In conversations with bankers and startups, it is clear that there they have differing views of the world. It is not as clear-cut as “nimble innovator versus dinosaur incumbent,” which is how many portray the chasm, but there is a radical difference in thinking, perhaps best summed up by a banker’s recent comment to me: “surely this is Techfin rather than Fintech.”
I thought about what he meant and realized that this is the subtle difference between the innovator and the incumbent. An innovator thinks of this as Fintech: taking financial processes and applying technology. Incumbents think of this as Techfin: taking technology to work with financial processes. This difference in thinking, although subtle, does create a very different thought process and output in the way technology is used. So I thought I would delve a little deeper, as this is a key to seeing how the world differs between the innovators and incumbents.
First, the startup Fintech firm. This firm looks at the world through the eyes of a technologist. This means that the start point is technology. Apps, APIs, analytics, and more are the foundations of their thinking. Open source, open operations, open thinking are at the heart of their culture. Embracing diversity and working globally without reference offices or structure are the tools of their skillset. And a mentor, an angel, and an investor are the base capital requirements to get them started.
This startup begins by thinking about how technology could transform financial processes. This means that they take something that exists – loans, savings, investments, payments, trading, and more – and think about how they could reinvent these processes. Peer-to-peer (P2P) lending is a good example. When Zopa started in business in April 2005, they told me about their business model and it sounded weird, to be honest. “We’re an eBay for loans,” they told me. “You give us your money and we lend it out on your behalf. You get better interest on your money than you would with a savings company, and people pay less for their loans,” they continued. “Want to invest £10,000?”
No way, as it sounded crazy. An untested, unproven business that would take my investment and manage the risk of lending that investment to borrowers? An eBay for loans? That’s startup thinking. A decade later, that startup is taking over £1.2 billion in funds from over 53,000 consumers to lend at the most competitive rates in the UK. In fact, the startup P2P model is so popular that it’s been copied worldwide. The US is one of the fastest growing markets – over $8 billion has been loaned, doubling year-on-year. It is why Lending Club had one of the hottest IPOs of 2014 followed up by SoFi receiving over $1 billion investment in its latest funding round.
These are significant numbers, but nowhere near as significant as the forecasts by banks like Goldman Sachs and Morgan Stanley. Goldman Sachs predicts that almost $11 billion of bank profits from lending will move to the new startup social economy by 2020 – about 5% of the current market – while Morgan Stanley estimates that global marketplace lending should reach $290 billion by 2020, with a CAGR (compound annual growth rate) of 51% from 2014-2020, and China and America the two largest markets.
And this is the key to the innovators’ Fintech thinking: How can we take an existing market with a middleman and replace the middleman with a technology intermediary? That is what Bitcoin is focused upon – replacing the bank with the Internet for value transfer; it is what new trading schemes like T0.com focus on – replacing the stock market with the blockchain; and it is what firms like TransferWise and Currency Cloud believe – replace FX markets with P2P connectivity to enable money to move.
There are many more examples. The rapidly growing and disruptive Fintech scene is hot because it is all about using technology to transform financial processes. The incumbent thinking of the Techfin is very different.
Thanks to the Internet, mobile technology and soon the Internet of things, people, places, organisations and objects are linked together like never before. Learn more about The Hyperconnected Economy and how that’s changing how we work and connect.
When customers own their engagement with your brand – where, when, and how it happens – when engagements are smaller and more personal, and when it takes more to keep hold of a customer’s attention, the lines between sales and marketing begin to blur. Sales would love marketing to deliver qualified leads, and marketing campaigns begin to look a lot more like sales conversations. But the success of your organization depends on these two teams working together.
A recent Aberdeen research report, commissioned by SAP, stresses the importance of the sales and marketing relationship for “best-in-class” companies. Each team needs to understand who’s bringing what to the table, and how best to collaborate to close more sales.
To start the conversations at your own company, here are a few key priorities to get the teams better aligned for success.
1. Ownership of a lead must be shared by both sales and marketing.
It may sound obvious, but in practice it can be difficult to differentiate whether lead generation and nurturing falls to marketing or sales. Parts of the process may fall to either team, and when each team feels like they’re working toward their own goals instead of a shared objective, that can lead to confusion and duplicate or conflicting efforts.
