CRM Archive

0

Why data culture matters

Organizational culture can accelerate the application of analytics, amplify its power, and steer companies away from risky outcomes. Here are seven principles that underpin a healthy data culture.

Revolutions, it’s been remarked, never go backward. Nor do they advance at a constant rate. Consider the immense transformation unleashed by data analytics. By now, it’s clear the data revolution is changing businesses and industries in profound and unalterable ways.

But the changes are neither uniform nor linear, and companies’ data-analytics efforts are all over the map. McKinsey research suggests that the gap between leaders and laggards in adopting analytics, within and among industry sectors, is growing. We’re seeing the same thing on the ground. Some companies are doing amazing things; some are still struggling with the basics; and some are feeling downright overwhelmed, with executives and members of the rank and file questioning the return on data initiatives.

For leading and lagging companies alike, the emergence of data analytics as an omnipresent reality of modern organizational life means that a healthy data culture is becoming increasingly important. With that in mind, we’ve spent the past few months talking with analytics leaders at companies from a wide range of industries and geographies, drilling down on the organizing principles, motivations, and approaches that undergird their data efforts. We’re struck by themes that recur over and again, including the benefits of data, and the risks; the skepticism from employees before they buy in, and the excitement once they do; the need for flexibility, and the insistence on common frameworks and tools. And, especially: the competitive advantage unleashed by a culture that brings data talent, tools, and decision making together.
The experience of these leaders, and our own, suggests that you can’t import data culture and you can’t impose it. Most of all, you can’t segregate it. You develop a data culture by moving beyond specialists and skunkworks, with the goal of achieving deep business engagement, creating employee pull, and cultivating a sense of purpose, so that data can support your operations instead of the other way around.

In this article, we present seven of the most prominent takeaways from conversations we’ve had with these and other executives who are at the data-culture fore. None of these leaders thinks they’ve got data culture “solved,” nor do they think that there’s a finish line. But they do convey a palpable sense of momentum. When you make progress on data culture, they tell us, you’ll strengthen the nuts and bolts of your analytics enterprise.

That will not only advance your data revolution even further but can also help you avoid the pitfalls that often trip up analytics efforts. We’ve described these at length in another article and have included, with three of the seven takeaways here, short sidebars on related “red flags” whose presence suggests you may be in trouble—along with rapid responses that can mitigate these issues. Taken together, we hope the ideas presented here will inspire you to build a culture that clarifies the purpose, enhances the effectiveness, and increases the speed of your analytics efforts.

Rob Casper, chief data officer, JPMorgan Chase

Ibrahim Gokcen, chief digital officer, A.P. Moller – Maersk

Cameron Davies, head of corporate decision sciences, NBCUniversal (NBCU)

Jeff Luhnow, general manager, Houston Astros

Takehiko (“Tak”) Nagumo, managing executive officer, Mitsubishi UFJ Research and Consulting (MURC); formerly executive officer and general manager, corporate data management, Mitsubishi UFJ Financial Group (MUFG)

Ted Colbert, CIO, Boeing

Jeff Luhnow, Houston Astros

More: www.mckinsey.com/business-functions/

By Alejandro Díaz, Kayvaun Rowshankish, and Tamim Saleh

 

0

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

0

Blockchain beyond the hype: What is the strategic business value?

Companies can determine whether they should invest in blockchain by focusing on specific use cases and their market position.

Speculation on the value of blockchain is rife, with Bitcoin—the first and most infamous application of blockchain—grabbing headlines for its rocketing price and volatility. That the focus of blockchain is wrapped up with Bitcoin is not surprising given that its market value surged from less than $20 billion to more than $200 billion over the course of 2017.1 Yet Bitcoin is only the first application of blockchain technology that has captured the attention of government and industry.

0

Jak odnieść sukces w dobie transformacji cyfrowej?

Firmy z sektora TMT będą motorem napędowym transformacji cyfrowej światowej gospodarki.

Co różni liderów z sektora TMT (Technologia / Media / Telekomunikacja) od reszty firm? To, że są przygotowani na nadchodzącą czwartą rewolucję przemysłową (Industry 4.0). Innowacje są dla nich priorytetem, zwiększają swoje możliwości biznesowe dzięki strategicznym partnerstwom i opracowują strategie cyfrowe przyszłości. Dlatego to firmy z sektora TMT mają szanse być najdynamiczniej rozwijającymi się i stać się liderami pionierami rozwiązań digitalowych.

TMT i Industry 4.0. Czwarta rewolucja przemysłowa nabiera rozpędu. Pierwsze trzy dotyczyły odpowiednio mechanizacji, produkcji masowej i komputeryzacji / automatyzacji; czwarta, czyli Industry 4.0, to mariaż technologii, tych fizycznych i cyfrowych. Tak jak poprzednie, oznacza ona całkowitą zmianę i przedefiniowanie metod funkcjonowania organizacji tworzących wartość dla klientów.

