Informatyka w Firmie Archive

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Kedro, McKinsey’s first open-source software tool

 

QuantumBlack, the advanced analytics firm we acquired in 2015, has now launched Kedro, an open source tool created specifically for data scientists and engineers. It is a library of code that can be used to create data and machine-learning pipelines. For our non-developer readers, these are the building blocks of an analytics or machine-learning project. “Kedro can change the way data scientists and engineers work,” explains product manager Yetunde Dada, “making it easier to manage large workflows and ensuring a consistent quality of code throughout a project.”

McKinsey has never before created a publicly available, open source tool. “It represents a significant shift for the firm,” notes Jeremy Palmer, CEO of QuantumBlack, “as we continue to balance the value of our proprietary assets with opportunities to engage as part of the developer community, and accelerate as well as share our learning.”

The name Kedro, which derives from the Greek word meaning center or core, signifies that this open-source software provides crucial code for ‘productionizing’ advanced analytics projects. Kedro has two major benefits: it allows teams to collaborate more easily by structuring analytics code in a uniform way so that it flows seamlessly through all stages of a project. This can include consolidating data sources, cleaning data, creating features and feeding the data into machine-learning models for explanatory or predictive analytics.

More: www.mckinsey.com; https://github.com/quantumblacklabs/kedro

  What are the main features of Kedro?

1. Project template and coding standards

  • A standard and easy-to-use project template
  • Configuration for credentials, logging, data loading and Jupyter Notebooks / Lab
  • Test-driven development using pytest
  • Sphinx integration to produce well-documented code

2. Data abstraction and versioning

  • Separation of the compute layer from the data handling layer, including support for different data formats and storage options
  • Versioning for your data sets and machine learning models

3. Modularity and pipeline abstraction

  • Support for pure Python functions, nodes, to break large chunks of code into small independent sections
  • Automatic resolution of dependencies between nodes
  • (coming soon) Visualise your data pipeline with Kedro-Viz, a tool that shows the pipeline structure of Kedro projects

Note: Read our FAQs to learn how we differ from workflow managers like Airflow and Luigi.

4. Feature extensibility

  • A plugin system that injects commands into the Kedro command line interface (CLI)
  • List of officially supported plugins:
    • (coming soon) Kedro-Airflow, making it easy to prototype your data pipeline in Kedro before deploying to Airflow, a workflow scheduler
    • Kedro-Docker, a tool for packaging and shipping Kedro projects within containers
  • Kedro can be deployed locally, on-premise and cloud (AWS, Azure and GCP) servers, or clusters (EMR, Azure HDinsight, GCP and Databricks)

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Six governing considerations to modernize marketing

Most chief marketing officers (CMOs) understand that the utilization of data, analyses, and algorithms to personalize marketing drives value. Concept tests are becoming more efficient, customer approaches are being accelerated, and revenues are quadrupling in certain channels (Exhibit 1). All the evidence suggests that marketing functions should invest in, collect, and analyze available data to support their decision making.

We strive to provide individuals with disabilities equal access to our website. If you would like information about this content we will be happy to work with you. Please email us at: McKinsey_Website_Accessibility@mckinsey.com

No wonder, then, that one in three CMOs is driving a digitization initiative with high personal involvement, according to a McKinsey survey. Despite notable successes, digital marketing has often stalled in a trial phase for years in many companies. Why is that? We find that the managers responsible often blame it on culture and legacy behavioral patterns (Exhibit 2). These soft factors lie far ahead of technical issues, such as IT infrastructure and data availability, which is not surprising. It is easy enough to buy a new server for the customer database, and even new customer-relationship-manager software is quickly installed. But how does one change the attitudes and behaviors of those who use the technology?

We strive to provide individuals with disabilities equal access to our website. If you would like information about this content we will be happy to work with you. Please email us at: McKinsey_Website_Accessibility@mckinsey.com Based on our experience from a multitude of digital engagements, modernizing the marketing organization to unlock the full potential of the digital revolution requires business leaders to address six considerations.

1. How to centralize guidance and oversight

2. How to bring together marketing and IT (heart and brain)

3. How to build collaboration and agility

4. How to reinvent HR to meet talent demands

5. How to build flexibility into resource planning

6. How to make cultural change a continuous task

Modernizing marketing is a process that relies on multiple factors for success. Only by understanding what these are and by focusing on how to address them can marketers hope to get real value from digital. An earlier version of this article was published in the December 2018 issue of McKinsey’s German-language consumer journal Akzente.