Does this sound familiar? Marketing creates leads to pass to sales, but sales already has leads of their own. Or your marketing team is driven by quantity and delivers leads to sales that don’t seem to be high-quality.
Figure out a way to determine what qualifies as a good lead, and establish from the get-go who’s responsible for it when. Marketing and sales should engage each other regularly for feedback to optimize the entire lead-gen process and work towards a common objective, instead of battling it out internally or blaming each other for a lack of qualified leads.
2. Share data and info to personalize customer experiences.
In order to deliver the amazing customer experiences you imagine, you need to know your customer – to really understand their industry, and dive into the challenges they face. You need to be able to talk to them on their terms, so you need to understand where they’re coming from. Thankfully, in the age of connected tech, we have the data to do that. And it’s handled by the marketing team.
Marketing does the research to get to know the customers and their industries. They have content that is relevant to every stage of the selling process. If sales leverages the expertise of the marketing team, they can guide customers along their journey, eventually leading them to the sale.
Sales can then personalize the entire customer experience, tailoring the conversation and engaging customers on a deeper level. And marketing can act more as the experts and thought leaders, doing the research and gaining a wealth of information on various industries and challenges. The entire experience is elevated.
3. Leverage analytics and insights to drive higher-quality understanding.
Beyond an understanding of a customer’s industry, high-quality analytics can be used to better understand how best to close a sale. With effective data collection tools, marketing has access to a wealth of information about a customer’s engagement with your brand – how and where they’ve engaged with you, the type of information they’ve sought out, how many pieces of content they’ve downloaded, what events they’ve attended – all of which illustrates where their interests and priorities lie.
Sales can use this industry-specific information to nurture leads in a deeply personalized way, knowing where the customer is coming from and where they are on their journey. They can use historical data – past customers in similar situations – to determine what content and information to pass onto the customer, and how to effectively address their needs and make the sale. They can seize opportunities for cross-selling. They can look at a customer’s journey and attempt to replicate it with suggestions and recommendations that serve the customer.
In turn, sales can then report back to marketing with insights as to which leads and opportunities are the most promising, showing the most potential to close, to help drive marketing’s future content-to-conversion research.
No matter what, communication and collaboration are key. The feedback loop must be closed between the teams, with each group providing feedback to the other and adapting for more effective processes. By working closely together internally, you’ll be able to engage customers more personally and more effectively.
As commerce continues to get more digital and more social, companies of all sizes are trying to bring their businesses online quickly, easily, and at low cost. Many are opting for more streamlined, agile cloud-based platforms. But even then, there are so many options to choose from.
Some platform providers are pitching the seemingly attractive offer of revenue-sharing pricing models, which promise a low cost of entry, as you don’t pay a cent until your company starts earning revenue. Sounds like a risk-free offer, but take a closer look and you’ll see you may not be getting the “free lunch” you were promised. When evaluating these platforms that offer revenue-sharing pricing models, it’s important to ask yourself the following questions to make sure you’re really getting the most bang for your buck.
1. Does the solution meet your company’s needs?
It’s so tempting sometimes to go with the more economical solution, we forget to ask the most basic question when choosing a commerce platform: does the software meet the needs of your business? This is going to be the base of your entire commerce operation, your company’s future, so it’s important to stay focused on the things that are most important to your company. Ask, is the platform capable of meeting your business’s objectives? Does it support the nuances of your industry? If no, consider the opportunity cost of choosing this over a platform that may have upfront capabilities that will work for you more long-term.
2. How flexible is the platform?
One way platform providers are able to cut costs is by delivering a more standardized, templated solution to all their clients. This saves implementation time and money, but limits your ability to customize your experience to differentiate in the market. Think about how you want your site to look and function, and ask yourself if the platform is able deliver, or if you will be sacrificing differentiation to save money.
And along those lines…
3. How do you implement features that aren’t provided by the standard platform?
For those features that you’re not willing to sacrifice, but that aren’t available in the standard version of the cloud platform, how will you bring those to life? Most often, you’ll need to rely on third-party partners to build and implement those features and functionality, and those providers will likely not be included under the revenue-sharing pricing model. They’re additional costs that may not have been accounted for until you’ve already started down that path.
