The digital transformation in manufacturing represents a significant opportunity to improve operational efficiency, reduce costs, and enhance the customer experience. The manufacturing industry is undergoing a profound transformation driven by rapid advances in technology and the growing demand for more efficient, sustainable, and data-driven operations. This transformation often referred to as Industry 4.0, marks the beginning of a new era of digitalization, data-driven decision-making, and smart automation. The adoption of Industry 4.0 technologies and practices is critical for manufacturers looking to remain competitive in a rapidly changing business environment.

In an era where data is becoming the new currency, manufacturers that can leverage data and analytics to drive innovation and improve their operations will be the ones that succeed. The digital transformation of manufacturing also presents a critical solution for addressing the global challenges posed by climate change, as it enables the creation of more sustainable and environmentally friendly production processes.

The importance of digital transformation in manufacturing

Manufacturing has long been a vital part of the global economy, and the industry has come a long way in recent decades. However, with the advent of new technologies and the increasing demand for customization and efficiency, the manufacturing sector is undergoing a significant transformation. Digital transformation is at the forefront of this change, offering manufacturers a wealth of opportunities to improve their operations, meet the demands of customers, and remain competitive in a rapidly evolving market.

A brief overview of the current state of manufacturing and the benefits of digital transformation

The current state of manufacturing is characterized by a growing need for customization and personalization, as well as increased pressure to reduce costs, improve quality, and speed up delivery times. To meet these demands, manufacturers are turning to digital solutions to streamline their operations, increase efficiency, and gather valuable data and insights. From Industry 4.0 technologies and practices to investments in data and analytics infrastructure to the adoption of smart automation and connected systems, digital transformation is enabling manufacturers to stay ahead of the curve and thrive in today’s fast-paced, global market.

The benefits of digital transformation in manufacturing are numerous and wide-ranging. By adopting digital solutions, manufacturers can improve their operations in a number of ways, including increased efficiency and productivity, reduced costs, improved quality control, and enhanced customer experience. Additionally, digital transformation can provide manufacturers with valuable insights into their operations, enabling them to make data-driven decisions, optimize their processes, and stay ahead of the competition.

The drivers of digital transformation in manufacturing

The manufacturing industry is undergoing a significant transformation, driven by a number of factors, including technological advancements and increased competition. These drivers are driving manufacturers to embrace digital solutions and transform their operations in order to remain competitive in the rapidly evolving market.

The role of technology advancements and increased competition

The role of technological advancements in digital transformation in manufacturing cannot be overstated. Advances in areas such as the Internet of Things (IoT), artificial intelligence (AI), and cloud computing have enabled manufacturers to collect and analyze vast amounts of data, automate processes, and streamline operations. This has allowed manufacturers to become more efficient, reduce costs, and improve the quality of their products and services.

Increased competition is another key driver of digital transformation in manufacturing. With the rise of global competition, manufacturers are facing increased pressure to improve their operations and offer unique and innovative products and services. By embracing digital solutions, manufacturers can not only stay ahead of the competition but also differentiate themselves in the market and better meet the demands of their customers.

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The impact of changing customer demands and market trends

Changing customer demands and market trends are also driving the need for digital transformation in manufacturing. Consumers are increasingly demanding customized and personalized products and services and are expecting faster delivery times and improved quality. Manufacturers must respond to these demands by embracing digital solutions that allow them to streamline their operations, improve efficiency, and meet the needs of their customers.

The key components of a successful digital transformation in manufacturing

The manufacturing industry has undergone a significant transformation in recent years, with the rise of Industry 4.0 and the increasing adoption of digital technologies. The successful implementation of a digital transformation in manufacturing requires a combination of the right technology, a supportive culture, and a focus on data and analytics.

Adoption of Industry 4.0 technologies and practices

Industry 4.0 refers to the fourth industrial revolution, characterized by the integration of advanced technologies such as the Internet of Things (IoT), artificial intelligence (AI), and robotics into the manufacturing process. The adoption of these technologies is a key component of a successful digital transformation in manufacturing. By implementing these technologies, manufacturers can improve efficiency, reduce costs, and enhance the overall quality of their products.

Investment in data and analytics infrastructure

Data is at the heart of a successful digital transformation in manufacturing. Investing in a robust data and analytics infrastructure is critical for manufacturers looking to take advantage of the opportunities presented by Industry 4.0. This infrastructure should include the collection, storage, and analysis of data from a variety of sources, including IoT-connected devices, machines, and production lines. The insights generated from this data can help manufacturers make informed decisions, improve their processes, and stay ahead of the competition.

