Flywheel provides a research data platform that works with biomedical and imaging data that are managed at life sciences, clinical, and academic institutions globally. Flywheel provides a comprehensive research data solution with all the tools needed for curation, image processing, machine learning workflows, and secure collaboration. Flywheel was founded in 2015 and is based in Minneapolis, Minnesota.
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ESPs containing Flywheel
The ESP matrix leverages data and analyst insight to identify and rank leading companies in a given technology landscape.
The companies in this market provide solutions that improve the process of collecting data and documentation associated with clinical research. Solutions typically encompass quality control (QC) and quality assurance (QA) activities, both of which ensure the safety of patients and credibility of results.
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Research containing Flywheel
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CB Insights Intelligence Analysts have mentioned Flywheel in 1 CB Insights research brief, most recently on Aug 4, 2021.
Expert Collections containing Flywheel
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Flywheel is included in 5 Expert Collections, including Digital Health.
Startups recreating how healthcare is delivered
Clinical Trials Tech
Companies developing products and services to streamline drug R&D, from drug discovery, pre-clinical testing, and clinical trials.
This collection includes startups selling AI SaaS, using AI algorithms to develop their core products, and those developing hardware to support AI workloads.
Clinical Trials Tech Market Map
This CB Insights Tech Market Map highlights 100+ clinical trials tech companies that are addressing 8 distinct technology priorities that pharmaceutical companies and CROs face.
Latest Flywheel News
Oct 25, 2022
October 26, 2022 - Advertisement - Data and analytics are delivering on their promise. Every day, it helps countless organizations do everything from measuring their ESG impact to creating new streams of revenue, and as a result, companies without strong data cultures or concrete plans to create one are feeling the pressure. Some are our customers—and more of them are seeking our help with their data strategy. - Advertisement - Often their asking is a small busted admission of exaggeration. They also struggle to articulate their purpose, or don’t know where to start. The variables seem endless: data-security, science, storage, mining, management, definition, erasure, integration, access, architecture, archiving, governance, and the always-elusive, data culture. But for all that technical complexity, their clout is often a symptom of mindset. They think that when creating their first formal data strategy, they should have all the answers up front – that all relevant people, processes, and technologies should be neatly lined up like dominoes. We discourage that thinking. Collecting data is like spinning a flywheel: it takes tremendous effort to propel the wheel, but its speed is largely self-sustaining; And thus, as you apply force, the wheel spins faster and faster, until the fingertip touches are close enough to maintain a blistering velocity. As the wheel builds up to that momentum, the people, processes, and technologies it needs to support make themselves evident. - Advertisement - In this article, we provide four things to make your flywheel spin faster, and examine each through the story of ChampionX Chief Information Officer Alina Parst, and how she’s helping the company change ( which provides solutions for the upstream and midstream oil and gas industries) into a data-driven powerhouse. Step 1: Choose the right problem When ChampionX went public, its cross-functional team (which included supply chain, digital/IT and commercial experts) avoided any grand, buzz-filled announcements in favor of “conversions” and “data-driven cultures”. Or at least restrained. Real world problem solving. But at the same time, it didn’t choose any problem: it chose the right problem—the first and most important step in getting your wheel to spin. At the time, one of ChampionX’s most expensive activities in its chemical technologies business was monitoring and maintaining customer sites, many of which were in remote parts of the country. “It was more than just labor and fuel,” Alina explained. “We had to spend a lot on maintaining vehicles capable of navigating the routes at those sites and finding out what, exactly, those routes were. There was no Google Maps for our field technicians to go where, And still is.” Among those costs was the cost of “keeping customers’ tanks full, not dry” – one of ChampionX’s guiding principles and the core of its value proposition to improve the lives of its customers. “And so, we thought, ‘How can we serve that end? '” The problem the team chose to solve – reducing the cost of site visits – may seem mundane, but it had all the right ingredients to get the flywheel running. At first, the problem was urgent, as it was one of ChampionX’s most significant expenses. Second, the problem was simple (even if it didn’t have a solution). It was easy to explain: it costs us a lot to visit these places. How can we reduce that cost? Third, it was tangible. It deals with real-world objects—trucks, wells, equipment, and