4. What happens when your revenue grows?
Revenue-sharing solutions are the most enticing for smaller organizations with tighter budgets, because they’re not on the hook to pay anything until they’re make money. But most businesses don’t aim to stay small for long. Be clear about what revenue growth will mean for the TCO of your platform. When revenue-sharing models charge a percentage of your revenue, you pay more the more revenue you generate. At a certain point, it stops being cost-effective. Be realistic about your targets and projections for both the short- and long-term, and factor those into the total cost of ownership of the solution.
It’s also important to remember that revenue ≠ profit. You can drive a lot of revenue, but if your margins are low that’s a higher percentage of your profits that will be going to pay for your platform.
There are certainly scenarios in which revenue-sharing pricing models make sense. But whenever someone offers you a deal that seems too good to be true, it’s important to dig deeper to make sure you fully understand what the offer entails, and risks involved. Remember that as you design and manage your business strategy, it’s less about cost and more about driving value. Choosing a cheaper solution may save you money upfront, but at what cost? Find a solution that adds value to your business and you’ll end up saving and winning in the long-term. And isn’t that why you got in the business to start with?
Last August, a woman arrived at a Reno, Nevada, hospital and told the attending doctors that she had recently returned from an extended trip to India, where she had broken her right thighbone two years ago. The woman, who was in her 70s, had subsequently developed an infection in her thigh and hip for which she was hospitalized in India several times. The Reno doctors recognized that the infection was serious—and the visit to India, where antibiotic-resistant bacteria runs rampant, raised red flags.
When none of the 14 antibiotics the physicians used to treat the woman worked, they sent a sample of the bacterium to the U.S. Centers for Disease Control (CDC) for testing. The CDC confirmed the doctors’ worst fears: the woman had a class of microbe called carbapenem-resistant Enterobacteriaceae (CRE). Carbapenems are a powerful class of antibiotics used as last-resort treatment for multidrug-resistant infections. The CDC further found that, in this patient’s case, the pathogen was impervious to all 26 antibiotics approved by the U.S. Food and Drug Administration (FDA).
In other words, there was no cure.
This is just the latest alarming development signaling the end of the road for antibiotics as we know them. In September, the woman died from septic shock, in which an infection takes over and shuts down the body’s systems, according to the CDC’s Morbidity and Mortality Weekly Report.
Other antibiotic options, had they been available, might have saved the Nevada woman. But the solution to the larger problem won’t be a new drug. It will have to be an entirely new approach to the diagnosis of infectious disease, to the use of antibiotics, and to the monitoring of antimicrobial resistance (AMR)—all enabled by new technology.
Keeping an Eye Out for Outbreaks
Like others who are leading the fight against AMR, Dr. Steven Solomon has no illusions about the difficulty of the challenge. “It is the single most complex problem in all of medicine and public health—far outpacing the complexity and the difficulty of any other problem that we face,” says Solomon, who is a global health consultant and former director of the CDC’s Office of Antimicrobial Resistance.
Solomon wants to take the battle against AMR beyond the laboratory. In his view, surveillance—tracking and analyzing various data on AMR—is critical, particularly given how quickly and widely it spreads. But surveillance efforts are currently fraught with shortcomings. The available data is fragmented and often not comparable. Hospitals fail to collect the representative samples necessary for surveillance analytics, collecting data only on those patients who experience resistance and not on those who get better. Laboratories use a wide variety of testing methods, and reporting is not always consistent or complete.
Surveillance can serve as an early warning system. But weaknesses in these systems have caused public health officials to consistently underestimate the impact of AMR in loss of lives and financial costs. That’s why improving surveillance must be a top priority, says Solomon, who previously served as chair of the U.S. Federal Interagency Task Force on AMR and has been tracking the advance of AMR since he joined the U.S. Public Health Service in 1981.
A Collaborative Diagnosis
Ineffective surveillance has also contributed to huge growth in the use of antibiotics when they aren’t warranted. Strong patient demand and financial incentives for prescribing physicians are blamed for antibiotics abuse in China. India has become the largest consumer of antibiotics on the planet, in part because they are prescribed or sold for diarrheal diseases and upper respiratory infections for which they have limited value. And many countries allow individuals to purchase antibiotics over the counter, exacerbating misuse and overuse.
In the United States, antibiotics are improperly prescribed 50% of the time, according to CDC estimates. One study of adult patients visiting U.S. doctors to treat respiratory problems found that more than two-thirds of antibiotics were prescribed for conditions that were not infections at all or for infections caused by viruses—for which an antibiotic would do nothing. That’s 27 million courses of antibiotics wasted a year—just for respiratory problems—in the United States alone.
