Lou Brooks
Senior Vice President Data and Analytics, Life Sciences
Optum
Life sciences organizations find themselves trying to figure out the pieces of the puzzle when it comes to acquiring the right data, tools and analytics for their data strategy. The "Four Steps to a Real-World Data Enterprise Strategy" podcast series shares how to bring the pieces together to ensure you’re driving value from your data.
Step 1: Planning is Everything
The fundamental step in implementing a real-world data (RWD) or real-world evidence (RWE) strategy is planning — yet, it’s the step many organizations often overlook. Your end goal may be to become a data-driven organization, but what does that really mean?
What role do you see data playing in different areas of your business? How can you ensure your RWD doesn’t just provide incremental gains in specific use cases, but instead is flexible and scales to span the entire organization? Without a good plan, it’s difficult to determine the path forward when implementing the solutions most needed.
Listen to the episode below to learn more.
Data-driven? What does that even mean? Hear Lou break it down.
Video: Planning is Everything
- [Narrator] There's been a lot of buzz about the need for a real-world data and real-world evidence strategies. Life sciences organizations find themselves trying to figure out the pieces of the puzzle when it comes to acquiring the right data, tools and analytics that will derive insights from volumes of information. This has led to growing interest in how such data may support organizations more broadly and how to best invest in data, technology and the infrastructure to truly achieve a successful real-world data program. Welcome to the podcast, "4 Steps to a Real-World Data Enterprise Strategy." In this series, Lou Brooks, Senior Vice President Data and Analytics at Optum, identifies the four essential steps of an enterprise strategy to help ensure your driving value from the data. Let's dive right in with episode one, planning is everything. In this episode, Lou shares more detail on how planning is critical to laying the foundation for a successful enterprise strategy. Let's turn it over to Lou to find out more.
- [Lou] Let's talk for a moment about the four steps to a successful real-world data strategy. There's a lot of buzz for the last few years with various organizations talking about data, talking about tools, talking about solutions and how you derive insights from your data. There hasn't been a lot about how to set that up successfully. Most of these organizations are out trying to sell their own particular solution or piece of the overall puzzle, but it opens up a broader question around how your thinking about data and using data to support your organization, and ultimately what that translates to in terms of organizational changes, investments in technology and other types of infrastructure, as well as the data itself and having an organization that is really set up to oversee that process and ensure that you're getting the maximum value in return out of that particular investment, because let's be honest, it's not cheap to set up these types of real-world data and real-world evidence platforms, and you can find yourself with a significant amount of expense with not a great deal of return if it's not done correctly. So let's just talk about what those four key steps will be or are, and we'll take it from there. I think the first and it's kind of hard to think about this in any other way other than you gotta have a plan, we see a lot of stuff, you know, being published around being a data-driven organization, but what does that really mean? Does that mean that you're utilizing data for your commercial operations? Does that mean that you're using data for your research and development opportunities? Are you using it to connect with individual stakeholders in a different fashion, whether those be patients or providers or payers? Is it all of the above? I think the one fundamental problem that many organizations look at and run into when they look at the real-world data strategies is that they don't have a clear plan and as a result of not having that clear plan, they don't have a really good solution to what they're going to do and how they're going to make their investments, and as a result, they end up with a hodgepodge that results in incremental gains in specific use cases but lacks the broader return that could be achieved by doing these types of things at scale. And if you want to embrace empirical analysis in utilizing data and understanding what's happening in the real world, and that thought process has to span the entire organization. It has to start with, you know, preclinical and evaluating opportunities for investment, and it has to carry all the way through patent expiration in an individual molecule. And I think that the best way that I've seen this implemented lays out their key areas of focus and looks to build a centralized organization that enables them to not so much control real-world data and evidence, but guide the individual stakeholder in businesses within their organization to gain the maximum value out of the particular investments that are being made. And I think that those organizations that have been most successful in centralizing some type of real-world data and evidence solution, ultimately the ones that have also provided the greatest deal of flexibility to their individual constituents. So it's not so much that this organization is the all being, all knowing, all saying, dictator of all things real-world data and evidence, rather it's the hub of a spoke designed to allow each individual organization, whether that be R&D, whether that be clinical development, whether that be managed markets, payer interactions contracting, commercial operations, the brand teams, doesn't matter who it is, each of those individual organizations have their own specific use cases and needs, but they're all pulling from that centralized repository.
- [Narrator] Be sure to listen to episode two for the rest of the story.
STEP 2: Set Your Data Free to Maximize its Full Potential
The value of data isn’t apparent when it's just sitting on a server. Its value is generated when organizations and individuals can access it, share it, and use it to conduct analysis and to generate insights. When acquiring any data asset, even internal data, it’s important to have agreements that allow its use across multiple areas of the enterprise.
Data is often siloed, which limits the value it can provide. Removing silos can be challenging, but democratizing data allows everyone to access it through a single repository, rather than creating multiple copies that can lead to inefficiency and errors during updates.
Listen to the episode below to learn more.
Breaking down data silos can free your teams to push innovation.
Video: From 1s and 0s to Actionable Analytics
- [Narrator] Episode two, Set your data free to maximize its full potential. In this episode, Lou offers the case for removing silos, so data can be democratized, allowing wider access through a single repository to maximize value across the enterprise. Let's join Lou to learn more.