Building a digital-first culture and mindset

A digital-first culture and mindset are essential for the successful implementation of a digital transformation in manufacturing. This means that manufacturers need to adopt a customer-centric approach and embrace the change brought about by digital technologies. Companies must empower their employees with the right tools, training, and support to fully leverage these technologies and cultivate a culture that values innovation and continuous improvement.

Implementation of smart automation and connected systems

Smart automation and connected systems play a crucial role in the digital transformation of manufacturing. By automating repetitive tasks and connecting systems and devices, manufacturers can reduce the risk of human error and improve the overall efficiency of their operations. Additionally, the data generated by these connected systems can provide valuable insights into the production process, allowing manufacturers to identify areas for improvement and optimize their operations.

Developing a digital transformation roadmap for manufacturing

A digital transformation roadmap is a strategic plan that outlines the steps a company must take to achieve its digital transformation goals. In the context of manufacturing, a digital transformation roadmap can help companies embrace new technologies, modernize their operations, and stay ahead of the competition. In this section, we’ll discuss the key components of a successful digital transformation roadmap for manufacturing.

Step 1: Assessing current state and defining goals

The first step in developing a digital transformation roadmap is to assess the current state of the company and define clear, measurable goals for the transformation. This should include an analysis of the company’s technology and data infrastructure, processes, and culture. The goals should be aligned with the company’s overall business strategy and should take into account both short-term and long-term needs.

Step 2: Identifying priorities and developing a plan

Once the current state and goals have been defined, the next step is to identify the priorities for the transformation. This should be done in consultation with key stakeholders and should take into account the resources available, the timeline for the transformation, and the potential impact on the business.

Based on the priorities, a detailed plan for the transformation should be developed. This plan should include specific actions and timelines for implementation, as well as a clear understanding of the resources required and the roles and responsibilities of the various stakeholders.

Step 3: Implementing and monitoring progress

The implementation phase is where the plan for the digital transformation is put into action. This will likely involve a significant amount of change management as employees and processes are adapted to the new technologies and approaches. It is important to communicate the benefits of the transformation clearly and effectively and to provide support and training as needed.

Finally, progress should be regularly monitored and evaluated, and the roadmap should be adjusted as necessary. This will help ensure that the transformation remains on track and that the goals are achieved.

Case studies of digital transformation in manufacturing

The successful implementation of a digital transformation in manufacturing can have a profound impact on a company’s operations, competitiveness, and bottom line. In this section, we’ll take a closer look at some real-world examples of digital transformation in the manufacturing industry.

Digital transformation examples in manufacturing

One prominent example of digital transformation in manufacturing is the automotive industry. Automakers have invested heavily in Industry 4.0 technologies and data-driven processes, leading to significant improvements in efficiency and productivity. Another example is the aerospace industry, where digital technologies have been leveraged to improve supply chain management, streamline the design process, and reduce the time it takes to bring a new product to market.

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Discussion of the key elements that led to their successful transformations

The successful transformations in the automotive and aerospace industries were driven by a number of key elements, including investment in advanced technologies, the adoption of data-driven processes, and a focus on building a digital-first culture. In each case, the companies were able to leverage digital technologies to improve their operations, reduce costs, and stay ahead of the competition. The key to their success was a combination of technology, process, and culture, working in harmony to deliver tangible results.

Challenges and best practices for digital transformation in manufacturing

While digital transformation in manufacturing offers many benefits, it is not without its challenges. In this section, we’ll discuss some of the common obstacles manufacturers face when undergoing a digital transformation and provide best practices for overcoming them.

Common obstacles and how to overcome them

One of the biggest challenges in digital transformation in manufacturing is resistance to change. Many employees may be skeptical of new technologies and processes and may be unwilling to adopt them. To overcome this challenge, manufacturers need to provide their employees with training and support and communicate the benefits of the transformation clearly and effectively.

Another challenge is a lack of investment in technology and data infrastructure. Manufacturers must invest in the right technology and data infrastructure if they want to reap the benefits of digital transformation. This includes not only the technology itself but also the personnel and resources needed to manage it effectively.

