Companies can track and more efficiently operate corporate assets, vehicles and other machinery, gather data and analyze it from every endpoint chip.
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Such infrastructural complexity makes:. The three-tier model of cloud addresses these concerns by shifting computing, server maintenance, and integration of applications from the internal corporate IT to a third-party cloud. This shift is also supported by several studies. According to the Right Scale survey , 93 percent of companies used some variation of cloud solutions, with 58 percent using both private and public cloud, 30 percent opting for public cloud only, and just 5 percent using private cloud.
The statistics imply the gradual migration from private cloud to public cloud. Why is that happening? Public cloud is the main infrastructural driver of digital transformation. For enterprise software, the main spending will be allocated to SaaS and PaaS layers. Why is that important?
If done right, public cloud allocates scalable computing power, provides a unified development environment, and supports cross-device access for all users. Public cloud structure can be divided into three basic levels: infrastructure-as-a-service IaaS , platform-as-a-service PaaS , and software-as-a-service SaaS. IaaS is the lowest tier of cloud computing. A third-party provider supports all infrastructure components, including server hardware, and covers all tasks associated with maintenance and backup.
What are the main business problems that this layer addresses? Cloud infrastructure allocates room for dynamically changing computing events , which is critical in the digital era to meet responsivity and user interaction speed. PaaS level supports a complete life cycle of developing and shipping applications within a single platform. The end user of the PaaS layer is a software engineer who can build and integrate applications using the same environment. It solves the following tasks:. The keyword for the platform layer is obviously integration. As the number of touchpoints—both customer and employee facing—grow, low cost integration allows for delivering custom applications tailored to current needs and customer expectations.
SaaS is the top and most visible layer of the cloud structure. In provides visual access to applications functioning in the platform layer and unifies experience across these applications. Additionally, the SaaS level can support access to legacy systems beyond platform layer. What are the main tasks of the software level? The core goal of the software level is to unify all operational components in a digestible way for as many devices as possible.
Most businesses have embraced cloud in one form or another. The most comprehensive digital transformation potential lies in this three-tier model, which enables scalable computing power, a lone engineering environment, and visual accessibility across devices. Another most revealing shift in the cloud transformation has happened quite unnoticed. Today, increasingly more CIOs consider cloud migration as a primary measure in security risk mitigation.
This trend challenges the main point of hesitation between cloud and on-premise alternatives. Cloud security has overcome this psychological barrier and finally has been acknowledged as the approach more secure than on-premise data storage in addition to its high efficiency, streamlined updates, and scalability. Every time we talk about analytics, the main point of consideration is how to shift from assumptions, based on experience and intuition, to data-driven decision making. The eventual goals of analytics are to optimize the existing processes to reduce cost, personalize customer experience—we talked about that earlier—and automate processes using gathered data and best practices.
But achieving these goals through analytics is inseparable with new ways of information and data management. These not only entail proper software tools but also ground changes in the ways the organization operates. The most recognized approach to understanding the levels of analytics development is the maturity model. It describes how analytics evolve as a company moves from assumption-based decision making to a data-driven organization.
Descriptive analytics. A person can get the answer looking at dashboards and reports. In most cases, analytics initiative stops here and decisions are still based on assumptions that derive from partly unanalyzed data. Diagnostic analytics. The question is Why did it happen? To realize the potential of diagnostic analytics, the organization must acquire software and talent capable of yielding these insights.
Today, diagnostic tasks can be addressed with data mining and machine learning techniques. Predictive analytics. The question is What will happen? Will this customer leave? What is the price going to be for this product? How long will it take for a vehicle to operate with the current workload until it needs maintenance? Predictive analytics is usually realized by means of machine learning.
We discussed business applications of machine learning in our previous white paper. Prescriptive analytics. The question is What should we do? Based on best practices of resolving issues, prescriptive analytics automates decision making, when a number of specific conditions is confirmed. For example, international banks gather various data about credit card transactions and—with a high degree of confidence—can understand whether some transactions are fraudulent.
Hence, a decision to block a suspicious card is made in a fully automatic way, once identified specific circumstances trigger an algorithm. The path from descriptive analytics to prescriptive analytics can only be completed if an organization makes a set of operational changes. Analytics thrive within a fertile environment. But there are many internal and external barriers to it, from people used to making decisions based on their experience to the mere lack of analytics talent.
But the usual scenario is that different departments are hoarding their data and impeding a holistic understanding of processes for other business units. Combating this behavior on the C-level will allow for an analytics jumpstart. Embarking on predictive and prescriptive analytics initiative with the use of the latest technological advancements entails collecting as much data as possible. Besides common figures, try to capture the decisions that surrounded use of the data and how confident those decisions were.
You will be able to employ this information to build more comprehensive algorithms based in machine learning. In theory, establishing data-driven decision making looks good. But in practice, it may be difficult to convince people to think differently. Consider machine learning. People, on the other hand, can justify their decisions by experience.
Not only the initiative should be introduced by the C-level but it should also be advocated this way. Analytics talent is scarce and expensive in terms of compensation and retention. This person should bridge technical aspects of analytics with concrete business interests and sometimes act as a visionary to foster the use of the latest data science techniques. In this case, we usually recommend data be anonymized beforehand by simply substituting revealing records with numbers.
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The first step is to consider how the digitalization is driven within the given industry. Start with assessing the strategic moves by industry competitors. Is the industry impacted by sustaining innovation or is it disrupted? Define mobile and networking adoption levels among customers as well as partners.
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Based on insights, consider the strategic path of digital transformation. Should the operational model be reshaped to support the delivery of a physical product or a service? Or should the product or service be reshaped to meet the digital expectations of customers and partners? Based on the chosen path, which aspect of transformation should be approached first?
Assess internal operations and prioritize what should be reconsidered first.
How holistic the existing cloud shift is? On which level of analytics maturity the organization is? Break down your agenda to concrete tactical moves. What kind of acquisitions do you have to make? Consider the resources and timeline required to pursue the strategy. Connect domain knowledge and technical expertise to consider available solutions and foresee how these technologies can be employed in the industry in a new way. For example, Fujitsu offered an IoT Cloud solution that tracks cow activities and analyzes gathered data to increase the conception rate in cattle.
This visionary activity challenge is to acquire new talent, engage a consultant, or transition internal talent to management. Study the cloud supplier and other possible suppliers. How much does the digital and application development environment of a given supplier match the chosen technology enablers?
Embracing Digital Change Requires a Clear Strategic Focus
How much investment is needed to fill the technological missing gaps that this supplier has? The transition from the old IT to the new cloud-based digitized ecosystem sets up a challenge of operating in a hybrid environment. Organizations must gradually move to the future infrastructure and platform while maintaining the existing ones. This requires additional budgeting for personnel training and data migration. As you contemplate any kind of transformation, the vector of cultural change should not be overlooked.
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McKinsey argues that only 4 percent of global companies truly have a board that is digitally ready. To support continuous change, the vision should be aligned through the whole organization, and the first link in this alignment is definitely the board itself. Digital transformation is a broad term that embraces a wide array of elements that—when combined—define how an organization addresses interactions with customers and clients, how it operates internal tools and manages employee interactions, and eventually how this new digital framework is supported on the technical level.
If done right, the transition eventually reduces operational cost. Hardest is the cultural transformation in businesses that have very deep legacy and cultural roots. From leveraging the new tools to establishing an entirely different style of communication between management and employees, the cultural transformation will remain the main driver of the digital change.
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