tools to well understand business needs. If the technology doesn’t directly support your use case, drop it and move on.
RAMON CHEN: Many companies think big data is a research project, and mistakenly spawn off 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 find 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 team want value immediately. Using the right modern data management platforms, many business challenges can be solved in weeks, not months or years.
ONEIL REGO: It’s important to understand the depth of the question and to create the right pilots to analyze information assets. Fine-tune both the answers and the questions – oftentimes you want to know if the questions were correct to begin with. Remember that you are fundamentally changing the way people think from standard, classic reporting to using information assets to find the right answers and solutions in a sea of data. There’s a cultural shift that needs to happen, and that takes time.
IDO HADARI: Generating the voice of health consumers based on billions of online conversations across the web is a significant challenge. Add to the mix the broad set of topics within healthcare and the use of day-to-day human language, and the task of extracting meaningful insights becomes ten times more challenging. Our approach is to create dedicated, life sciences specific technology that helps in the collection, storage, analysis, organization and the “connection of the dots” on all of these online conversations in a meaningful way. We have created health-specific natural language processing algorithms and extensive medical dictionaries and patient language databases to draw connections, identify interrelationships and deliver sophisticated social health analysis.
What’s a good approach to data governance and stewardship? Should you have a stewardship strategy or program in place before you start?
ONEIL REGO: You have to understand what you’re going to store and what needs to be governed so you can make conscious decisions about the governed assets and set up the right constructs and processes for the governed attributes that need to come together. With the big data play, there’s the new role of the Chief Data Officer (CDO). Whether the CDO will become part of the executive governance committee or the sponsor of the program is yet to be seen. The questions around a CDO’s role are becoming increasingly important to answer.
RAMON CHEN: Data governance and stewardship is a must. Most companies forget the discipline of trusted, secure and reliable data when they embark upon their big data strategy. You hear justification such as “the type of data we are capturing in big data projects don’t need governance.” This is a slippery slope. Big data lakes will soon 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.
GUSTAVO DELEON: In a big data development and production process governance and stewardship must be elevated into the actual application develop process. The two primary goals being to document the “lineage” of each data element – where did the data come from, what transformations have been performed – and “suitability of purpose.” For example, a single data set may be a perfect match for a web application where the end user is more interested in functionality and less interested in absolute accuracy of the underlying data. For the same data set, the lack of accuracy may make it unsuitable for use by other applications – accounting, forecasting & supply chain. In a big data reality the focus of governance should be around data lineage and suitability of purpose.
What new data sources have you used as part of a big data program? Were they valuable?
IDO HADARI: Treato has expanded and diversified its data sources from just healthcare specific communities and discussion boards to social networks (e.g. Face-book, Twitter, Reddit) and other new media types (e.g. comment boards), which often times reflect discussion trajectory and velocity that complement the deep insights available through more focused community discussions.
GUSTAVO DELEON: Social media has had an amazing impact on the value of analytics from big data. The ability to understand the changing sentiment for a brand, product or service in near real-time has transformed entire corporate functions from sales & marketing to supply chain. While big data does provide enormous horsepower to drive traditional analytics – cross sell/up sell, market basket, etc. – being able to detect and react to sentiment changes as these events occur drives new value opportunities that have never before existed.
ONEIL REGO: In pharma, we’re starting to see a proliferation of using unstructured and third-party data. Ironically, in today’s world, for example, some labs are not using electronic lab notebooks and a level of unstructured data is produced that requires natural language processing to integrate successfully. Digitization is happening. Technology is on the forefront of being able to do a lot of interesting things with these large sets of data.
RAMON CHEN: At Reltio, we’ve seen our customers use data from traditional 3rd 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.
What new “big data” technologies have been utilized? Did they make a difference?
GUSTAVO DELEON: When most people say of “big data” they are really saying “Hadoop.” However there are other big data technologies making a clear impact across businesses. For example, MongoDB, with its flexible data structures, is dominating the web application business as the big data database of choice. Another example is Cassandra’s role as a high-performance, highly reliable transaction database based on big data technologies. These are two examples that scale horizontally – to process more data faster, simply add more machines/nodes without having to change your application. This scalability has opened the possibility of advanced analytics at scale (across all your data) in seconds.
IDO HADARI: Treato was one of the first health IT startups to adopt the Hadoop framework to store and process big data. Hadoop allows us to identify, collect, analyze, index and aggregate our mass amounts of user generated content. Hadoop also enables us to address scalability issues as we work around the clock collecting billions of patient and caregiver conversations in real-time. By managing our data with Hadoop we are able to provide insights into every condition and medication used that has been discussed online.
