Informatyka w Firmie Archive


How IoT Data Ecosystems Will Transform B2B Competition

Former Cisco CEO John Chambers got it mostly right when he said that every company today is a technology company. In fact, every company is becoming a technology and data company, and the consequences of this distinction are substantial.
The real value of the Internet of Things (IoT) lies in the data it serves up and the insights that result. Much has been written about how IoT is unlocking significant value for companies by enabling smart factories and connected supply chains as well as the ability to monitor products and deliver new services. But IoT isn’t just changing how companies operate; it’s changing the very nature of their businesses. In asset-heavy industries, the proliferation of IoT data is fundamentally shifting the customer value proposition from goods to services, and this shift is leading companies to adopt new business models that require new capabilities.
The majority of IoT solutions today are built around internal applications such as predictive maintenance, factory optimization, supply chain automation, and improved product design. But to fully capture the value of their IoT data, B2B companies need to think beyond their own walls. By collaborating with new business partners, including industry incumbents and players in other sectors, companies can form new data ecosystems. These ecosystems give their participants access to valuable collective data assets as well as the capabilities and domain expertise necessary to develop the assets into new data-driven products and services.
Data ecosystems will play a critical role in defining the future of competition in many B2B industries. They enable companies to build data businesses, which are valuable not only because they generate high-margin recurring revenue streams but also because they create competitive advantage. New data-driven products and services deliver unique value propositions that extend beyond a company’s traditional hardware products, deepening customer relationships and raising barriers to entry. They also build highly defensible positions, thanks to natural monopolies rooted in economies of scale and scope (similar to monopolies based on proprietary IP or trade secrets). Companies that secure advantaged positions in data ecosystems will generate significant value and competitive advantage across their entire business, including their traditional hardware offerings.

Digital ecosystems—networks of companies, consumers, customers, and others that interact to create mutual value—have enabled some of the most profitable and valuable business models that exist today. (See “Getting Physical: The Rise of Hybrid Ecosystems,” BCG article, September 2017, and “The Age of Digital Ecosystems: Thriving in a World of Big Data,” BCG article, July 2013.) In fact, the five most valuable public companies in the US (at the time of publishing)—Apple, Google, Microsoft, Facebook, and Amazon—are all orchestrators of digital ecosystems. These digital leaders have built platform-based business models that capitalize on the winner-take-all dynamic of ecosystem competition to reach enormous scale and establish dominant positions.

These orchestrators exploit three factors:

  • They scale up rapidly, capitalizing on virtually zero marginal production costs, network effects, and low barriers to geographical expansion (in the absence of protectionism).
  • They take advantage of the “data flywheel effect”; digital ecosystems enable unprecedented data accumulation and analysis, fueling improvements to products and business processes and stimulating further growth and data access.
  • And ecosystems are able to provide seamless and comprehensive digital experiences for customers by organizing business partners on a single platform to satisfy multiple customer needs. They thereby lock in customers and capture a greater portion of their attention, time, and value.


By Massimo Russo and Michael Albert


Competing on the Rate of Learning

New technologies, particularly artificial intelligence, have the potential to propel the rate of learning in business to new heights—the volume and velocity of data have exploded, and algorithms can unlock complex patterns and insights with unprecedented speed. In an era of shrinking product life cycles and rapidly changing business models, the companies that are the first to decode new trends or emerging needs have the best chance to take advantage of them.

But learning at the speed of algorithms requires more than algorithms themselves. New technology can accelerate learning in individual process steps, but to create aggregate organizational learning and competitive advantage it must be complemented by organizational innovation. Moreover, slow-moving contextual shifts, driven by social, political, and economic forces, are becoming just as important to business as fast-moving technologies. To compete on the ability to learn, therefore, leaders must reinvent their organizations to leverage both human and machine capabilities synergistically in order to expand learning to both faster and slower timescales.

