3 items tagged "R&D "

  • Data, Analytics & Fuel Innovation at Celgene

    Williams-Richard-CelgeneCIO Richard Williams leads a global IT organization that’s harnessing digital, data, and analytics to support R&D innovation, drive operational excellence, and help Celgene achieve first-mover advantage in the shift to value-based, personalized health care intended to help patients live longer and healthier lives.
    An explosion of electronic health information is rocking the entire health care ecosystem, threatening to transform or disrupt every aspect of the industry. In the biopharmaceutical sector, that includes everything from the way breakthrough scientific innovations and insights occur to clinical development, regulatory approvals, and reimbursement for innovations. Celgene, the $11 billion integrated global biopharmaceutical company, is no exception.
    Indeed, Celgene, whose mission is to discover, develop, and commercialize innovative therapies for the treatment of cancer, immune-inflammatory, and other diseases, is aggressively working to leverage the information being generated across the health care system, applying advanced analytics to derive insights that power its core business and the functions that surround and support it. Long known for its commitment to external scientific collaboration as a source of innovation, Celgene is investing to harness not only the data it generates across the enterprise, but also the real-world health care data generated by its expanding network of partners. Combined, this network of networks is powering tremendous value.
    CIO Richard Williams sees his mission—and that of the IT organization he leads—as providing the platforms, data management, and analytics capabilities to support Celgene through the broader industry transition to value-based, personalized health care. At Celgene, this transformation is enabled by a focus on the seamless integration of information and technology. A cloud-first platform strategy, coupled with enterprise information management, serves as the foundation for leveraging the data generated and the corresponding insights from internal and external health care data.
    Williams recently shared his perspective on the changes wrought by enormous data volumes in health care, the role of IT at Celgene, and the ways IT supports life sciences innovation.
    Can you describe the environment in which Celgene is currently operating?
    Williams: We are living in an exciting era of scientific breakthroughs coupled with technology convergence. This creates both disruption and opportunity. The explosion and availability of data, the cloud, analytics, mobility, artificial intelligence, cognitive computing, and other technologies are accelerating data collection and insight generation, opening new pathways for collaboration and innovation. At Celgene, we’re able to apply technology as never before—in protein homeostasis, epigenetics, immuno-oncology, immuno-inflammation, informatics, and other fields of study—to better understand disease and develop targeted therapies and treatments for people who desperately need them.
    How does IT support scientific and business innovation at Celgene?
    At its core, Celgene IT is business aligned and value focused. Rather than looking at technology for technology’s sake, we view information and technology as essential to achieving our mission and business objectives. As an integrated function, we have end-to-end visibility across the value chain. This enables us to identify opportunities to leverage technology investments to connect processes and platforms across all functions. As a result, we’re able to support improvements in R&D productivity, product launch effectiveness, and overall operational excellence.
    This joint emphasis on business alignment and business value, which informs everything we do, is manifest in three important ways:
    First is our emphasis on a core set of enterprise platforms, which enable us to provide end-to-end visibility rather than a narrower functional view. We established a dual information- and cloud-first strategy to provide more comprehensive platforms of capabilities that can be shared across Celgene’s businesses. The cloud—especially with recent advances in security and analytics—provides tremendous scale, agility, and value because it allows us to standardize and create both consistency and agility across the entire organization regardless of device or access method. It’s our first choice for applications, compute power, and storage.
    Second is our focus on digital and the proliferation of patient, consumer, scientific, and it is creating. Health care data is growing exponentially—from something like 500 petabytes (PB) of data in 2013 to 25,000 PB by 2020, according to one study.
    To address this opportunity, we’ve initiated an enterprise information management (EIM) strategy through which we are targeting important data domains across our business and applying definitions, standards, taxonomies, and governance to data we capture internally and from our external partners. Establishing that consistency is critically important. It drives not only innovation, but also insight into our science, operations, and, ultimately, patient outcomes. Celgene is at the forefront in leveraging technologies that offer on-demand compute and analytic services. By establishing data consistency and influencing and setting standards, we will support our own objectives while also benefiting the broader industry.
    Third is our support for collaboration—the network of networks—and the appropriate sharing of information across organizational boundaries. We want to harness the capabilities and data assets of our partners to generate insights that improve our science and our ability to get better therapies to patients faster. Celgene is well-known in the industry for external innovation—how we partner scientifically—and we are now extending this approach to data and technology collaboration. One recent example is our alliance with Medidata Solutions, whose Clinical Cloud will serve as our enterprise technology and data platform for Celgene clinical trials worldwide. Celgene is also a founding commercial member of the Oncology Research Information Exchange Network, a collaboration of cancer centers spearheaded by M2Gen, a health informatics solution company. And we have teamed with ConvergeHEALTH by Deloitte and several other organizations for advanced analytics around real-world evidence and knowledge management, which will also be integrated into our data platform.
    You’re building this network-enabled, data-rich environment. But are your users prepared to take advantage of it?
    That’s an important aspect of the transformation and disruption taking place across multiple industries. Sure, IT can make information, technology, and insights available for improved decision-making, but the growing complexity of the data—whether it’s molecular structures, genomics, electronic medical records, or payment information—demands different skill sets.
    Data scientists are in high demand. We need to embed individuals with those specialized skills in functions from R&D to supply chain and commercial. At the same time, many more roles will require analytics acumen as part of the basic job description.
    As you build out your platform and data strategies, are you likely to extend those to your external alliances and partners?
    External collaboration enabled by shared data and analytics platforms is absolutely part of our collaboration strategy. If our informatics platforms can help our academic or commercial biotech collaborators advance the pace of their scientific evaluations, clinical studies, and commercialization, or they can help us with ours, that’s a win-win situation—and a differentiator for Celgene. We are already collaborating with Sage Bionetworks, leveraging Apple ResearchKit to develop an app that engages patients directly in innovation aimed at improving treatments for their diseases. We’re also working with IBM Watson to increase patient safety using cognitive computing to improve drug monitoring. As the power of collaborative innovation continues, collaboration will become more commonplace and lead to some amazing results.
    As you look out 12 to 18 months, what technologies might you want to bolt onto this platform or embed in your EIM strategy?
    The importance of cognitive computing, including machine learning and artificial intelligence, will continue to grow, helping us to make sense of the increasing volumes of data. The continued convergence of these technologies with the internet of things and analytics is another area to watch. It will result in operational insights as well as new, more intelligent ways to improve treatments for disease.
    What advice do you have for CIOs in health care or other industries who may not be as far along in their cloud, data, and analytics journeys?
    A digital enterprise is a knowledge- and information-driven enterprise, so CIOs should first focus on providing technologies and platforms that support seamless information sharing. In the process, CIOs should constantly be looking at information flows through an enterprise lens—real value is created when information is connected across all functions. Next, it’s increasingly important for CIOs to help build a technology ecosystem that allows the seamless exchange of information internally and externally because transformation and insight will occur in both places. Last, CIOs need to recognize that every job description will include data and information skills. This is an especially exciting time to be in IT because the digital capabilities we provide increasingly affect every function and role. We need to help people develop the skills they need to take advantage ofwhat we can offer now and in the future.
    Source: deloitte.wsj.com, November 14, 2016
  • Growth Stories: Change Everything