RAMON CHEN: 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.
What new insights into your customers are typically derived from data analysis?
IDO HADARI: Big data analytics allow pharma marketers to understand patients’ fears, perceptions and attitudes as they make their way from coping with symptoms, understanding their diagnosis and considering and complying with treatment. We have worked with healthcare marketers on topics ranging from the quality of life for people suffering from rheumatoid arthritis to the factors that influence women’s breast cancer treatment decisions.
ONEIL REGO: If you look at reasons why a patient has gaps in between infusion sessions, you may find that it’s because they didn’t have a means of transportation to the location – and that it may not be about access to a healthcare plan or appropriate coverage. By looking at these variables and patterns, you can get a view across not only a patient’s social and demographic information, but also details that affect their daily life to in turn help you better serve those patients.
DERRICK HUANG: Rather than “new” insights, data analysis often allows for earlier detection and improved precision and accuracy into existing insights. In the hospital setting, for example, being able to anticipate and manage readmissions and adverse events are key to delivering high quality care and saving costs. Pairing real-time EMR data containing unstructured physician notes with advanced data analytics based on NLP has enabled us to predict these events earlier than ever with a high degree of precision and accuracy.
RAMON CHEN: 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.
How do you act on those insights? What go to market processes are impacted?
RAMON CHEN: 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. This is already happening 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, you go filter and search for the ones that are relevant to you. It also 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, need similar help in their day-to-day operations. But for the most part they are saddled legacy CRM 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 a simple example, a data-driven application for a pharma sales rep should recommend the best path to connect with a key influencer in a formulary committee. Or guide a marketing professional to the best candidates for key opinion leaders 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.
DERRICK HUANG: Insights become actionable when they are integrated into the right processes such that they are delivered to the right people at the right time. In the case of a readmissions or adverse event risk score, for example, the target would be the attending physician or nurse at all the appropriate decision points during a patient’s care. In the case of customer behavior, the insights could be appropriate for a sales rep—both before actual customer engagement (to develop the right sales strategy) as well as during the actual customer interaction (real-time decision support).
While certain go-to-market processes seem be targeted more frequently than others (e.g., improving the effectiveness of sales and marketing campaigns), big data insights are applicable throughout an organization and are often the most effective at improving internal operational processes rather than customer facing ones.
IDO HADARI: Big data insights can impact all aspects of a business. It can start with an environmental analysis to inform unmet needs of patients and potential new formulations as well as competitive intelligence. To the formation of a pre- and post-product launch strategy by examining real-world, real-patient journeys in which marketing decisions are impacted such as target segments to focus on, communications channels to reach these segments, messaging and positioning, identification and adjustment of access-related issues, identification of critical knowledge gaps, physician interaction failure points, unearthing growth opportunities, and more.
What are the challenges you experience with respect to people, process and technology?
ONEIL REGO: The first challenge is to realize that data-driven decision-making doesn’t come naturally to people – we’re wired to make gut decisions. And in the healthcare space, you’re also dealing with expert bias, where end users feel they already have the answers and don’t need the data to help them make decisions. To think people will adopt data-driven decision making with open hands is naive. The second challenge is from inflexible processes and technologies. Most technology and processes are designed to get a specific task done, not to take insights and tailor actions accordingly. But if you’re utilizing an insight, that’s exactly what you need to do. When you start to go down the path of big data analytics, it usually takes 4-6 months or more to get people to change their behaviors. Getting the change and cohesion around ideas and actions is a bigger challenge than the technology piece.
GUSTAVO DELEON: The challenge is getting the corporate and IT leadership to accept that people, process and technology need to change at the same time. Companies are searching for competitive advantage and a faster time to market via the new flood of big data. It is no longer a matter of what is the best technology. It requires a strategy that links a business use case to necessary changes/adaptations of business processes, the roles of employees and, finally, technology. We live in the world of Uber, AirBnB, Amazon, et. al. – a time of great disruption of the status quo in the way products and servers are delivered to consumers. The catalyst behind this is not technology, but a united people, process and technology strategy that starts with the user experience, the processes to serve that experience and the technology to support a new way of operating – disruption.
IDO HADARI: Access to such new insights may be ahead of the curve for some teams, and requires a significant change management process, work processes, methodologies and thinking in order to fully utilize the immense potential value. One example we have seen is the slow yet steady shift from learning about consumer behavior, perceptions and decision-making through third party reports that are focused on specific research questions and take months to gather. As opposed to utilizing real-time platforms, often supported by an expert data analytics team, which brings the real world into the board room and the patient into the decision-making process almost instantaneously.