A Brief History of Learning Organizations

In first-generation learning organizations, businesses learned how to execute existing processes more efficiently—best exemplified by the “experience curve.” As Bruce Henderson observed half a century ago, firms tend to reduce their costs at a constant and predictable rate as their cumulative experience increases. For example, in the early 20th century costs of the Model T consistently fell by about 25% every time the cumulative product volume doubled.

In this model, learning was a game of continuous improvement aimed at reducing marginal costs. Competing on learning was essentially about building volume, and therefore experience, faster than competitors. This permitted a strategy of pricing for the anticipated value of learning and pursuing cost reductions systematically, using mechanisms such as statistical process control, kaizen, Six Sigma, and quality circles.

In recent years, a second-generation concept of learning came to the forefront: learning how to envision and create new products. In other words, companies must learn not only to descend experience curves but also to “jump” from one curve to another.

This second dimension of learning has always existed in business, but its importance has grown. Technological innovation has compressed product life cycles, so new learning curves appear before old ones have fully played out—and firms must balance both dimensions of learning at the same time. For example, Netflix jumped from a DVD rental business to a streaming service to in-house content creation, while expanding to 190 countries, in less than a decade.

Today, a third phase of the learning game is beginning to unfold. Modern technologies, such as sensors, digital platforms, and AI, promise to massively accelerate the rate at which information is generated, gathered, and processed. This potentially enables companies to operate at superhuman speed, learning about the market and reacting in seconds or even milliseconds.

At the same time, however, companies must also expand their learning abilities to consider longer timescales, as social, political, and economic shifts gradually reshape the business context. Most businesses have woken up to the reality of time compression, but this is only half the picture. The range of timescales that need to be considered is being stretched in both directions. A third-generation learning organization is one that can embrace this new reality—adopting algorithmic principles over shorter timescales while adapting to nonbusiness forces that operate over longer ones.

To make this leap, businesses cannot rely on technological sophistication alone. Repeating a well-established historical pattern, evolution of the organizational model is needed to unlock the potential of new technologies. The original experience curve could be exploited only when new industrial technologies were complemented by organizational innovations like new factory layouts, redefined roles for workers (such as the assembly line), and new managerial approaches like quality circles and kanban. In the same way, to build the third generation of learning organizations, leaders must reinvent the enterprise not only to unlock the potential of new technologies but also to synergistically combine the unique learning capabilities and timescale advantages of both humans and technology—in other words, to build effective “human + machine” machines.


The third generation of learning organizations presents an enormous opportunity. Companies can unleash both the power of technology for rapid learning and human ingenuity on longer timescales. But this will require leaders first to reimagine the organization and how it is managed.

More: BCG By Martin Reeves and Kevin Whitaker


Intelligent process automation

Full intelligent process automation comprises five key technologies. Here’s how to use them to enhance productivity and efficiency, reduce operational risks, and improve customer experiences.

Since the financial crisis of 2007–09, many companies have applied lean management to improve cost efficiencies, customer satisfaction, and employee engagement simultaneously, and many programs have achieved substantial impact on all dimensions. Progress on digital, however, has been more uneven. In the insurance sector, for example, an October 2016 FIS study found that 99.6 percent of insurers surveyed admitted they face obstacles in implementing digital innovation, while 80 percent recognize they need digital capabilities to meet business challenges. This difficulty has been compounded by the boom in “insurtech” investments in 2016—topping $3.5 billion in funding across 111 deals since 2015.

As macroeconomic conditions continue to put pressure on profit margins across sectors, cost productivity and unlocking new value are back at the top of the senior-management agenda. The question is, what else can be done?

That’s where intelligent process automation (IPA) comes in. We believe it will be a core part of companies’ next-generation operating models. Many companies across industries have been experimenting with IPA, with impressive results:

  • Automation of 50 to 70 percent of tasks . . .
  • . . . which has translated into 20 to 35 percent annual run-rate cost efficiencies . . .
  • . . . and a reduction in straight-through process time of 50 to 60 percent . . .
  • . . . with return on investments most often in triple-digit percentages.