And even in countries where there are national guidelines for prescribing antibiotics, those guidelines aren’t always followed. A study published in medical journal Family Practice showed that Swedish doctors, both those trained in Sweden and those trained abroad, inconsistently followed rules for prescribing antibiotics.
Solomon strongly believes that, worldwide, doctors need to expand their use of technology in their offices or at the bedside to guide them through a more rational approach to antibiotic use. Doctors have traditionally been reluctant to adopt digital technologies, but Solomon thinks that the AMR crisis could change that. New digital tools could help doctors and hospitals integrate guidelines for optimal antibiotic prescribing into their everyday treatment routines.
“Human-computer interactions are critical, as the amount of information available on antibiotic resistance far exceeds the ability of humans to process it,” says Solomon. “It offers the possibility of greatly enhancing the utility of computer-assisted physician order entry (CPOE), combined with clinical decision support.” Healthcare facilities could embed relevant information and protocols at the point of care, guiding the physician through diagnosis and prescription and, as a byproduct, facilitating the collection and reporting of antibiotic use.
Cincinnati Children’s Hospital’s antibiotic stewardship division has deployed a software program that gathers information from electronic medical records, order entries, computerized laboratory and pathology reports, and more. The system measures baseline antimicrobial use, dosing, duration, costs, and use patterns. It also analyzes bacteria and trends in their susceptibilities and helps with clinical decision making and prescription choices. The goal, says Dr. David Haslam, who heads the program, is to decrease the use of “big gun” super antibiotics in favor of more targeted treatment.
While this approach is not yet widespread, there is consensus that incorporating such clinical-decision support into electronic health records will help improve quality of care, contain costs, and reduce overtreatment in healthcare overall—not just in AMR. A 2013 randomized clinical trial finds that doctors who used decision-support tools were significantly less likely to order antibiotics than those in the control group and prescribed 50% fewer broad-spectrum antibiotics.
Putting mobile devices into doctors’ hands could also help them accept decision support, believes Solomon. Last summer, Scotland’s National Health Service developed an antimicrobial companion app to give practitioners nationwide mobile access to clinical guidance, as well as an audit tool to support boards in gathering data for local and national use.
“The immediacy and the consistency of the input to physicians at the time of ordering antibiotics may significantly help address the problem of overprescribing in ways that less-immediate interventions have failed to do,” Solomon says. In addition, handheld devices with so-called lab-on-a-chip technology could be used to test clinical specimens at the bedside and transmit the data across cellular or satellite networks in areas where infrastructure is more limited.
Artificial intelligence (AI) and machine learning can also become invaluable technology collaborators to help doctors more precisely diagnose and treat infection. In such a system, “the physician and the AI program are really ‘co-prescribing,’” says Solomon. “The AI can handle so much more information than the physician and make recommendations that can incorporate more input on the type of infection, the patient’s physiologic status and history, and resistance patterns of recent isolates in that ward, in that hospital, and in the community.”
Speed Is Everything
Growing bacteria in a dish has never appealed to Dr. James Davis, a computational biologist with joint appointments at Argonne National Laboratory and the University of Chicago Computation Institute. The first of a growing breed of computational biologists, Davis chose a PhD advisor in 2004 who was steeped in bioinformatics technology “because you could see that things were starting to change,” he says. He was one of the first in his microbiology department to submit a completely “dry” dissertation—that is, one that was all digital with nothing grown in a lab.
Upon graduation, Davis wanted to see if it was possible to predict whether an organism would be susceptible or resistant to a given antibiotic, leading him to explore the potential of machine learning to predict AMR.
As the availability of cheap computing power has gone up and the cost of genome sequencing has gone down, it has become possible to sequence a pathogen sample in order to detect its AMR resistance mechanisms. This could allow doctors to identify the nature of an infection in minutes instead of hours or days, says Davis.
Davis is part of a team creating a giant database of bacterial genomes with AMR metadata for the Pathosystems Resource Integration Center (PATRIC), funded by the U.S. National Institute of Allergy and Infectious Diseases to collect data on priority pathogens, such as tuberculosis and gonorrhea.