RAMON CHEN: 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 aspect not often discussed 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.
DERRICK HUANG: In some companies, too much emphasis is put on technology and not enough on people and process. In developing big data capabilities, it’s not uncommon for companies to put 90+ percent of their investment in data and modeling with organizational structure and skills as an afterthought. The reality is that in many respects, big data tools and technologies have become commoditized. The real “special sauce” in big data is to ensure that your organization has the right skills in place to implement a holistic big data strategy. This means hiring (or training) the right data scientists—the ones who really understand how to evaluate data sets, manipulate them, and turn them into something useful—and coupling them with the right strategic and operational leaders so that big data becomes truly integrated into the organization’s culture and workflow.
What are some of the mistakes you’ve seen people make in their approach to big data?
IDO HADARI: The biggest mistake we are seeing is lack of knowledge or strategy that result in organizations not adopting big data approaches to enhance their businesses. It is the equivalent of trying to cross the ocean with a canoe while having a brand new jet parked outside – not only faster and more effective but different, and that difference is the key to unlocking the future of healthcare.
DERRICK HUANG: Perhaps one of the bigger mistakes is ignoring organizational buy-in as part of the big data equation. Here’s an obvious statement: big data insights create business value only when they are actually used. All too often, though, the needs and perspectives of the frontline workers who are expected to use these insights are ignored. The tools that deliver these insights are often too complex to be useful or not well integrated into the workflow, leading to low adoption. It’s also important that the frontline understands how the models work—they can’t be expected to make important business decisions based on blind faith in a blackbox mathematical model. To obtain buy-in, an organization needs to ensure that its data analytics insights are being delivered through easy-to-use tools and that the frontline workers have been trained to understand what the insights mean. This allows the frontline to then seamlessly combine their own human experience and judgment with the output of mathematical models to make the best decision possible.
GUSTAVO DELEON: Our experience with dozens of customers has shown us many missteps along the big data journey.
• “We will build a data lake and load all our data in a single repository, making it available to everyone for advanced analytics.” This ignores the garbage in/garbage out principle of computer science. And it can quickly evolve in to a massive undertaking that, in the end, creates a new data silo of limited value to the corporation
• “IT will decide the big data strategy.” The business has to be at least an equal partner. Optimally the business should lead the creation and implementation of a big data strategy
• “If we build it (a Hadoop data lake), they will come and use it.” Without business use cases driving such an effort, the end result will have the business users saying “I never asked you to build that.”
• “Let’s do a Proof of Concept with big data technology and take that to production as our first steps into big data.” A Proof of Concept (POC) is just that: a “proof ” or hypothesis. POCs are necessary tools for learning, planning and building roadmaps. They are not foundational. They should not be used to substantiate a big data architecture
• 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.
MEGHAN LOPRESTO: Thanks to everyone who spent the time to share your expertise and help us novices move forward on the path to intelligent and productive use of big data. From my own understanding of your contributions, I think this is a great roadmap to helping us serve the health of our nation in the way that we all want to. •
MEET OUR MODERATOR
Director, Customer Engagement Strategy, Boehringer Ingleheim
Meghan has over 14 years of experience in the pharmaceutical industry in leadership roles focusing on multichannel marketing, customer engagement strategy, digital innovation, business development, account leadership, and sales operations. She has been with Omnicare, Forest Laboratories, Sepracor/ Sunovion, Publicis Strategic Solutions Group, The CementBloc, and, most recently, Boehringer Ingelheim. Well known as an industry expert in sales and marketing strategies, Meghan has worked with sales, marketing and operations teams to develop and implement a variety of successful and creative initiatives and brand strategies for products from the pre-launch phase to maturity, and, while at CementBloc, worked in consultive roles with companies in the pharmaceutical, biotech and medical device industries to provide strategic support and help drive their sales and marketing initiatives. Meghan was honored as an HBA Woman of the Year Rising Star in 2012, was granted a Marketing Innovation Award at BI in 2013, and in 2015 was named a PM360 ELITE Transformational Leader. She is now responsible for leading the team driving customer engagement strategy, innovation, and multichannel promotion for the prescription medicine franchises at Boehringer Ingelheim.