New technologies that promise double-digit or even triple-digit same-year returns should rightfully be viewed with skepticism. But our experience shows that the promise of IPA is real if executives carefully consider and understand the drivers of opportunity and incorporate them effectively with the other approaches and capabilities that drive the next-generation operating model. (For more on these approaches and capabilities, please read “The next-generation operating model for the digital world.”)


Shaking up the value chain

Data and digitization are creating a growing array of value-creation choices in industries as diverse as pharmaceuticals, mining, and energy.

During the 1980s, McKinsey’s Fred Gluck and Harvard Business School professor Michael Porter began writing about the interrelated activities through which companies create value for their customers. Executives have always had choices about how to perform the activities in this “business system” (Gluck’s words) or “value chain” (Porter’s). In the digital age, as information disrupts the nature of value creation in many industries, the range of choices available to senior business leaders has increased. For example, digital platforms in the pharmaceutical industry now make it possible to aggregate massive amounts of data on diseases—potentially accelerating the discovery and design of new drugs and challenging the industry’s legacy processes. In energy production and mining, although companies have long outsourced some functions in efforts to drive down costs, digital requires a new approach. Using data, suppliers can offer incumbents an expanded range of capabilities and productivity gains—alluring possibilities that are accompanied by the risk that sharing too much data could shut off areas of future growth. This type of flux in value chains will only intensify across industries, forcing leaders to grapple with existential questions about core competitive strengths in an environment where destabilizing technologies will be the norm.

Will digital platforms transform pharmaceuticals?

Start-up companies are combining genetic information and new therapies to transform drug discovery and development—at greater speed and scale.

Product innovation is at the heart of the pharmaceutical industry’s value chain. Long, capital-intensive development cycles and legacy processes, though, have made it difficult to exploit the full potential of emerging digital technologies to deliver faster, more agile approaches to discover and develop new drugs. Indeed, McKinsey research shows that the industry’s digital maturity lags that of most other industries.

A new current is forming in one area of the industry: start-up companies that are creating biomolecular platforms around cellular, genetic, and other advanced therapies.1 The platforms marshal vast amounts of data on the genetics of diseases, such as cancer, and combine that with patients’ genetic profiles and related data. They zero in on key points along the information chain—for example, where there are linkages between DNA and proteins, and then cells—to “design” new drugs. Much like software developers, the platforms engineer disease therapies built upon the “code-like” DNA and RNA sequences within cells (Exhibit 1).

These techniques have significant implications for the treatment of many life-threatening illnesses that are outside the reach of standard therapeutic approaches. They could also disrupt the industry’s value chain as they speed up drug discovery and development, with the potential for a single platform to scale rapidly across a range of diseases (Exhibit 2).

In one example of a biomolecular platform, for a disease that results from a mutation in DNA that codes for a needed enzyme, the platform models the disease from medical and genetic data to arrive at an enzyme “optimized” to correct for the mutation. The platform then designs a sequence of genetic material to treat the disease, as well as a delivery vehicle to get it to the target cells. In another example, for CAR-T2 therapies, the platform modifies a patient’s T cells (an immune-system cell), which are then deployed to attack a cancer.

A new competitive landscape

Optimized biomolecular platforms have the potential to accelerate the early stages of R&D significantly. For example, it can take as little as weeks or months to go from concept to drug versus what’s often many months, if not years, of trial and error under conventional discovery methods. This is achieved by routinizing key steps (such as preparing a drug for preclinical testing) and using common underlying elements in the design of the drug (such as drug-delivery vehicles that are similar). In the past five years or so, a number of start-ups have formulated dozens of drugs that are in clinical trials and, in some cases, drugs that have already been approved. The large information base behind therapies helps identify the right targets for preclinical and clinical trials.