Benefits of digital transformation in manufacturing

The benefits of digital transformation in manufacturing are many and varied. By adopting Industry 4.0 technologies and data-driven processes, manufacturers can improve efficiency, reduce costs, enhance the quality of their products, and better meet the needs of their customers. Additionally, digital transformation can help manufacturers stay ahead of the competition and remain relevant in an increasingly digital world.

Strategies for ensuring long-term success

To ensure long-term success, manufacturers must develop a clear and comprehensive digital transformation strategy. This strategy should be built around the specific needs and goals of the company and should take into account the technology, process, and culture elements discussed earlier. Manufacturers should also be prepared to continually evaluate and adjust their strategy as needed in response to changing market conditions and technology trends.

Another important factor in long-term success is the development of a strong data and analytics culture. Manufacturers should encourage the collection and analysis of data from all aspects of their operations and should use these insights to drive continuous improvement. Finally, manufacturers should foster a culture of innovation and continuous learning, empowering their employees to take advantage of new technologies and processes as they emerge.

The future of digital transformation in manufacturing

The manufacturing industry is undergoing a rapid transformation driven by digital technologies, and the pace of change is only expected to accelerate in the coming years. In this section, we’ll examine some of the emerging technologies and trends shaping the future of digital transformation in manufacturing and discuss how manufacturers can prepare for what’s to come.

Discussion of emerging technologies and trends

One of the key trends shaping the future of digital transformation in manufacturing is the increasing use of artificial intelligence (AI) and machine learning (ML). These technologies have the potential to revolutionize the way manufacturers approach operations by enabling them to make better use of data, automate repetitive tasks, and optimize decision-making.

Another important trend is the growing adoption of the Internet of Things (IoT) in manufacturing. The IoT refers to the network of connected devices that can communicate and share data with one another and has the potential to create more efficient, interconnected, and responsive manufacturing operations.

AI and big data are the driving forces behind Industry 4.0

Predictions for the future of the industry and how manufacturers can prepare

In the years to come, it is likely that the use of AI, ML, and IoT will become increasingly widespread in the manufacturing industry. Manufacturers that embrace these technologies and incorporate them into their operations will be well-positioned to stay ahead of the competition and continue to grow.

To prepare for the future, manufacturers must invest in the development of their data and analytics capabilities and actively seek out new technologies and best practices. They should also be proactive in developing their human capital by providing their employees with the skills and training they need to succeed in a rapidly evolving digital landscape.

Conclusion

In conclusion, the digital transformation of manufacturing is an essential step for companies looking to remain competitive in the rapidly changing business landscape. By embracing Industry 4.0 technologies and practices, manufacturers can improve their operations, reduce costs, enhance the customer experience, and address the global challenges posed by climate change. The future of manufacturing is digital, and manufacturers that can effectively navigate the transformation will be the ones that thrive. The digital transformation of manufacturing represents a significant opportunity for growth and innovation, and those companies that embrace it will be well-positioned to succeed in the era of data and digitization.

There must always be a base from which to innovate. With the underlying analysis, shifting funds from infrastructure to innovation is possible. If not, your infrastructure is at risk. In many situations, innovation may necessitate new corporate investment.

IT transformation is the comprehensive review and reworking of an organization’s IT infrastructure to increase effectiveness and delivery in a digital economy. Business leaders, such as the CIO, are in charge of IT transformation, which is the cornerstone of an organization’s overall digital transformation plan. It may entail updating and changing network infrastructure, hardware, software, IT service management, and the methods used to store and retrieve data. Informally, the motto “rip and replace” may be used to describe IT transition.

What is IT transformation?

The majority of consumers in the modern market want to feel exactly the same way when using technology for work as they do when using it for personal interests. However, a firm may find it challenging to accomplish this. Because of this, businesses are calling for more hybrid IT solutions that will ensure that customers receive the best possible service.

The ultimate goal of every corporate CEO is to increase staff productivity and effectiveness. This justifies the necessity of IT transformation. It offers significant solutions for crucial corporate processes, including finance and human resources.

Understanding the IT transformation strategy

Businesses must take action to keep up with a market that is becoming more digital and competitive by not just improving their current systems but also developing and acquiring new applications and services that provide deeper insights into their operations, industry, and clientele. IT transformation frequently seeks to transition the IT department from a reactive, rigid organization to a proactive, adaptable component of the company that can react fast to shifting digital business requirements and make better-informed decisions.

The ultimate objective of these efforts, according to Deloitte, is to “reimagine IT development, delivery, and operating models, and to enhance IT’s ability to collaborate effectively within the enterprise and beyond its traditional boundaries.”

