Imagine trying to track the movements, spacing, types of plays and the performance of 10 players on a basketball court — for every second of a game. Then add in the precise location of the ball to your data sets. It’s a vast understatement to say how daunting that task might be.
But there’s a system, involving six cameras, that is doing just that in the NBA. And the data those cameras are capturing is taking the league by storm.
SportVU, a company purchased by STATS LLC over four and a half years ago, uses cameras that record 25 frames per second and collects the X and Y coordinates for players along with X, Y and Z coordinates for a basketball. That data is matched with play-by-play logs and put through algorithms created by the company. The result? There’s an entirely new set of stats to pore over, such as how teams space the floor, the way certain players position themselves for rebounds, or how good a defender is at coming to help a teammate.
“It is like going from analyzing monthly returns in financial data to high-frequency sub-millisecond trading,” says Philip Maymin, an assistant professor of Finance and Risk Engineering at NYU-Poly and an analytics consultant to an NBA team. Though the data isn’t available to the general public, experts like Maymin can request it and use it for studies, such as the paper he presented at the MIT Sloan Sports Analytics Conference earlier this year: “Acceleration in the NBA: Towards an Algorithmic Taxonomy of Basketball Plays.”
Last year, 15 teams had SportVU cameras installed around their home courts, but the NBA announced in September that every arena will have the system beginning this fall, proof that the data they produce has had an impact. “At halftime [or] right after the game, you could be accessing this information for simple things,” says Brian Kopp, the head of STATS LLC’s Sports Solutions group. “In the middle of a game, you’re not going to be doing in-depth analysis, but [you know] how many times the ball got into the post and how many times a certain player touched the ball?”
Kopp explains that SportVU can also focus on fitness and training using its detailed statistics to determine how many cuts and sprints a player had in a game — a factor that could help a coach adjust his star’s recovery time between contests.
“People are starting to come around to the fact that there’s other ways to measure the intangibles,” Kopp says. “There’s always going to be that part that you can’t measure about someone’s effort, or their heart, or how they step up in big areas. But there are a lot of the intangibles we can now measure in terms of spacing and impact on the game that haven’t been quantified before.”
In other words, thanks to SportVU, basketball is now a whole new ballgame.
On a recent morning, as I was going through my usual routine, my coffeemaker broke. I cannot live without coffee in the morning, so I immediately looked up my coffeemaker on Amazon and had it shipped Prime in one day. My problem was solved within minutes. My Amazon app, and my loyalty account with that company, was there for me when I needed it most.
It was in this moment that I realized the importance of digital presence for retailers. There is a chance that the store 10 minutes from my house carries this very same coffeemaker; I could have had it in one hour, instead of one day. But the need for immediate access to information pushed me to the online store. My local retailer was not able to be there for me digitally like Amazon.
Retail is still about reading the minds of your customers in order to know what they need and create a flawless experience. But the days of the unconnected shopper in a monochannel world are over. I am not alone in my digital-first mindset; according to a recent MasterCard report, 80% of consumers use technology during the shopping process. I, and consumers like me, use mobile devices as a guide to the physical world.
We don’t need to have an academic discussion about multichannel, omnichannel, and omnicommerce and their meanings, because what it really comes down to for your consumers, or fans, is shopping. And shopping has everything to do with moments in your customers’ lives: celebration moments, in-a-hurry moments, I-want-to-be-entertained moments, and more. Most companies only look for and measure very few moments along the shopping journey, like the moment of coupon download or the moment of sales.
Anticipating these moments was easier when mom and pop stores knew their customers by name. They knew how to be there for their shoppers when, where, and how they wanted it. And shoppers didn’t have any other options. Now it is crucial for companies to understand all of these moments and even anticipate or trigger the right moments for their customers.
In today’s digital economy the way to achieve customer connection is with simple, enjoyable, and personalized front ends that are supported by sophisticated, digital back ends. Then you can use that system to support your customer outreach.
Companies around the world are using creative and innovative methods to find their customers in various moments. Being there for customers comes in many different shapes and forms. Consider these examples:
A Brazilian maker of fashion sunglasses, glasses, and watches, Chilli Beans has a loyal following online and at over 700 locations around the world. Chilli Beans keeps its customers engaged by releasing 10 limited-edition styles each week. If customers like what they see, they have to buy fast or risk missing out.
Online men’s fashion retailer Bonobos reaches its customers with its Guide Shops. While they look like traditional retail outlets, the shops don’t actually sell any clothes. Customers come in for one-on-one appointments with the staff, and if they like anything that they try on, the staff member orders it for them online and it is shipped to their house. The 20 Guide Shops currently open have proven very successful for the company.
Peak Performance, a European maker of outdoor clothing, has added a little magic to its customer experience. It has created virtual pop-up shops that customers can track on their smartphones through CatchMagicHour.com, and they are only available at sunrise and sunset at exact GPS locations. Customers who go to the location, be it at a lighthouse or on top of a mountain, are rewarded with the ability to select free clothing from the virtual shop that they have unlocked on their phones.