By Olivier Leclerc and Jeff Smith



Do 2022 roku technologie cyfrowe zrewolucjonizują sektor opieki medycznej


Dzięki urządzeniom mobilnym rośnie świadomość wśród pacjentów

Średnia długość życia w krajach OECD wynosi ponad 80 lat i stale się wydłuża. Jednak wraz z nią wzrasta liczba osób cierpiących na choroby przewlekłe. W Europie stanowią one aż 77 proc wszystkich schorzeń. To powoduje coraz większe zapotrzebowanie na ochronę zdrowotną. Jak pokazuje raport „A journey towards smart health. The impact of digitalization on patient experience”, przygotowany przez firmę doradczą Deloitte, największym wyzwaniem dla systemów ochrony zdrowia jest zapewnienie pacjentom wysokiej jakości usług i dostępu do opieki, przy jednoczesnym efektywnym zarządzaniu kosztami oraz postępem technologicznym. Coraz bardziej popularne staną się e-wizyty lekarskie, e-recepty oraz monitorowanie stanu zdrowia za pomocą urządzeń mobilnych.

Wraz ze zmianami gospodarczymi, starzeniem się populacji i częstszym występowaniem chorób przewlekłych, sektor opieki zdrowotnej na świecie odnotował gwałtowny wzrost kosztów. W rezultacie specjaliści z branży medycznej musieli się dostosować i skupić na wydajności oraz jakości świadczonych usług czy produkcji. Technologie cyfrowe stanowią rozwiązanie tego problemu. Nie tylko pacjenci, ale także firmy z sektora opieki medycznej mogą wiele skorzystać na innowacyjnych rozwiązaniach.

Starzejące się społeczeństwo wyzwaniem dla systemu zdrowia

Dzięki rosnącemu standardowi i poprawie stylu życia, lepszej edukacji oraz zwiększonemu dostępowi do usług zdrowotnych ludzie żyją coraz dłużej. Średnia długość życia w krajach OECD przekracza 80 lat i cały czas się wydłuża. Jednak wraz z długowiecznością wzrasta liczba osób cierpiących na choroby przewlekłe, a co za tym idzie, zapotrzebowanie na instytucje opieki zdrowotnej.

Technologia a opieka medyczna

Wpływ technologii cyfrowych na podejście do zdrowia pacjenta

Choroby przewlekłe, takie jak nowotwory, cukrzyca, problemy sercowo-naczyniowe i układu oddechowego, stanowią 77 proc. chorób w Europie i odpowiadają za 86 proc. wszystkich zgonów. Występują one u ponad 80 proc. osób w wieku powyżej 65 lat. Szacuje się, że w Unii Europejskiej roczne wydatki na choroby przewlekłe wynoszą około 700 miliardów euro, co średnio stanowi 70-80 proc. całkowitych wydatków zdrowotnych jednego kraju. Wzrost liczby tego typu chorób powoduje coraz większy nacisk na opiekę zdrowotną i systemy społeczne w UE. Obecnie największym wyzwaniem dla systemów ochrony zdrowia jest zapewnienie pacjentom wysokiej jakości usług i dostępu do opieki zdrowotnej przy jednoczesnym efektywnym zarządzaniu kosztami oraz postępem technologicznym – zaznacza Maciej Dalecki, Wicedyrektor w Dziale Doradztwa Finansowego Deloitte.

Nowe podejście – większe możliwości

Oczekuje się, że do 2022 roku medycyna będzie w pełni przewidywalna, prewencyjna, spersonalizowana i partycypacyjna (ang. P4 Medicine). Innowacyjna biotechnologia, możliwość wglądu w genetykę człowieka, precyzja w diagnozie a także spersonalizowana medycyna znacząco zmienią opiekę zdrowotną. Dynamiczny wzrost opieki opartej o technologie wymaga od sektora ochrony zdrowia ponownego zdefiniowania ról i obowiązków pracowników oraz dostosowania się do nowego sposobu pracy.