    mobile-uiInterview by Alastair Dryburgh

    What do you do with a small technology company which has an interesting product but is stuck in a crowded, noisy market where larger competitors have locked up many of the distribution channels? You could keep struggling on, or you could make a bold move; re-engineer the product to meet a different purpose for a different market. That's what Pentaho did, leading to 6-times growth over 5 years and a successful sale to a large organisation.

    In this interview their CEO Quentin Gallivan describes how they did it.

    Alastair Dryburgh: Quentin, welcome. This series is about that period of a company's evolution when it has to go through the rather dangerous territory that lies between being and exciting new start up and being an established profitable business. I'm told that you've got a very, very interesting story to tell about that with Pentaho. I'm looking forward to hearing that.

    Quentin Gallivan: Okay, great.

    Dryburgh: What would be useful would be if you could give us a very quick background sketch of Pentaho. What it does and how it's evolved in the last few years.

    Gallivan: So Pentaho, the company is approximately 12 years old. There were five founders, and they all came from a business intelligence technology background. What they were looking for was a different way to innovate around the business intelligence market place.

    One of the things I saw going on with that company was that the biggest challenge in companies doing data mining or predictive analytics on unstructured data or big data, was how do you get all this unstructured data, and unstructured data being clickstream data from websites, or weather data, or now what's very popular is machine data from Internet of Things devices.

    I wondered, is there a company out there that can actually make it easier to get all this different data into these big data analytical platforms? Because that was the biggest problem we had.