Because the current inability to identify microbes quickly is one of the biggest roadblocks to making an accurate diagnosis, the team’s work is critically important. The standard method for identifying drug resistance is to take a sample from a wound, blood, or urine and expose the resident bacteria to various antibiotics. If the bacterial colony continues to divide and thrive despite the presence of a normally effective drug, it indicates resistance. The process typically takes between 16 and 20 hours, itself an inordinate amount of time in matters of life and death. For certain strains of antibiotic-resistant tuberculosis, though, such testing can take a week. While physicians are waiting for test results, they often prescribe broad-spectrum antibiotics or make a best guess about what drug will work based on their knowledge of what’s happening in their hospital, “and in the meantime, you either get better,” says Davis, “or you don’t.”
At PATRIC, researchers are using machine-learning classifiers to identify regions of the genome involved in antibiotic resistance that could form the foundation for a “laboratory free” process for predicting resistance. Being able to identify the genetic mechanisms of AMR and predict the behavior of bacterial pathogens without petri dishes could inform clinical decision making and improve reaction time. Thus far, the researchers have developed machine-learning classifiers for identifying antibiotic resistance in Acinetobacter baumannii (a big player in hospital-acquired infection), methicillin-resistant Staphylococcus aureus (a.k.a. MRSA, a worldwide problem), and Streptococcus pneumoniae (a leading cause of bacterial meningitis), with accuracies ranging from 88% to 99%.
Houston Methodist Hospital, which uses the PATRIC database, is researching multidrug-resistant bacteria, specifically MRSA. Not only does resistance increase the cost of care, but people with MRSA are 64% more likely to die than people with a nonresistant form of the infection, according to WHO. Houston Methodist is investigating the molecular genetic causes of drug resistance in MRSA in order to identify new treatment approaches and help develop novel antimicrobial agents.
The Hunt for a New Class of Antibiotics
There are antibiotic-resistant bacteria, and then there’s Clostridium difficile—a.k.a. C. difficile—a bacterium that attacks the intestines even in young and healthy patients in hospitals after the use of antibiotics.
It is because of C. difficile that Dr. L. Clifford McDonald jumped into the AMR fight. The epidemiologist was finishing his work analyzing the spread of SARS in Toronto hospitals in 2004 when he turned his attention to C. difficile, convinced that the bacteria would become more common and more deadly. He was right, and today he’s at the forefront of treating the infection and preventing the spread of AMR as senior advisor for science and integrity in the CDC’s Division of Healthcare Quality Promotion. “[AMR] is an area that we’re funding heavily…insofar as the CDC budget can fund anything heavily,” says McDonald, whose group has awarded $14 million in contracts for innovative anti-AMR approaches.
Developing new antibiotics is a major part of the AMR battle. The majority of new antibiotics developed in recent years have been variations of existing drug classes. It’s been three decades since the last new class of antibiotics was introduced. Less than 5% of venture capital in pharmaceutical R&D is focused on antimicrobial development. A 2008 study found that less than 10% of the 167 antibiotics in development at the time had a new “mechanism of action” to deal with multidrug resistance. “The low-hanging fruit [of antibiotic development] has been picked,” noted a WHO report.
Researchers will have to dig much deeper to develop novel medicines. Machine learning could help drug developers sort through much larger data sets and go about the capital-intensive drug development process in a more prescriptive fashion, synthesizing those molecules most likely to have an impact.
McDonald believes that it will become easier to find new antibiotics if we gain a better understanding of the communities of bacteria living in each of us—as many as 1,000 different types of microbes live in our intestines, for example. Disruption to those microbial communities—our “microbiome”—can herald AMR. McDonald says that Big Data and machine learning will be needed to unlock our microbiomes, and that’s where much of the medical community’s investment is going.
He predicts that within five years, hospitals will take fecal samples or skin swabs and sequence the microorganisms in them as a kind of pulse check on antibiotic resistance. “Just doing the bioinformatics to sort out what’s there and the types of antibiotic resistance that might be in that microbiome is a Big Data challenge,” McDonald says. “The only way to make sense of it, going forward, will be advanced analytic techniques, which will no doubt include machine learning.”
Reducing Resistance on the Farm
Bringing information closer to where it’s needed could also help reduce agriculture’s contribution to the antibiotic resistance problem. Antibiotics are widely given to livestock to promote growth or prevent disease. In the United States, more kilograms of antibiotics are administered to animals than to people, according to data from the FDA.