The Boehringer Ingelheim group is one of the world’s 20 leading pharmaceutical companies. Headquartered in Ingelheim, Germany, Boehringer Ingelheim operates globally with 146 affiliates and a total of more than 47,700 employees. The focus of the family-owned company, founded in 1885, is researching, developing, manufacturing and marketing new medications of high therapeutic value for human and veterinary medicine. Social responsibility is an important element of the corporate culture at Boehringer Ingelheim. This includes worldwide involvement in social projects, such as the initiative “Making more Health” and caring for the employees. Respect, equal opportunities and reconciling career and family form the foundation of the mutual cooperation. In everything it does, the company focuses on environmental protection and sustainability. In 2014, Boehringer Ingelheim achieved net sales of about 13.3 billion euros. R&D expenditure corresponds to 19.9 per cent of its net sales.
MEET OUR EXECUTIVE PANEL:
Chief Marketing Officer, Reltio
Ramon is responsible for worldwide marketing and product management at Reltio. Prior to Reltio, he was VP of Product Marketing for Commercial, which encompassed Veeva CRM, Veeva Approved Email, Veeva Vault and Veeva Network at Veeva Systems. He has over 25 years of experience running marketing and product management teams and is a regular author and speaker on the topic of big data, databases, data-driven applications and modern data management.
Reltio delivers reliable data, relevant insights and recommended actions so companies can be right faster. Reltio Cloud combines data-driven applications with modern data management for better planning, customer engagement and risk management. Reltio enables IT to streamline data management for a complete view across all sources and formats at scale, while sales, marketing and compliance teams use data-driven applications to predict, collaborate and respond to opportunities in real-time. email@example.com
GUSTAVO DE LEON
Head of Big Data Strategy & Solutions, Cognizant
Gustavo is a big data evangelist and solution architect working with Fortune 1000 companies to realize the benefits of big data analytics. An innovation executive creating go-to-market solutions using big data technologies, and trusted advisor to business and IT executives to realize actionable big data strategy and solutions with a measurable ROI, Gustavo has worked in the information management space for over 20 years.
Cognizant Technology Solutions, headquartered in Teaneck, New Jersey, is a consulting and IT services firm which combines a passion for client satisfaction, technology innovation, deep industry and business process expertise and a global, collaborative workforce that embodies the future of work. With over 100 delivery centers worldwide and approximately 218,000 employees as of June 2015, Cognizant is a member of the NASDAQ-100, the S&P 500, the Forbes Global 2000 and the Fortune 500 and is ranked among the top performing and fastest growing companies in the world. Gustavo.DeLeon@cognizant.com
Ido is an evangelist for the role consumers are driving in the new healthcare economy. A frequently featured industry speaker and contributor, he’s an advocate of the transparency and disruption that information technologies such as big data analytics and social data are bringing to health consumers. Prior to Treato, Ido was CEO of Commtouch Software, a cloud-based Internet security solution provider. Treato is the leading source of real health insights from millions of real health consumers, using patented analytics and big data technology to turn billions of disparate online conversations into meaningful social intelligence. With two billion posts analyzed and continuously expanding, Treato has partnered with nine out of the world’s top ten pharma companies, as well as numerous other multi-national pharmaceutical companies and healthcare organizations. Treato.com, its consumer website, helps millions of visitors each month. Treato is privately held, with offices in Israel, New York and Princeton, NJ. firstname.lastname@example.org
Digital and Data Strategy Lead, The Stem
Derrick has extensive experience serving clients across the healthcare landscape – pharma, insurance, and hospital systems. Derrick has advised both large companies and startups on a variety of topics including growth strategy, lean operations, sales force effectiveness, data analytics and digital/mobile strategy. Derrick was formerly a consultant at McKinsey & Company, leading initiatives on competitive intelligence, strategic-decision making, and war gaming. Derrick began his career as a technologist at various Internet startups and technology firms, designing and developing software, leading projects teams, and selling software products and services.
The Stem enables health companies to maximize their return on digital and customer experience investments through a “networked consulting” model that draws on the industry’s leading independent talent. email@example.com
Partner & Life Sciences Information Management Practice Leader
For more than 20 years, Oneil has been a leader and practice builder focused on the sales and delivery of client engagements, primarily within the life sciences domain. He has proven success building relationships with senior life sciences clients and expanding those with existing clients both from a business and an IT perspective. He has previously held positions at Deloitte Consuting, ZACSC and Tata Consulting Services.
PwC US is committed to delivering quality in assurance, tax and advisory services. It also helps companies develop information management strategies, architectures and governance frameworks that help transform data into insight and action, enabling them to better manage the complexity of data and enterprise content in order to reduce costs and ensure the integrity of information assets. With business intelligence and applied analytics, PwC shows organizations how to better anticipate and manage risks and enhance performance through better information, greater insight and refined decision-making capabilities. firstname.lastname@example.org