other things that people could see, hear, or feel. Equally important, the team can point to specific financial line items that will propel their efforts. In the end, the problem was largely shared by the enterprise. As part of the cross-functional leadership team, Alina didn’t limit herself to solving CIO-related problems. She understood: If this was a problem she and her organization could help solve, it was a CIO related problem. IT executives often talk about people, processes, and technology as the cornerstones of IT strategy, but they sometimes forget to focus on the heart of all strategy: solving real business problems. When you’re starting out, put aside your worries about who you’ll be hiring, what tools you’ll use, and how your people work together—those things will make themselves clear over time. First put your leaders in a room. Skip the slides, spreadsheets, and roadmaps. Instead, ask honestly: What problem are we trying to solve? The answer won’t come as easily as you’d expect, but the conversation will be invaluable. Step 2: Capture the right data Once you have identified a solvable problem, the next step is to capture the data you need to solve it. If you’ve defined your problem well, you’ll know what that data is, which is important. Just as defining your problem describes the diversity of data you can capture, figuring out what data you need, where to get it, and how to manage it, it’s people, Will limit the vast list of processes and technologies that can make up your data environment. Consider how it went for Alina and ChampionX. Once the team discovered the problem—site visits were expensive—they immediately identified a logical solution: reduce the number of site visits required. Most of the visits were routine rather than a response to an active problem, so if ChampionX could remotely understand what was happening at the site, they could save a considerable amount of time, fuel and money. That insight told them what data they would need, which in turn allowed ChampionX’s IT and commercial digital teams to understand who and what they needed to capture it. They needed IoT sensors, for example, to extract relevant data from sites. And they needed a place to store that data—they lacked the infrastructure that would both sensors and coupling customer data (which resided within enterprise platforms like ERP, transportation, and supply and demand planning) to terabytes. could manage. Therefore, he built a data-lake. Each of these initiatives – secure cloud infrastructure, design of data lakes, sensors, storage, required training – was a major undertaking and continues to grow. But the ChampionX team not only solved the site-visit problem; They provided the company’s data environment and a foundation for the data-driven initiatives that would follow. Data Lake, for example, came to serve as a home to the ever-increasing volume and diversity of data from ChampionX’s other business units, which led to some valuable insights (more on that in the next section). Knowing what data to capture provides the context you need to begin selecting people, tools, and processes. Whatever you choose, they will lead themselves to unpredictable ends, so it’s a taxing and fruitless exercise to try and map out whichever way one component of your data environment will be connected to all the others—and from that, the toolkit. To choose. Instead, figure out what you need for the problem—and the data—in front of you. Because you will be making selections in relation to something real and important in your organization, chances are, your selections will serve something more real and important. But in this case, you’ll be able to specify the names, costs, and indexing of what you need—details that will make your data strategy realistic and your flywheel around faster. Step 3: Connect the Points That Once Looked Different As you start capturing data and your flywheel spins faster, new opportunities and data will reveal themselves. After ChampionX’s team installed IoT sensors to remotely monitor customer sites, it didn’t realize that the same data could be applied elsewhere. ChampionX now had a wealth of topographical data that no one else had, and it used this data to move both the up and down lines. It moved the bottom line by optimizing the routes that ChampionX’s vehicles took to the sites—solving the no-Google-Maps-where-we-going problem—and it used the data as a new revenue stream. Monetized in form and reached the top. Data Lake also took on new purpose. Other business initiatives began piling their data into it, which prompted cross-functional teams to consider the different types of information strung together and how they might be greater than the sum of their parts. One type was customer, order and supply chain data, which ChampionX regularly needed to pull and merge with site data to perform impact analysis – what and how their customers were affected by disruptions in the supply chain network. . It took weeks to merge those data, mainly because the two data had always lived in different ecosystems. Now, those same analyzes only took hours. There are two takeaways here. The first is that it’s okay if your data flywheel spins slowly in the beginning – just turn it on. Attracting some new opportunities or types of data will give you a chance to make connections between things that once seemed different. Recognition of