    When I looked at Pentaho, at the time it was not that company. It was not the new, sexy, next generation company, but I knew the venture capitalist behind Pentaho. We spent about a month just talking about what could the company be. Version one of the company was really a business analytics software product sold to the mid-market. They got some initial traction there, but that was a very cluttered market - very busy, a lot of noise, lots of large incumbents with channel dominance and then lots of small companies. It was hard to get above the din. I was not interested in Pentaho as the company was, right? I didn't see that as very interesting, very compelling.

    What interested me though, was when you dug deeper on the technology I thought it could be repurposed to address the big data problem. That was a big leap of faith, right? Because at the time, Pentaho wasn't doing any big data, didn't have any big data capabilities. The customers were all mid-market, small companies and it was known as a business intelligence company.

    Dryburgh: Pretty substantial change of vision really, isn't it?

    Gallivan: Massive, massive change, and I looked at it and I spoke to the VC's and said, "I would be interested in taking the CEO role, but not for the company that you've invested in, but for a very, very different company and I think we can do it. I don't know if we're going to do it. It's a long shot, but if you're willing to bankroll me, and allow me to build a team and support the vision, I'll give it a go."

    Dryburgh: Could I stop you there a moment to see if I could put a little bit of a frame around this? You've got a pretty fundamental change here.There's probably, very crudely, three different elements you've got to look after. First is obviously the technology and I guess that must have needed to evolve and develop. Then you've got what you might call the harder side of the organisational change, the strategy, definition of who the customer is, the organisation, the roles, the people you need, that's the second one. Then the third element which I think is particularly interesting is the softer side which is the culture. I'd be really interested to hear which of those was the biggest issue for you?

    Gallivan: That's a great question. I like the way you framed it, I would add a fourth dimension, which is the market perception of you. How do you get people to stop thinking about you as Open Source BI company for small and medium size businesses and think about you as leading, big data analytics platform for a large companies, for the large enterprise. Those are the four vectors that we needed to cross that chasm.

    The hardest one was not the culture because at the time, the company was very small. It had 75 employees and we are going to be over 500 employees this year, right? At the time it was really an open book from a culture... The founders were very open to a change in the business. For most startups, less than 100 employees, the culture is generally driven by the founder or founders and so there was no resistance.

    Dryburgh: Okay, good. So what were the biggest things you had to do to make the transformation work?

    Gallivan: If you look at those, just think about the transformation in those four key areas, you look at the metrics. Five years ago we were known as a commercial open source BI company selling to midsize companies. What we wanted to do was to be known as a big data analytics company selling to large enterprises because for big data that's where the dollars are being spent right now.. What we did was we set down the mission, we set down the strategy and then the other piece, and this is sort of from my GE days when it comes to strategic execution, that we employed was you've got to have metrics that drive milestones in the journey.

    What we started to do was we tracked what percentage of our business came from mid-sized small companies versus large. Five years ago 0% came from large. Last quarter it was 75%. Then over this journey we would track that percentage of our business that came from these larger enterprises. The other thing we would track was in that fourth vector, the brand. How do you change the brand from being known as an open source BI company to being known as a big data analytics company? There we had again, at the best marketing organisation I've ever worked with that had a share of a voice metric. Not a feel-good, hey we had so many press releases, but a quantifiable metric about our brand that we tracked four years ago and it was what position do we play and what share of voice do we have when people talk about big data versus non big data.

    That was where our marketing team was very aggressive and had these metrics. When we first started out, since we launched ourselves as a big data analytics company we had a pretty good penetration in terms of the brand, but over the last couple years we've been tracking, we've been number one or two versus our competitors as the most identifiable brand in big data. That's a metric we track every month. Very, very quantifiable, but it's part of the journey. It took us a while to get there.

    Then the other piece, the other key metric for us is really the R and D investment and that was, we basically had to transform or re-engineer the project to really meet the needs of the large enterprise from a security standpoint, a scalability standpoint. Making sure that we integrate with all the key technologies that the large enterprise have and so that was again, when we did prioritization around out R and D we would prioritize and we'd have metrics around large enterprise and then we would sacrifice the needs of the small/medium in the product road map. That again was an evolution.

    Five years ago 10% of our R and D investment went into large enterprise features. Now that's the majority, it's something didn't happen overnight but we tracked and we shared with the company and sort of made it work.