Business transformation

Making substantial changes to the way a corporation or organization operates is referred to as “business transformation.” Personnel, procedures, and technology are all included in this. Organizations that undergo these changes are better able to compete, become more efficient, or completely change their strategic direction.

Business transformations are large-scale, seismic adjustments that firms implement to spur development and change beyond the bounds of incremental improvements. The focus is broad and strategic, including changing to new operational or commercial models.

Business transformations are undertaken by organizations to increase value. To maximize the potential of the business, it can be necessary to optimize personnel potential, harness intellectual property and proprietary technology for other uses, or improve efficiency.

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Application transformation

Application transformation is the process used to analyze old software in a firm and evaluate whether applications can be modernized or moved to the cloud. The apparent first step is to take stock of what you already have, but for larger firms, some legacy systems contain layers of out-of-date languages with lost KT.

Plotting the application depending on its complexity and importance to the client and the organization’s future can therefore be the first step. From there, your “initial wave” efforts toward modernization will be those that are high-value and low-effort. The discovery process for application transformation makes it possible to choose the most appropriate course for modernization.

Benefits of IT transformation

In order to supply automated services, cloud computing, and new operating models, successful IT transformation creates a strong core infrastructure. Additionally, it streamlines and quickens the deployment of IT services while lowering deployment risk. IT transformation paves the way for more affordable, flexible, and innovative IT as a service delivery.

Organizations may free their IT budget from operational costs and allocate more money for digital transformation by optimizing existing IT cost models. Better business-IT alignment is another benefit of IT transformation.

IT transformation challenges

Since many firms were not founded in the digital era, they lack the freedom to quickly and totally take out and replace all current IT systems. These businesses must contend with outdated business models, software, and systems that limit their ability to transition while planning how to adopt contemporary techniques and methods. This involves diverting funds and resources from established programs to fresh IT transformation projects.

Like any significant organizational change, IT transformation has an impact on corporate culture, business standards, and workflow. In order to successfully convert an organization into a digital business, CIOs must first successfully navigate culture, according to research firm Gartner. In addition to organization-wide communication and training about new IT processes and technology, having a shared vision of the organization’s future state is crucial.

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IT transformation examples

If you’ve ever seen The Office, you’ve already witnessed (a drawn-out and undoubtedly imperfect) digital revolution over the course of 10 seasons. Although the purpose of this humorous example from pop culture was not to impart knowledge, it is a great illustration of how a traditional company and its workforce handled the transition to digital. Mike was not a strong supporter of the shift, but we shouldn’t dwell on that for too long; instead, we should move on to other examples of IT transformation in action.

UPS

One of the sectors affected most significantly by the development of digital technology is unquestionably logistics. UPS is a supply chain management and worldwide shipping business. Given that it was established in 1907, we believe that the digital revolution was simply one change in a lengthy series of changes. Although UPS shows it is not always true, established businesses often have greater trouble implementing IT transformation.

They created a fleet management solution in 2012 that employed machine learning to plan the most efficient delivery routes. The device markedly enhanced driver productivity while lowering fuel costs and carbon emissions. Software development is thought to be saving UPS $300M to $400M annually.

The implementation of numerous data-driven solutions to improve UPS’ internal operations is another illustration of the company’s IT transformation efforts (such as sorting packages, loading trucks, etc.).

UPS never stops and consistently takes on new tasks associated with its IT transformation. The company has saved hundreds of millions of dollars and is still one of the biggest shippers in the world since it actively adopted the shift.

Ikea

The international behemoth Ikea creates and markets ready-to-assemble furniture and home decor. Ikea made the decision to go digital in 2018 after operating an analog business for over 80 years and becoming one of the most recognizable brands in the world.

The business made a choice to bring on board a digital guru to guide it through the procedure. At the beginning of 2018, former Google and Samsung advisor Barbara Martin Coppola joined the Ikea team as the Chief Digital Officer.

Ikea made the decision to modify its stores and use them as fulfillment centers in order to adapt to the new business model. They employed algorithms to manage the supply chain in order to run two businesses concurrently from the same location (from thousands of locations, including Ikea shops and delivery centers). Additionally, they concentrated on creating analytics and incorporating them into decision-making.

Ikea has the same clientele, both in-store and online. The business made the decision to link in-person and online contacts with customers in order to improve customer experience and maintain consistent branding across all channels. For instance, one may begin planning their new kitchen on the Ikea website before visiting the shop. They can locate themselves by connecting to a remote customer meeting location in the store.