Shoes of Prey
The customer experience is completely custom at Shoes of Prey, a website where women can design custom shoes. From fabric to color, the customer picks every element, and then her custom creation is sent directly to her house. Shoes of Prey has even shifted its business model based on customer feedback. Its customers wanted to get inspiration and advice in a physical store. So Shoes of Prey made the move from online-only to omnicommerce and has started to open stores around the world.
While the customer experience for each of these connections is relatively simple – a website, a smartphone, an online design studio – the back end that powers them has to be powerful and nimble at the same time. These sophisticated back ends – powering simple, enjoyable, and personalized front ends – will completely change the game in retail. They will allow companies to engage their customers in ways we can’t even begin to imagine.
Technology will help you be there in the shopping moment. The best technology won’t annoy your customers with irrelevant promotions or pop-up messages. Instead, like a good friend, it will know how to engage with customers and when to leave them alone – how to truly connect with customers instead of manage them. Consequently, customer relationship management as we know it is an outdated technology in the economy of today – and tomorrow. Technologies that go beyond CRM will help retailers to differentiate. Aligning your organization and those technologies will be the Holy Grail to creating true and sustainable customer loyalty.
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About Ralf Kern
Ralf Kern is the Global Vice President, Business Unit Retail, at SAP, responsible for the future direction of SAP’s solution and global Go-to-Market strategy for Omnicommerce Retail, leading them into today’s digital reality.
Today the Internet of Things is revamping technology.
Smart devices speak to each other and work together to provide the end user with a better product experience.
Coinciding with this change in technology is a change in people. We’ve transitioned from a world of people who love processed foods and french fries to people who eat kale chips and Greek yogurt…and actually like it.
People are taking ownership of their well-being, and preventative care is at the forefront of focus for both physicians and patients. Fitness trackers alert wearers of the exact number of calories burned from walking a certain number of steps. Mobile apps calculate our perfect nutritional balance. And even while we sleep, people are realizing that it’s important to monitor vitals.
According to research conducted at Harvard University, proper sleep patterns bolster healthy side effects such as improved immune function, a faster metabolism, preserved memory, and reduced stress and depression.
Conversely, the Harvard study determined that lack of sleep can negatively affect judgement, mood, and the ability retain information, as well as increase the risk of obesity, diabetes, cardiovascular disease, and even premature death.
Through the Internet of Things, researchers can now explore sleep patterns without the usual sleep labs and movement-restricting electrode wires. And with connected devices, individuals can now easily monitor and positively influence their own health.
EarlySense, a startup credited with the creation of continuous patient monitoring solutions focused on early detection of patient deterioration, mid-sleep falls, and pressure ulcers, began with a mission to prevent premature and preventable deaths.
Without constant monitoring, patients with unexpected clinical deterioration may be accidentally neglected, and their conditions can easily escalate into emergency situations.
Motivated by many instances of patients who died from preventable post-elective surgery complications, EarlySense founders created a product that constantly monitors patients when hospital nurses can’t, alerting the main nurse station when a patient leaves his or her bed and could potentially fall, or when a patient’s vital signs drop or rise unexpectedly.
Now EarlySense technology has expanded outside of the hospital realm. The EarlySense wellness sensor, a device connected via the Internet of Things, mobile solutions, and supported by SAP HANA Cloud Platform, monitors all vital signs while a person sleeps. The device is completely wireless and lies subtly underneath one’s mattress. The sensor collects all mechanical vibrations that the patient’s body emits while sleeping, continuously monitoring heart and respiratory rates.
Watch this short video to learn more about how the EarlySense wellness sensor works:
The result is faster diagnoses with better treatments and outcomes. Sleep issues can be identified and addressed; individuals can use the data collected to make adjustments in diet or exercise habits; and those on heavy pain medications can monitor the way their bodies react to the medication. In addition, physicians can use the data collected from the sensor to identify patient health problems before they escalate into an emergency situation.
Connected care is opening the door for a new way to practice health. Through connected care apps that link people with their doctors, fitness trackers that measure daily activity, and sensors like the EarlySense wellness sensor, today’s technology enables people and physicians to work together to prevent sickness and accidents before they occur. Technology is forever changing the way we live, and in turn we are living longer, healthier lives.
To learn how SAP HANA Cloud Platform can affect your business, visit It&Me.
The Digitalist Magazine is your online destination for everything you need to know to lead your enterprise’s digital transformation.
Read the Digitalist Magazine and get the latest insights about the digital economy that you can capitalize on today.
About Christine Donato
Christine Donato is a Senior Integrated Marketing Specialist at SAP. She is an accomplished project manager and leader of multiple marketing and sales enablement campaigns and events, that supported a multi million euro business.
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.