One company has developed a rapid, on-farm diagnostics tool to provide livestock producers with more accurate disease detection to make more informed management and treatment decisions, which it says has demonstrated a 47% to 59% reduction in antibiotic usage. Such systems, combined with pressure or regulations to reduce antibiotic use in meat production, could also help turn the AMR tide.
Breaking Down Data Silos Is the First Step
Adding to the complexity of the fight against AMR is the structure and culture of the global healthcare system itself. Historically, healthcare has been a siloed industry, notorious for its scattered approach focused on transactions rather than healthy outcomes or the true value of treatment. There’s no definitive data on the impact of AMR worldwide; the best we can do is infer estimates from the information that does exist.
The biggest issue is the availability of good data to share through mobile solutions, to drive HCI clinical-decision support tools, and to feed supercomputers and machine-learning platforms. “We have a fragmented healthcare delivery system and therefore we have fragmented information. Getting these sources of data all into one place and then enabling them all to talk to each other has been problematic,” McDonald says.
Collecting, integrating, and sharing AMR-related data on a national and ultimately global scale will be necessary to better understand the issue. HCI and mobile tools can help doctors, hospitals, and public health authorities collect more information while advanced analytics, machine learning, and in-memory computing can enable them to analyze that data in close to real time. As a result, we’ll better understand patterns of resistance from the bedside to the community and up to national and international levels, says Solomon. The good news is that new technology capabilities like AI and new potential streams of data are coming online as an era of data sharing in healthcare is beginning to dawn, adds McDonald.
The ideal goal is a digitally enabled virtuous cycle of information and treatment that could save millions of dollars, lives, and perhaps even civilization if we can get there. D!
Despite the progress made in some countries, I am also aware of others that are still resistant to digitizing their economy and automating operations. What’s the difference between firms that are digital leaders and those that are slow to mature? From my perspective in working with a variety of businesses throughout Europe, it’s a combination of diversity and technology availability.
European companies are hardly homogenous. Comprising 47 countries across the continent, they serve communities that speak any of 225 spoken languages. Each one is experiencing various stages of digital development, economic stability, and workforce needs.
Nevertheless, as a whole, European firms do prioritize customer acquisition as well as improving efficiency and reducing costs. Over one-third of small and midsize companies are investing in collaboration software, customer relationship management solutions, e-commerce platforms, analytics, and talent management applications. Steadily, business leaders are finding better ways to go beyond data collection by applying predictive analytics to gain real-time insight from predictive analytics and machine learning to automate processes where possible.
Small and midsize businesses have a distinct advantage in this area over their larger rivals because they can, by nature, adopt new technology and practices quickly and act on decisions with greater agility. Nearly two-thirds (64%) of European firms are embracing the early stages of digitalization and planning to mature over time. Yet, the level of adoption depends solely on the leadership team’s commitment.
For many small and midsize companies across this region, the path to digital maturity resides in the cloud, more so than on-premise software deployment. For example, the flexibility associated with cloud deployment is viewed as a top attribute, especially among U.K. firms. This brings us back to the diversity of our region. Some countries prioritize personal data security while others may be more concerned with the ability to access the information they need in even the most remote of areas.
Technology alone does not deliver digital transformation
Digital transformation is certainly worth the effort for European firms. Between 60%–90% of small and midsize European businesses say their technology investments have met or exceeded their expectations – indicative of the steady, powerhouse transitions enabled by cloud computing. Companies are now getting the same access to the latest technology, data storage, and IT resources.
However, it is also important to note that a cloud platform is only as effective as the long-term digital strategy that it enables. To invigorate transformative changes, leadership needs to go beyond technology and adopt a mindset that embraces new ideas, tests the fitness of business models and processes continuously, and allows the flexibility to evolve the company as quickly as market dynamics change. By taking a step back and integrating digital objectives throughout the business strategy, leadership can pull together the elements needed to turn technology investments into differentiating, sustainable change. For example, the best talent with the right skills is hired. Plus, partners and suppliers with a complementary or shared digital vision and capability are onboarded.
The IDC Infobrief confirms what I have known all along: Small and midsize businesses are beginning to digitally mature and maintain a strategy that is relevant to their end-to-end processes. And furthering their digital transformation go hand in hand with the firms’ ability to ignite a transformational force that will likely progress Europe’s culture, social structure, and economy.