Ikea, a retailer, concentrated on growing its online business. Running a traditional store and an internet store are completely different tasks. Running both at once is a very different matter. It is impossible to define the boundary between one phase of the IT transformation and another.

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IKEA must incorporate digital into every facet of its business, as stated by Barbara Martin Coppola. While the company’s ideals remain constant, the methods of operation change to reflect the evolving business and lifestyle environments.

Adobe

American software business Adobe was established in 1982. Photoshop, Adobe Acrobat Reader, and Illustrator are some major products you have definitely used.

Adobe Systems, the company’s previous name, offered boxed software back then. The business took a big risk by switching from a license-based approach to a subscription-based model when the 2008 financial crisis hit. It reorganized its service portfolio into three cloud-based solutions: Experience Cloud, Document Cloud, and Creative Cloud. This is how Adobe evolved into a cloud business using the now-ubiquitous SaaS (software-as-a-service) model.

In the meanwhile, they bought an e-commerce platform and a web analytics firm (Omniture) as part of their IT transformation (Magento) efforts. The business also understood that only with the top talent would they be able to accomplish their objectives. As a result, Adobe concentrated on ensuring employee satisfaction and made investments in developing an employer brand and employee-focused work culture. In order to keep track of the company’s health, they also used a data-driven operation model.

What is the difference between IT and digital transformation?

Rethinking and changing business models while keeping the client at the forefront of our attention is a part of digital transformation. The practice of utilizing digital technology in all business domains to either develop new processes and customer experiences or adjust existing ones will fundamentally enhance how firms provide value to their consumers.

It is essential to comprehend what digital transformation is not in order to have a greater understanding of what it is. Digital transformation does not just entail boosting your social media presence and engagement but also means overhauling your current business procedures. Every aspect of the business is affected by digital transformation, which goes beyond processes and products to affect the organization’s culture as well. This includes how decisions are made, who is hired, how post-sales service is provided, and even how employees interact with one another internally.

It’s wise to clarify right away that IT transformation is distinct from digitization, which is the process of converting anything from analog to digital, and distinct from digitalization, which is the efficient use of data to streamline tasks. The CTO and the CIO are typically in charge of leading digital transformation, and they may collaborate with suppliers to partially or wholly outsource their transformation process.

IT transformation vs digital transformation

These two names are frequently confused with one another and used interchangeably. Let’s examine the distinctions between the two so that you may develop a digital transformation strategy that is appropriate for your company.

The overall approach for digital transformation must include IT change. This simply suggests that IT transformation is necessary for digital transformation to occur. A digital transformation’s foundation is formed through IT transformation.

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The digital transformation of a business affects its people, processes, products, and culture. The digitization of information systems like ERP and others, in contrast, aims to boost productivity and automate more processes. The approach behind digital transformation is customer-driven, with a focus on the preferences and happiness of the customer. IT transformation may not have a strong customer-centric focus and instead focuses mostly on enhancing the IT infrastructure by utilizing the advantages of the most recent technologies.

While IT transformation primarily falls under the purview of IT managers and teams, a digital transformation project necessitates the effort and participation of the entire business. Unlike IT transformation, which is driven by one function, digital transformation is a company-wide progression.

The size of their spheres of influence is another distinction between the two. The scope of IT transformation is restricted to changes in infrastructure, such as cloud computing, network needs, hardware, software, and data management. On the other hand, digital transformation uses all of these tools and covers every aspect that has an impact on an organization, making it far more comprehensive.

Digital transformation lacks an “end state,” whereas IT transformation has a well-defined ending state and aim. This is due to the fact that the process of digital transformation is constantly changing and requires good long-term management of changes in technology, business, and consumer behavior.

There will always be a need for creative strategies that foresee and satisfy client expectations as a result of emerging technologies and changing market dynamics. You may promote a culture of constant change that fuels the expansion of your company by looking at chances to provide relevant solutions for stakeholders and boost operational efficiencies with the cloud.

Despite the numerous advantages of digital transformation, firms frequently fail to realize them for a variety of reasons, one of which is failing to select the appropriate strategic partner to assist with the process. For a long-term, cost-effective, and significant transformation, you must work with a seasoned organization that matches the digital transformation techniques with your unique business demands.

