Jorn Bettin

Jorn Bettin

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The topic of Big Data has been propelled from the engine room of theWeb 2.0 giants into the mainstream press. Over the last decade, the volume of data that governments and financial institutions collect from citizens has been eclipsed by the data produced by individuals in terms of photos, videos, messages, as well as geolocation data on online social platforms and mobile phones, and also the data produced by large scale networks of sensors that monitor traffic,weather, and industrial systems.

IBRS has always recognised data as the key to value creation, and has built up an extensive body of research on the latest trends and the shift from enterprise data to “big data” that is currently unfolding. This white paper addresses the scale and the businessimplications of this shift.


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Conclusion: DevOps is a grassroots movement that is only a few years old but has quickly spread across the globe, and its influence is present in virtually all organisations that operate popular Cloud services. DevOps is a portmanteau of software system Development and Operations, referring to the desire to bridge the gap between development and operations that is inspired by agile techniques, and that is driven by the need to continuously operate and upgrade Cloud services. The DevOps movement is having a profound impact in terms of the tools and techniques that are used in the engine rooms of Clouds, leading to order of magnitude changes in the ability to perform hot system upgrades.


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Conclusion: The maturity of information management practices in an organisation has a direct effect on the ability to achieve business goals related to supply chain optimisation, the quality of financial decisions, productivity, and quality of service. The exponential growth of unstructured information is no replacement for structured information. Quite the opposite: a stream of unstructured Big Data can only be turned into tangible value once it is channelled through a distillery that extracts highly structured information accessible to human decision makers, and that can be used to provide a service to the public or to drive a commercial business model. The transformation of unstructured data into knowledge and actionable insights involves several stages of distillation, the quality of which determine the overall performance of the organisation.


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Some standards are undeniably useful, and the benefits of these standards can typically be quantified in terms of improvements in quality and productivity due to increases in the level of automation and interoperability. In contrast, other standards mainly fuel a certification industry that has developed around a standards body, without leading to any measurable benefits, whilst clearly adding to the operating costs of those organisations that choose to adopt such standards.


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Conclusion: Increasingly, organisations are recognising that they can benefit from a so-called software product line approach. The transition from an IT organisation that operates entirely in project delivery mode to a product development organisation that introduces a product line governance process is a significant undertaking. The process involves the designers of business information services as well as Enterprise Architects and other domain experts. Achieving the benefits of a product line approach (systematic reuse of shared assets) requires the adoption of a dedicated product line engineering methodology to guide product management, design, development, and operations, and it also requires knowing where to draw the boundary between product development and the delivery of professional services.


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Conclusion: Effective data science requires a cross-disciplinary team of highly skilled experts, as well as data in sufficient quantity and quality. These requirements imply a level of maturity in information management that is beyond the capability of most organisations today. An information management maturity assessment can help determine whether an organisation is ready to embark on a big data initiative, and to identify any concrete deficits that need to be addressed.


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Conclusion: There are many links between the story of data warehousing and the story of SAP adoption, going all the way back to 1997, when SAP started developing a “Reporting Server”. Over the following decade SAP firmed up its dominant position as a provider of Enterprise Resource Planning functionality, creating countless business intelligence initiatives in the wake of SAP ERP implementation projects. Up to 80% of data warehouses have become white elephants, some completely abandoned, and others have been subjected to one or more resuscitation attempts. Big data can either be the last nail in the coffin, or it can be the vaccine that turns the colour of the data warehousing elephant into a healthy grey.


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Conclusion: Direct dependencies between services represent one of the biggest mistakes in the adoption of a service oriented architecture. An event driven approach to service design and service orchestration is essential for increasing agility, for achieving reuse and scalability, and for simplifying application deployment. Complex Event Processing offers a gateway to simplicity in the orchestration of non-trivial service supply chains.


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Conclusion: Big data not only refers to the growing amounts of netizen-generated online data, it also refers to customer expectations related to the data services provided by corporations and government departments. Increasingly corporate and individual service users expect not only a basic service, but also access to advanced tooling for data transformation, representation, and integration into other systems. In the future, the level of maturity and professionalism of an organisation will increasingly be determined by data-related quality of service characteristics. It is time for organisations to grow-up, and to treat information services as a core product line.


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Conclusion: When conceiving and designing new services, the primary focus of product managers and technologists is often on functionality, and adequate quality of service is largely assumed as a given. Similarly, from the perspective of a potential user of a new service – the user is mainly concerned about the functional fit of the service, and is prone to making implicit assumptions about quality of service based on brief experimental use of a service. The best service level agreements not only quantify quality of service, they also provide strong incentives for services provider and service users to cooperate and collaborate on continuous improvement.


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Conclusion: Increasingly, organisations are looking beyond classical agile methodologies, towards lean techniques pioneered in industrial production. The transposition of lean techniques into the context of corporate IT is a challenge that requires a high level of process maturity and organisational discipline. The desired benefits only materialise if the lean approach is applied to processes that can be put under statistical control, and if the approach feeds into a domain engineering process that addresses the root causes of operational inefficiencies.


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Conclusion: All organisations are multilingual, and most, more so than may seem apparent on the surface. A systematic effort to minimise the likelihood and impact of communication problems can lead to significant cost savings, productivity improvements, and improvement of staff morale. Data quality, the quality of system integration, and the quality of product or system specifications often turn out to be the Achilles’ heel. It is a mistake to assume that the biggest potential for misunderstandings is confined to the communication between business units and the internal IT department. Whilst some IT departments could certainly benefit from learning to speak the language used by the rest of the business, the same conclusion applies to all other business units.


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Circa 1960: The “Hard theory of platforms”

In the early days of information technology, hardware was THE platform. Companies such as IBM and DEC provided the big iron. Business software was THE application. In those days even software was as hard as stone. The term application platform was unheard of.


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Conclusion: Pattern-based and repeatable processes, such as gathering operational data, validating data, and assessing data quality, offer potential for automation. The Web and software-as-a-service technologies offer powerful tools that facilitate automation beyond the simple mechanical pumping of data from one system to the next. Operational management tasks that focus on administration and control can and should be automated, so that managers have time to think about the organisation as a system, and can focus on continuous improvement.


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