Tym razem jednak prędkość zachodzących zmian znacząco przewyższa poprzednie dzięki inteligentnym technologiom sieciowym, rozwijającym się w tempie wykładniczym. Innowacje te, obejmujące przetwarzanie danych w chmurze, platformy cyfrowe, big data i analitykę, rozwiązania mobilne, sieci społecznościowe, oprogramowanie do pracy grupowej, Internet Rzeczy (IoT) i sztuczną inteligencję (AI) – napędzają i przyspieszają nadejście transformacji cyfrowej. Dzięki technologii całkowicie zmienia się sposób działania firm, opracowywania innowacji i tworzenia produktów i usług. Innowacje zachęcają do eksperymentowania z nowymi modelami biznesowymi i sposobami oferowania nowych wartości klientom.  W szerszej perspektywie zacierają się granice między poszczególnymi branżami, a powiązania między partnerami biznesowymi ulegają przekształceniu. Liczba firm nieprzygotowanych do czwartej rewolucji jest jednak ciągle duża. Najnowsze badanie Deloitte Industry 4.0, obejmujące członków zarządów firm z całego świata, wykazało, iż we wszystkich sektorach zaledwie 14% członków naczelnego kierownictwa ma „dużą pewność”, że ich firmy są przygotowane do wdrożenia zmian związanych z nadejściem nowej cyfrowej ery.

– Co szósty ankietowany pracujący w sektorze TMT uważa, że w obecnych czasach kluczowym dla konkurencyjności organizacji jest wdrażanie nowych technologii wspierających biznes. „Liderzy TMT wierzą, że Mobile, Cloud Computing, IoT czy AI będą kluczowe w najbliższych pięciu latach. Dlaczego? Bo to właśnie dzięki nim zaciera się granica między offline i online. Co oznacza, że organizację mogą pozyskiwać i przetwarzać większe ilości danych, a co za tym idzie szybciej i mądrzej się rozwijać” – Jan Michalski, Partner, Deloitte

Firmy z sektora technologii, mediów i telekomunikacji (TMT) znajdują się w epicentrum wstrząsu wywołanego przez Industry 4.0. Są pionierami zastosowania inteligentnych technologii i szybkiej transmisji danych, a często twórcami cyfrowych innowacji. Aby zrozumieć, w jaki sposób firmy te reagują na zmiany, nowe możliwości i wyzwania nowej ery, stworzone zostało badanie Deloitte Industry 4.0. Jak pokazują wyniki, nawet te firmy nie są w 100% gotowe na nowe. Zaledwie jedna piąta przebadanych firm jest „całkowicie przygotowana” do wdrożenia nowych modeli biznesowych i technologii autonomicznych. Raport pozwala jednak zidentyfikować liderów z sektora TMT, którzy są gotowi na rewolucyjne zmiany, związane z Industry 4.0. Pionierzy ci są głęboko przekonani, że nowe technologie dają nową wartość ich klientom.  Dzięki temu, są nie tylko dobrze przygotowani na zmiany, ale mają pewność osiągnięcia dobrych wyników finansowych. Liderzy sektora TMT wyróżniają się spośród pozostałych firm, dzięki wykorzystaniu technologii cyfrowych w procesach biznesowych. Liderzy nie działają w pojedynkę, ciągle poszukują partnerów do opracowywania nowych, efektywniejszych modeli biznesowych.

Metodologia

Aby uzyskać obraz tego, w jaki sposób liderzy i organizacje z sektora TMT przygotowują się na zmiany wynikające z czwartej rewolucji przemysłowej, przeanalizowane zostały dane z badania „The Fourth Industrial Revolution is here—are you ready”. Badanie zostało przeprowadzone w 2017 roku przez Deloitte przy współpracy z Forbes Insights. W badaniu wzięło 1603 dyrektorów oraz managerów wyższego szczebla z całego świata. Sektor TMT reprezentowało 416 respondentów z 19 krajów z obu Ameryk, Azji oraz Europy. 35% Ankietowanych związanych jest z branżą technologiczną, 34 procent z telekomunikacyjną i 31 procent z mediami. Wszyscy ankietowani to dyrektorzy w tym: CTO (19% ankietowanych), CIO (18% ankietowanych) i prezesi/dyrektorzy zarządzający (18% ankietowanych), CFO (1518% ankietowanych), CMO (15% ankietowanych) oraz COO (15% ankietowanych).

0

EY Tax alert

Report on recent US international tax developments. The United States (US) Internal Revenue Service (IRS) on 4 June issued a news release (IR-2018-131) announcing that certain late-payment penalties relating to the Internal Revenue Code Section 965 repatriation transition tax will be waived, and providing additional information for individuals subject to the transition tax regarding the due date for relevant elections. The relief is explained in three new FAQs posted on the IRS’ tax reform page, supplementing 14 existing questions and answers that provide details on reporting and paying the tax.

The IRS announced that, in some cases, the IRS will waive estimated tax penalties for taxpayers subject to the transition tax who improperly attempted to apply a 2017 calculated overpayment to their 2018 estimated tax, if all required estimated tax payments are made by 15 June 2018. In addition, the IRS will waive the late payment penalty for individual taxpayers who missed the 18 April 2018 deadline, if the installment is paid in full by 15 April 2019. This relief is only available if the total transition tax liability is less than US$1 million.

….

EY’s new Tax News Update: Global  Edition is a free, personalized email subscription service that allows you to receive EY Global Tax Alerts, newsletters, events, and thought leadership published across all areas of tax. For additional information with respect to this Alert, please contact the following:

Ernst & Young LLP, International Tax Services, Washington, DC

Arlene Fitzpatrick arlene.fitzpatrick@ey.com;

Joshua Ruland joshua.ruland@ey.com

More: Global Tax alert library in https://www.ey.com