- [Lou] The second step is really about democratizing access to the data. Data unto itself doesn't have value, when it's just sitting on a server. Its value is truly generated when organizations and individuals can access it, mine it, conduct analysis with it, and develop a variety of different insights from it. So if you think about, you know, acquiring any data asset, even data that you may have internally on your own, that you've collected through clinical research or other types of activities, as long as you have the appropriate data use agreements in place, then the data can be utilized for research purposes and can provide value than more than one use case from that standpoint. And one of the things that I find very often, in working with our clients, is that data is siloed, and those silos ultimately limit the potential that can be attained from licensing that data. As well as maximizing the return that an organization gets out of the investment. Let's, let's use a simple case. You know, an example from, from that standpoint, let's say that you're, you know, an organization and your licensing electronic health record data. And let's say that you're the research and development clinical organization, and you licensed that data primarily to support the validation of protocols. You want to make sure that you, as you're designing protocols and you're working through your clinical development process, that you have some real world data guiding the different things that you want to ensure either included or excluded in your patient population. And let's say you're the only ones that have access to that data that data could provide benefit across the broader organization, your health, economics, and outcomes research teams could ultimately be utilizing that data in other markets for products that might already be in, in, approved and, and in promotion today to demonstrate real world evidence. And to highlight the value that these products bring, comparing them to other products in a head-to-head fashion as an example, your commercial team could be utilizing it to evaluate the potential changes to their marketing campaign and evaluating the types of patients that are on product versus those that they want to obtain from that standpoint. And your managed markets organization could be leveraging the data to evaluate the landscape, to come up with new and novel value-based contracting arrangements, you know, but in that scenario with all three organizations having access to the data, quite simply, you obviously have more value to be obtained from that initial investment. And I think that that's one of the biggest challenges that organizations have is in figuring out how to bring this data into their organization and democratize it. Put it in a place where everyone can access it, rather than creating either multiple copies of the same data, which breeds inefficiency, and leads to problems down the road. When you know, one database isn't getting updated, maybe when it should be, or, you know, maybe there's a problem in, in one, you know, in one copy of the data, as a result of some sort of transmission error or whatever the case may be, it's inefficient to have these multiple silos of data. It's much more efficient to have one central repository that each organization is pulling from in which to conduct their analysis. It really boils down to setting up a single source of the truth for organizations to tap into, and to put in place the appropriate guidelines, and processes and procedures to control access. Some datasets shouldn't be accessed by some organizations. As well as to ensure that data is consistent as it's moving from one organization to the other.
- [Narrator] Be sure to listen to episode three for the rest of the story.
STEP 3: From 1s and 0s to Actionable Analytics
As we discussed in episode 2, data itself doesn't generate value sitting on a server — it's just 1s and 0s until someone works with it. A successful RWD strategy requires the right experts and analytics tools. Those experts can be internal data scientists or outside consultants — but they must have the background and skill set to mine data for actionable insights and leverage it for modeling and planning.
That analysis should also include different perspectives, including clinical outcomes, commercial operations and financial insights. When choosing analytics tools, begin by considering your organization’s business needs, including things like data warehousing, visualization, security and governance.
Listen to the episode below to learn more.
Without the right people and tools, data sitting on a server is just 1s and 0s.
Video: From 1s and 0s to Actionable Analytics
- [Host] Episode 03, "From 1s and 0s to Actionable Analytics". In this episode, Lou goes deeper onto why it's so important to choose the right analytics experts, tools and different perspectives. Let's go back to Lou for more on this topic.
- [Lou] The third step, empowering analytics. As I said earlier, data unto itself doesn't generate value as it's sitting on a server, it's just ones and zeros from that standpoint. And as a result, if you truly want to maximize the value of your investment in data you have to have two key things as it relates to analytics. One, you have to have people with the background and skillset that can mine that data and turn it into insights from its raw form, from that particular standpoint. And whether those individuals are a member of your organization, contractors, or outside consultants, you need a certain level of experience working with the different types of data in order to maximize the value of that investment. The second thing you need is I'll classify it as tools. If we think about this in terms of building a house, raw materials, lumber, corresponding to data; hammer, nails, things of that nature are tools, right? The carpenter is just like the analytic resources that I just talked about a moment ago but the hammer, the nails, the saws, those are the tools. Those are the things that we leverage, the visualization software, the infrastructure, the other types of software that exist to do statistical analysis, to do machine learning, et cetera. All of that information, again, is not generating value without having individuals with the right tools available to do the analysis and those experts should be wide ranging, right? They should have expertise in looking at and understanding data in relation to clinical trials, they should have a set of skills that can analyze data from an outcome standpoint, from an epidemiological standpoint, from a commercial operation standpoint. There should be actuaries and financial consultants that can leverage the data to do various types of modeling and planning to help you manage your business overall. And if you've got that team of individuals, or that network of individuals and appropriate tools, then you now have the right foundation to derive value from the data. And a lot of this gets back to that earlier plan, making sure that you know what your use cases are, know what types of tools and what types of people you're going to need? How many of those people are very important pieces to know so that you truly set yourself up for success as you look to work with the data and develop various types of real world evidence.
- [Host] Be sure to listen to episode 04 for the rest of the story.
STeP 4: The Flexibility to Be Nimble
The adage, “the only constant is change,” is certainly true for life sciences organizations. They must navigate between constantly market trends, regulatory updates and new opportunities coming from both the life sciences and health care industries. Your RWD strategy should have a certain level of flexibility to handle these changes in direction.
That flexibility revolves around three areas:
- Acquiring more or differentiated data
- Integrating data to address new questions
- Having the right resources to handle new demands
All three hinge on having the right data strategy framework and aligned partners so that you can move nimbly when the time comes.
Listen to the episode below to learn more.