  • Only Half of Companies Actually Use the Competitive Intelligence They Collect

    jan16-26-128244186For more than 30 years, most large corporations worldwide have adopted competitive intelligence (CI) as a way to expedite good decisions. And yet for almost every company that uses CI in their decision-making, there’s another that disregards CI’s mix of industry analysis, rival positions, and market insight to their detriment.

    We recently conducted a survey of CI managers and analysts who’ve been through our training program to see how much their findings influenced major company decisions, and why. We received 236 responses from 21 industries in U.S. and European corporations, from CI-trained analysts in marketing, business development, strategy, R&D, finance, and other fields. They had an average of 6.3 years of experiencing in using CI frameworks and tools, and 62% were from companies with over $1 billion in annual sales revenues.

    We found that 55% of our respondents said that their input on major management decisions made enough difference to improve the decision. But 45% said their CI analysis did not.

    Why did some analysts have their input incorporated, while others didn’t? Our survey suggested several key reasons.

    First, many executives decide on a course of action and then use CI to ratify their choice. When asked, “What percent of your reports do you feel are just ‘confirmatory’ for an executive who already made a decision?” a full one-third of our respondents claimed “high” or “very high.” In these cases, the analysis may just be an obligation to be checked off a list.

    We also ran several simple OLS regression models and tested more than two dozen variables to see if they affected which companies actually allowed their CI analyses to influence their decisions. At the end, we found four variables turned out to be highly significant in explaining the difference in impact.

    1. The analyst was assigned a “sign-off” authority over major decisions. The single most effective way to ensure intelligence is used in any given decision is to give the analyst a say in moving it forward. In practical terms this means the analyst – not just the PowerPoint deck – becomes part of discussions leading to the decision. That is the one area where “intelligent organizations” differ most from others.

    2. Management was open to perspectives that were different from the internal consensus. Management that was more open to different perspective was also more likely to ask the analyst for the “big picture” rather than just the data.

    3. The analyst’s report called for proactive action more than reaction. Most companies are reactive by nature, and a lot of intelligence is about reacting to competitors’ moves. However, the decisions that matter more may well be those that are proactive. When the analyst provided proactive recommendations, the analysis had more of an impact.

    4. The analyst was involved in product launches. We don’t know why analysts in this area felt particularly impactful, but we do know that competitive intelligence is highly popular in tactical areas, and that product launches are an area where companies are most worried about competitors’ responses; successful product launches depend on correctly gauging the response of other players in the market. These include, naturally, customers and competitors, but also the less obvious responses by distribution channels, regulatory authorities, and influencing agents. Lack of insightful anticipation of these reactions — which is where competition analysts have the greatest expertise — leads to many more failures than there should be. Perhaps the analysts involved with product launches are thus given more of a mandate than analysts involved in other kinds of activities.

    None of these steps involves spending millions on the intelligence or hiring legions of analysts. And overall, these four variables explained a respectable 40% of the variability in having an impact on decisions. In terms of magnitude of the effect, the simple “sign off” requirement from management was clearly the leading contributor to explaining variability of impact.

    For these decisions – the ones that were improved by competitive intelligence — CI analysts reported many applications of their insights. While product launches were over-represented, our respondents told us about a wide array of applications for their analyses. They were evenly distributed between pursuing opportunities (46%) and reducing risks (44%), and ran the gamut from product pricing and features, capex investments, manufacturing processes, market expansion, joint ventures, M&A, and more.

    For example, in the pharmaceutical industry, respondents said that use of competitive intelligence had either saved or generated millions through discontinuing ineffective drug development efforts, walking away from bad deals and/or licensing opportunities, or accelerating new drug development based on what competitors were doing. For example, as one told us, “We accelerated our orphan disease program, based on accurate prediction of rival expected entry.”

    A common theme across industries was the smart reallocation of resources. One analyst told us that their company had stopped development on a project that was consuming lots of local resources after the analysis indicated it wouldn’t be effective. They then re-applied those resources to an area with true growth potential — that area is now starting to take off. In a different company, an analysis led to the cancellation of an extremely high-risk R&D program.

    This is not to discount the importance of ratifying a current course of action. In one of our favorite answers to our open-response question, an analyst described how CI had “identified only a single competitor, while determining others did not have the business case to continue a pursuit.” But it’s clear to us from this and other surveys we’ve done that the companies that get the most out of CI use it for a wide array of purposes – and actually let it shape their decisions.

    Source: Harvard Business review

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