The Treehouse Tech group, which has years of experience in digital transformation and automation, is committed to creating data-led digital transformations that keep businesses ahead of the curve by combining big data with cutting-edge analytical software and cloud architecture.

Conclusion

IT transformation is the process of using digital technologies to reinvent your company for the benefit of both you and your consumers. Running it effectively requires a fundamental change in perspective, yet there don’t seem to be many other options. The clientele has already embraced the digital era; therefore, going digital offers more benefits than just a strategic advantage.

Because of the quickly changing environment (VUCA) and the gradual adoption of technology, businesses are under tremendous strain. In light of this, a well-planned and executed digital transformation strategy may alter one’s place in the race while also leaving one with an open mind and the foundation for future success.

One of the biggest advantages of IT transformation is that it will enable your company to gather and analyze data that it can turn into insights that generate money. In the past, you may have collected and used no data, or your data may have been dispersed over numerous disconnected platforms. Strong data collecting, centralized data storage, and the development of technologies to evaluate and transform data into information that supports informed business decision-making will be made possible by digital transformation.

Software defined data center (SDDC) is the result of decades-long progress in server virtualization. SDDC extends virtualization into data storage and networking, and it provides a single software toolset for managing those virtualized assets. All infrastructure elements, networking, storage, CPU, and security are virtualized and delivered as a service in the scope of SSDC. Deployment, operation, provisioning, and configuration are also abstracted from the hardware and implemented through intelligence.

In the cloud and edge computing era, software-defined data center architectures provide significant agility gains to IT departments. An SDDC can improve IT departments’ ability to react to new requests for IT resources by pooling IT infrastructure assets and standardizing management tools across infrastructure layers. While software defined data centers offer end-users more control and flexibility, IT staff also retains provisioning control while lowering expenditures and laying the groundwork for app modernization.

What is software defined data center?

A traditional data center is a facility where corporate data, applications, networks, and infrastructure are centrally maintained and accessed. The data center is where IT operations and physical infrastructure gadgets are housed, such as servers, storage devices, network devices, and security equipment. Traditional data centers are usually hosted on-premises, with a managed service provider, or on the cloud.

In contrast, a software-defined data center is a platform that serves the needs of an organization’s software, infrastructure, or platform. SDDC can be deployed on-premises, with the MSP, or on private, public, or hosted clouds. SDDCs also host servers, storage devices, network equipment, and security devices like traditional data centers. Unlike a traditional data center, SDDC takes a programmatic approach to the functions of a conventional data center.

In 2012, former VMware CTO Steve Herrod coined the term “software defined data center”. Since then, this technology has gained a lot of traction.

Components of a software defined data center

The SDDC, which stands for “software-defined data center,” is a platform that manages the integrated environment. It combines virtualized compute, storage, and network resources with a platform for managing the integrated setting.

Server virtualization

Server virtualization, often known as computing virtualization, allows separating operating systems and applications from physical servers. Virtual machines (VMs) allow IT administrators to run many applications and operating systems on a single server. For over a decade, organizations have used compute virtualization to cut server sprawl and boost resource efficiency.

Storage virtualization 

Storage virtualization pools resources and eliminate isolated storage systems. Storage virtualization allows for more flexibility and scalability since it is possible to provision storage from the pool without purchasing a new capacity. You may dynamically allocate storage with virtual storage, allowing each application to have the capacity it requires as needed.

Network virtualization 

Network virtualization enables the provision and manages networks independently of the physical hardware. The abstraction of resources lowers provisioning time and boosts flexibility, allowing you to move workloads across data centers more freely. Businesses should consider investing in the right network virtualization solution to prevent IT data losses and improve productivity, including security features to secure networks and isolate workloads.

Single management and automation platform

The SDDC integrates these virtualization layers, creating a single, hyperconverged environment that allows the delivery of IT resources as a service, regardless of whether the SDDC is hosted in a private, public, or hybrid cloud. Single management and automation platform standardizes management across the virtualization layers and allows policy-based automation, which streamlines operations.

Benefits of software defined data center

Today, the migration to software defined data centers has accelerated, and many organizations are replacing their traditional infrastructures with their modern counterparts.

Organizations can gain both immediate and long-term advantages by implementing a software defined data center architecture. Agility is the first and most crucial short-term benefit. You can significantly cut down on time it takes to provision new resources when using an SDDC. It no longer takes days to set up a new physical server, add more storage space to an application, or make physical network modifications. Policy-based automation can also speed things up considerably by allowing deploying resources in mere minutes.