About the authors: Patrick Guggenberger is a consultant in McKinsey’s Vienna office, Miriam Lobis is a partner in the Berlin office, and Patrick Simon and Kai Vollhardt are partners in the Munich office.

More: www.mckinsey.com/industries/

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Ericsson and SoftBank will start the technology 5G in Japan

Operators are seeking solutions to both support traffic growth as well as emerging use cases and business opportunities. To assist operators with these challenges and opportunities, Ericsson 5G radio access technologies are being created to provide the infrastructure needed to support the world’s growing demand for high-bandwidth connections and support the real-time, high-reliability communication requirements of mission-critical applications.

The specification of 5G will also include the development of a new flexible air interface, NR, which will be directed to extreme mobile broadband deployments. NR will also target high-bandwidth and high-traffic-usage scenarios, as well as new scenarios that involve mission-critical and real-time communications with extreme requirements in terms of latency and reliability. Ericsson is extending the Ericsson Radio System to deliver new radio access products and functionality to smooth the operator’s transformation journey to 5G. Ericsson Radio System extensions deliver a high performance, end-to-end 5G access system which includes the industry’s first global portfolio of 5G NR radios. Ericsson is also first to market with solutions that enable 4G LTE networks to evolve smoothly on the journey to 5G, such as Ericsson’s new 5G platform for combined core and radio use cases. The platform comprises the 5G core, radio and transport portfolios, together with digital support systems, transformation services and security.

Ericsson i SoftBank wprowadzą w Japonii sieć 5G

  • Ericsson dostarczy firmie SoftBank sieć dostępu radiowego pracującą w średnim i wysokim paśmie 5G, a także rozbuduje obecną sieć LTE
  • Wybór firmy Ericsson jako dostawcy technologii 5G to efekt wspólnych działań w zakresie weryfikacji koncepcji
  • Obie firmy planują dalsze prace nad rozwojem wykorzystania technologii 5G

Firma Ericsson (NASDAQ: ERIC) została wybranym przez SoftBank dostawcą technologii 5G w celu wdrożenia wielopasmowej sieci 5G w Japonii, po skutecznym przeprowadzeniu wspólnych działań w zakresie weryfikacji koncepcji, które trwają od 2015 r.

Na mocy umowy, firma Ericsson dostarczy do SoftBank urządzenia do obsługi sieci dostępu radiowego, w tym produkty z portfela Ericsson Radio System. Umożliwi to firmie SoftBank uruchomienie usług 5G w ramach nowo przyznanych pasm o częstotliwości 3,9-4,0 GHz oraz 29,1-29,5 GHz, w celu obsługi nowych sieci radiowych 5G. Dodatkowo firma Ericsson wzmocni istniejącą w SoftBank sieć LTE.

Produkty z serii Ericsson Radio System zostaną wdrożone w kilku regionach, co pozwoli firmie SoftBank na dokonanie optymalizacji całego spektrum aktywów.

Chris Houghton, Starszy Wiceprezes i Szef Działu Rynku Azji Północno-Wschodniej Ericsson, powiedział: „SoftBank i Ericsson są partnerami od czasów wprowadzenia technologii 2G, dlatego też jesteśmy podekscytowani możliwością wspierania firmy SoftBank na kolejnym odcinku ich technologicznej podróży. Dzięki produktom Ericsson, firma SoftBank będzie mogła wykorzystać potencjał technologii 5G, oferując ją społeczeństwu japońskiemu, a my z niecierpliwością czekamy na dalszy rozwój naszego długoletniego partnerstwa”.

Firmy Ericsson i SoftBank zainicjowały wspólne działania w zakresie weryfikacji koncepcji w 2015 r. i skutecznie rozszerzyły swoją współpracę o badania sieci wielopasmowych 5G, w tym o częstotliwościach 28 GHz i 4,5GHz. Obie firmy będą kontynuować prace w zakresie analizy wykorzystania technologii 5G, promowania przekształcenia przez SoftBank sieci LTE w 5G oraz realizowania komercyjnych usług wykorzystujących technologię 5G w 2019 r.

Katarzyna Pąk, Head of Marketing & Communications

 

 


O firmie Ericsson

Ericsson, największy na świecie dostawca technologii i usług dla operatorów telekomunikacyjnych, oferuje społeczeństwu sieciowemu efektywne rozwiązania działające w czasie rzeczywistym, które pozwalają nam wszystkim swobodniej studiować, pracować i żyć w zrównoważonych społecznościach na całym świecie.

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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.

More:

https://www.bcg.com/publications/2018

By Massimo Russo and Michael Albert

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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.

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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