2017

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Why Your Company Needs More Collaboration

Digitization demands a focus on cooperation and collaboration that is unprecedented for most enterprises.What distinguishes companies that have built advanced digital capabilities? The ability to collaborate. MIT Sloan Management Review’s research finds that a focus on collaboration — both within organizations and with external partners and stakeholders — is central to how digitally advanced companies create business value and establish competitive advantage. These companies recognize that digital transformation blurs — and sometimes obliterates — traditional organizational boundaries and demands a focus on cooperation and collaboration that is unprecedented for most enterprises. Based on a global survey of more than 3,500 managers and executives, MIT Sloan Management Review and Deloitte’s third annual report on digital business found that the most digitally advanced companies – those successfully deploying digital technologies and capabilities to improve processes, engage talent across the organization, and drive new value-generating business models – are far more likely to perform cross-functional collaboration. More than 70% of these businesses use cross-functional teams to organize work and charge them with implementing digital business priorities. This compares to less than 30% for organizations in an early stage of digitization.

Lack of Communication and Collaboration in Enterprise Architecture Development

Enterprise architecture (EA) is widely employed to reduce complexity and to improve business–information technology (IT) alignment. Despite the efforts by practitioners and academics in proposing approaches to smoothen EA development, it is not easy to find a fully successful EA. Because EA development is a complex endeavour, it is important to understand the obstacles that practitioners face during EA development. With the grounded theory, we studied how obstacles during EA development emerged from practitioners’ point of view in 15 large enterprises. The study identifies lack of communication and collaboration as the core obstacle that can explain many other obstacles. Communication and collaboration were also harmed by other perceived EA development obstacles, including lack of knowledge and support inside organization and issues imposed by external parties, hesitation in training personnel, setting too ambitious goals, constant change of management, (lack of) clarity in EA development process, lack of budget, forcing personnel to adopt EA, lack of motivation, organizational culture, and organizational structure deficiencies. The lack of communication and collaboration caused several undesired effects to organizations, such as being unable to set common goals and achieve a shared understanding, personnel’s distrust, endangered EA governance, lack of innovation capability, lost competitive edge, and ineffective EA outputs. The study highlights that organisations should improve their communication and collaboration before embarking on EA to encounter fewer obstacles. We provide four recommendations for practitioners to improve communication and collaboration in EA development.

Systems Approaches to Public Sector Challenges: Working with Change

Complexity is a core feature of most policy issues today and in this context traditional analytical tools and problem-solving methods no longer work. This report, produced by the OECD Observatory of Public Sector Innovation, explores how systems approaches can be used in the public sector to solve complex or “wicked” problems . Consisting of three parts, the report discusses the need for systems thinking in the public sector.

A Manager’s Guide to Augmented Reality

There is a fundamental disconnect between the wealth of digital data available to us and the physical world in which we apply it. While reality is three-dimensional, the rich data we now have to inform our decisions and actions remains trapped on two-dimensional pages and screens. This gulf between the real and digital worlds limits our ability to take advantage of the torrent of information and insights produced by billions of smart, connected products (SCPs) worldwide. Augmented reality, a set of technologies that superimposes digital data and images on the physical world, promises to close this gap and release untapped and uniquely human capabilities. Though still in its infancy, AR is poised to enter the mainstream – according to one estimate, spending on AR technology will hit $60 billion in 2020. AR will affect companies in every industry and many other types of organizations, from universities to social enterprises. In the coming months and years, it will transform how we learn, make decisions, and interact with the physical world. It will also change how enterprises serve customers, train employees, design and create products, and manage their value chains, and, ultimately, how they compete. In this article we describe what AR is, its evolving technology and applications, and why it is so important. Its significance will grow exponentially as SCPs proliferate, because it amplifies their power to create value and reshape competition. AR will become the new interface between humans and machines, bridging the digital and physical worlds. While challenges in deploying it remain, pioneering organizations, such as Amazon, Facebook, General Electric, Mayo Clinic, and the U.S. Navy, are already implementing AR and seeing a major impact on quality and productivity. Here we provide a road map for how companies should deploy AR and explain the critical choices they will face in integrating it into strategy and operations.

Analytics, Innovativeness, and Innovation Performance

Based on organizational information processing theory, this paper develops and tests a research model to deepen the understanding about the conditions under which the use of data analytics contributes to innovation performance. This paper suggests that firm innovativeness, as an organization cultural concept, should moderate the relationship between data analytics use and innovation performance. The results of a moderation analysis based on data from cross-sectional survey support this account. The findings indicate a significant inversely U-shaped effect of innovativeness on the relationship between data analytics use and innovation performance. The effect of data analytics use on innovation performance is strongest under medium levels of innovativeness but comparatively weaker when firms have a low or a high level of innovativeness. These insights contribute to the IS literature by clarifying the important role of firm cultural factors in shaping information needs and deployment of information processing capabilities.

What can machine learning do? Workforce implications

Digital computers have transformed work in almost every sector of the economy over the past several decades. We are now at the beginning of an even larger and more rapid transformation due to recent advances in machine learning (ML), which is capable of accelerating the pace of automation itself. However, although it is clear that ML is a general purpose technology, like the steam engine and electricity, which spawns a plethora of additional innovations and capabilities, there is no widely shared agreement on the tasks where ML systems excel, and thus little agreement on the specific expected impacts on the workforce and on the economy more broadly. We discuss what we see to be key implications for the workforce, drawing on our rubric of what the current generation of ML systems can and cannot do [see the supplementary materials (SM)]. Although parts of many jobs may be suitable for ML (SML), other tasks within these same jobs do not fit the criteria for ML well; hence, effects on employment are more complex than the simple replacement and substitution story emphasized by some. Although economic effects of ML are relatively limited today, and we are not facing the imminent end of work as is sometimes proclaimed, the implications for the economy and the workforce going forward are profound.