The SDDC gives a unique approach to data storage that is ideal for businesses looking to use DevOps to embrace digital transformation successfully

In addition to offering increased security and reliability, an SDDC can also assist with infrastructure performance. You may optimize the performance of each application and workload without having to make physical modifications to the infrastructure.

In the long run, the SDDC helps you keep costs down. Pooling resources allows you to use your existing infrastructure more efficiently and avoid the need for new system purchases. Redundancy and multiple backup sources contribute to more efficient operations, which equals less infrastructure sitting dormant. Implementing an SDDC using hybrid or public cloud infrastructure allows the transition from CAPEX to an OPEX model, eliminating significant up-front expenditures.

Establishing a strategy to modernize your infrastructure and applications is another advantage of adopting an SDDC approach. By focusing on a single management platform, you may easily connect new technologies and shift workloads to the cloud.

Challenges of software defined data center

It is wise to explore the challenges that might risk your ROI while implementing software defined data center. The most challenging part is obtaining a cross-functional agreement. Cross-team congruity is essential to adopting SDDC, but many legacy IT companies are trapped in siloed procedures and rules that make standardization harder than it needs to be. It might take time to get procurement, development, IT analysts, system administrators, and other teams on board with new tools and procedures. Nonetheless, when SDDC is fully implemented, the payoff in efficiency, creativity, and overall cost of ownership can be enormous.

After you’ve completed the initial stage of standardization, there are still technical difficulties to overcome. Depending on the nature of your application and how you customize it, switching over to the new environment might result in some app downtime. Deploying software defined data center components in stages will lower the risk of downtime. Choosing a cloud-based SDDC allows you to experiment with new virtualization layers before putting the entire environment into production at a lower cost.

Without a doubt, the moment you start to virtualize new infrastructure layers like storage and networking, your operations teams’ procedures and workflows will have to be modified. IT departments may have a learning curve when they’re ready to manage the new environment and utilize new tools. Using a familiar toolset to develop an app on the cloud can reduce the learning curve considerably.

SDDC’s past, present, and future

VMware introduced x86 server virtualization in 2006, and the software defined data center evolved from this trend.

The software defined data center market is divided into three categories: Software-defined compute, networking, and storage. According to Allied Market Research’s report, the SDDC market is anticipated to reach $139 billion in 2022, with a CAGR of 32% over the last five years.

SDDCs are expected to have a similar impact on computing in the data center as isolated networks have done on the internet. The ability to abstract the application layer from the underlying physical hardware allows an app to be deployed in many locations.

SDDC is the standard virtual infrastructure for moving computing resources from demand to private, public, and hybrid clouds. As SDDC grows, IT security must be revised as software abstraction of data center technologies necessitates. Security and virtualization teams can combine efforts to spot and neutralize possible risks. Integration of security in software enables organizations to modify and orchestrate security measures to combat new dangers swiftly.

Digital transformation is more than a mythical buzzword these days. Technological improvements have disrupted many industries, and innovation has transformed the way businesses execute their processes. The number of advances healthcare has made in recent years is mind-boggling, but they’re just getting started.

Healthcare innovation has always centered around improving patient outcomes, increasing preventive healthcare, and reducing physician workloads. A study by Grand View Research projects the American digital healthcare market, currently valued at $110.2 billion, to earn $295.4 billion in 2028. 

Here are four key trends that will drive the way forward as digital transformation changes the way we look at healthcare.

AI and Predictive Healthcare

Data collection has been a pivotal element in healthcare for a long time. Patient medical histories and treatment information are now being used to transform the way hospitals and clinics prescribe treatment. 

Identifying and prescribing preventive plans has helped hospitals reduce loads on emergency rooms and clinics. Big data analysis has also helped hospitals predict the number of admissions they can expect during different seasons of the year and staff them appropriately. 

As big data collection has grown, companies have begun investing in AI-enhanced solutions that have been trained on historical datasets. The public has already been exposed to robots such as Moxi, which is designed to assist nurses with routine tasks.

AI-powered chatbots are increasingly finding their way into customer service and even therapeutic roles. However, AI’s power can be fully unleashed in the field of medical research. Precision medicine, genomics, medical imaging, and drug discovery will benefit from AI algorithms’ ability to quickly process large data sets and discover hidden patterns in them.

Big pharmaceutical companies already use AI to shorten the drug development cycle and have found that discovery timelines have been reduced by four years on average. To fully embrace AI’s potential, healthcare companies need to invest in making AI more friendly to humans.