that pattern will speed up your flywheel at an exponential rate and encourage a seemingly complex data environment to take shape around it. The second takeaway is similar to the first two steps: Choose wisely among the opportunities you can pursue. Not every insight that is interesting is useful; Pursue what is most valuable and real, that people can see, measure, and feel. These will overlap significantly with tedious and ordinary, recurring organizational activities (such as pulling together impact reports). If you can solve these problems, you’ll prove the viability of data as a force for change in your organization, and a rich data culture will begin to emerge, pushing Flywheel at an intimidating pace. Step 4: Build Outward from Your Original Problem The ChampionX story we examined is only one chapter in a much larger story. As the company collects more data and its people gain new insights, the problems faced by Alina and its business partners have grown in scope and complexity, and ChampionX’s flywheel has reached a speed capable of powering the data. -Company’s first problem-solution complete supply chain. Still, most problems somehow go back to the simple question of how a company can spend less on site-checks. The ChampionX team is not concerned with problems that are related to supply chains that are related to marketing, or human resources, or finance; The team is expanding outward from its original problem in a logical progression. And because they have, their people, processes, and technologies, in terms of maturity, they are only a stone’s throw from being able to tackle the next challenge – which has always built upon the one before it. As your flywheel spins faster, you’ll have more problems to choose from. Prioritize those that are not only viable and valuable but also in line with the problems you have already solved. That way, you’ll be able to take advantage of the momentum you’ve built up. Your data environment will already contain many of the people and tools you need for the job. You won’t feel like you’re starting anew or that you have to argue a scratch case for your stakeholders. Creating a data strategy is like spinning a wheel. It is cyclic, iterative, gradual, eternal. There is no specific line that, if crossed, your organization will be considered “data-driven”. And so on, there’s no use thinking of your data strategy as something binary, as if it were an under-construction building that will one day be complete. The best thing you can do is to use your data to solve problems that are urgent, simple, tangible and valuable. Gather the people, processes, and techniques needed to deal with those problems. Then, move on to the next, and then the next, and then the next, allowing elements of a vibrant data ecosystem to emerge along the way. You cannot bring your data strategy into existence; You can draw it by simply focusing on the flywheel. And when it appears, you and everyone else will know it.
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Flywheel Frequently Asked Questions (FAQ)
When was Flywheel founded?
Flywheel was founded in 2015.
Where is Flywheel's headquarters?
Flywheel's headquarters is located at 1015 Glenwood Ave, Minneapolis.
What is Flywheel's latest funding round?
Flywheel's latest funding round is Grant.
How much did Flywheel raise?
Flywheel raised a total of $51.74M.
Who are the investors of Flywheel?
Investors of Flywheel include Bill & Melinda Gates Foundation, Intuitive Ventures, iSelect Fund, 8VC, Argonautic Ventures and 11 more.
Who are Flywheel's competitors?
Competitors of Flywheel include YonaLink, Medexprim, Gesund.ai, Saama Technologies, Ambra Health and 12 more.
Compare Flywheel to Competitors
Saama Technologies is a clinical data and analytics company. Saama's unified, AI-driven clinical data analytics platform integrates, curates, and animates unlimited sources of structured, unstructured, and real-world data to deliver insights across all therapeutic areas. The platform gives real-time visibility into clinical data, enabling sponsors to file New Drug Applications (NDAs) more efficiently to bring drugs to market faster and at lower costs. Saama Technologies was founded in 1997 and is based in Campbell, California.
Complion provides clinical research site regulatory and document management solutions including its eRegulatory and document management software for sites, health systems, academic medical centers and cancer centers.
YonaLink develops a platform to migrate clinical research data saved in health records, from medical centers to research databases in an automated, secure, validated, trusted, and error-free way. Its SaaS platform stream up-to-date data from the EHR platform and populate it within YonaLink’s EDC or other data capture systems. The company was founded in 2019 and is based in Boston, Massachusetts.
Sycamore Informatics offers a way for clinical trial data handling, analysis and reporting functions to be more efficient and cost-saving. It is based in Waltham, Massachusetts.
eClinical Solutions is a provider of end-to-end data management services and cloud-based products for clinical data optimization.
Specializes in statistical data analysis services and proprietary business intelligence analysis software development, principally in the pharmaceutical and life sciences industries.
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