W najbliższych latach powszechne staną się e-wizyty, e-recepty, a także śledzenie postępu choroby, diagnozowanie i leczenie za pomocą zdalnego monitorowania cyfrowego, co w znacznym stopniu pomoże zoptymalizować czas personelu – wyjaśnia Oliver Murphy, Partner, Lider ds. Sektora Farmaceutycznego i Opieki Zdrowotnej w Deloitte.
Sektor będzie odchodził od modelu postrzegania pacjenta jako przeciętnego petenta, skupiając się na konkretnym przypadku. W nowym modelu lekarze będą koncentrować się na aktywnym promowaniu dobrego stanu zdrowia i zapobiegania chorobom, zamiast ich leczeniu – dodaje.

Według raportu Deloitte 74 proc. pacjentów deklaruje, że udziela różnym pracownikom sektora ochrony zdrowia tych samych informacji. Co więcej, 60 proc. ankietowanych wielokrotnie wykonuje te same testy. Innowacyjne rozwiązania mogą usprawnić zarządzanie danymi pacjentów, zmniejszając tym samym znaczące ryzyko powielania lub zaniedbywania zbieranych informacji. Docelowo lekarzom będzie się płacić nie za liczbę wizyt czy przeprowadzonych testów (tzw. opłata za usługę), a za opiekę opartą na wartości (ang. Value-based Care). Przede wszystkim cenione będą wydajność i efektywność.

Niezastąpiona rola pacjenta

Aplikacje mobilne i urządzenia do noszenia na ciele (smartwatch, okulary smart itd.) zapewniają przyjazne dla użytkownika rozwiązania 24 godziny na dobę. Dzięki dostępności nowych technologii sektor może zachęcać pacjentów do edukacji i ciągłego monitorowania swojego stanu zdrowia oraz promować działania zapobiegawcze w sposób efektywny kosztowo. Pomoże to również lekarzom w zbudowaniu bardziej świadomej bazy pacjentów.

Tylko w 2017 roku powstało 78 tysięcy nowych mobilnych aplikacji zdrowotnych, co zwiększyło ich ogólną ofertę do 325 tysięcy. Liczba aplikacji poświęconych opiece zdrowotnej znacząco wzrosła w szczególności na dwóch wiodących platformach mobilnych. System iOS w 2017 roku odnotował wzrost o 20 proc. w ujęciu rocznym, a w przypadku Androida liczba ta zwiększyła się nawet o 50 proc. Aplikacje zdrowotne zachęcają właścicieli smartfonów do monitorowania stanu zdrowia i lepszego nim zarządzania. Nawet 57 proc. badanych używa elektronicznego urządzenia do śledzenia różnych wskaźników swojego stanu zdrowia. Mimo to, tylko jedna trzecia z nich udostępnia te informacje swojemu lekarzowi – podsumowuje Maciej Dalecki.

Cyfryzacja opieki medycznej w Polsce

Digitalizację opieki medycznej w Polsce najwyraźniej widać w sektorze farmaceutycznym. Zmienia się rola aptek, które muszą dostosować się zarówno do postępującej cyfryzacji, jak i rosnących oczekiwań klientów. Pierwsze efekty zobaczymy do końca roku, kiedy wejdzie zapowiadana możliwość realizacji e-recept. Jak pokazuje ostatni raport Deloitte pt.: „Jak wprowadzić w Polsce opiekę farmaceutyczną: Rola i wyzwania współczesnej apteki” elektroniczne recepty zastąpią recepty papierowe, bo będą wygodne zarówno dla pacjenta, jak i dla lekarzy. Będzie można je wystawić bez osobistego zbadania pacjenta, np. po konsultacji za pośrednictwem internetu lub telefonu. Prognozujemy, że e-recepta stanie się nie tylko kluczowym narzędziem wymiany informacji między pacjentem, lekarzem a farmaceutą, ale również fundamentem systemu opieki farmaceutycznej. E-recepty będą też istotnym narzędziem skracającym czas oczekiwania na wizytę lekarską, który jest w Polsce ponad trzykrotnie dłuższy niż średnia krajów OECD  – zaznacza Rafał Rudzki, Starszy Menedżer w Zespole ds. Zrównoważonego Rozwoju w Polsce i  Europie Środkowej, Deloitte.

Daniel Dziula