As industry thought leader Koen Kas says, “The future of healthcare is not so much about adoption of technology, it is about changing behavior. And doing that in an invisible, delightful fashion, by surprise and reward, in the background.”

On-Demand Healthcare

More than half of all internet traffic is from mobile phones today, as they’re used to communicate, research, transact and carry out daily tasks. Add to this fact that more than 4 billion people worldwide have access to the internet, and it’s easy to see how healthcare can be provided at a patient’s convenience.

People use online information hubs primarily to research doctors and medical facilities, but they don’t use them to schedule appointments. The healthcare booking process is an anomaly compared to the progress achieved in the rest of the sector.

Patients still dial into clinics and have operators book them into slots manually. Research conducted by scheduling solutions provider Deputy reveals that young adults are more likely to book appointments by calling instead of via apps or online channels. The lack of usability inherent to online channels is the major reason for this. 

Aside from making online channels more usable, healthcare has also witnessed the rise of the freelance medical professional. Companies such as Nomad Health link doctors and professionals with medical centers that need their skills.

As a result, hospitals can now accommodate a wider range of treatments, even if they don’t have staff on-site with the necessary skills. This prevents the need for patients to travel to specialty hospitals and instead receive treatment at their preferred venues.

Wearable Health Devices

Wearable medical devices are a fast-growing market. Some estimates expect the market to reach $195.57 billion in size by 2027. The appeal of wearables lies in their ability to inform preventive healthcare procedures.

Fitbit, perhaps the most popular wearable biometric collection device on the market, revealed how wearables could play a role in combating the COVID-19 pandemic. The company found that its devices can detect about half of all COVID-19 cases one day before participants report the onset of symptoms. 

“If we can let people know they should get tested a day before symptoms begin,” wrote Fitbit Director of Research Conor Heneghan about the implications of these findings, “they can isolate and seek care sooner, helping to reduce the spread of COVID-19.”

As the adoption of wearables grows, companies are discovering new ways of personalizing the healthcare experience. From empowering individuals to take better care of themselves, to providing insurance incentives, the ceiling is very high when it comes to healthcare wearables.

The US healthcare system will receive the greatest benefit. Approximately 90% of the $3.5 trillion spent annually goes towards treating chronic and mental conditions that can be better managed via preventive healthcare programs. Wearables are the key to deploying more effective preventive healthcare programs, and they’re just getting started.

Decentralized Databases for Record Storage

As the amount of data gathered by companies grows, security is increasingly becoming a necessity. Cybercrime is increasing across the globe, and this trend is particularly alarming for healthcare due to the sensitive nature of medical records and data.

A persistent problem healthcare professionals have faced is the existence of fragmented medical records. People receive treatment from different doctors for different diseases at various points in their lives, and any of their prior treatments can cause adverse reactions in the present.

The lack of a centralized database that records every person’s medical history is both a risk and an impediment. It creates a single point of failure, but it also increases the chances of an inappropriate treatment being prescribed. 

The blockchain is an elegant solution to this problem. Thanks to its nature, a blockchain network is close to impossible to hack. The network can also detect conflicting information and alert administrators automatically. 

Australia and the UK have begun experimenting with migrating patient records to the blockchain and handling data transfers between providers.

In the United States, patient privacy is a hurdle, but there is an increasing number of startups that are bringing app-based security to these records. It’s no wonder that the blockchain for the healthcare market is expected to reach $5.5 million by 2027.

Digital Transformation = Instant Healthcare Access

All of these trends ensure that shortly, people will have the power to address all aspects of their health within the palms of their hands. The rise of preventive healthcare also promises to relieve the burden hospitals and healthcare providers currently experience. 

With data increasingly being analyzed and transformed to actionable advice, the world is set to become a healthier place. 

This article was originally published at Grit Daily and is reproduced with permission.

Some things are just guaranteed to set me off. Denying facts, poor quality chocolate, and unsubstantiated myths top the list, although not necessarily in that order. I’ve had my fair share of exposure to all three recently, and although I want to get to the strange myths about digital transformation, I need to begin with what started off this recent spate of personal triggers. I was sharing my personal history with a new business acquaintance, elaborating on my experiences of having lived across a few countries, when they unknowingly chose to repeat a myth about one of those countries.

“Oh yes, Singapore!”, they said, “I know it’s illegal to chew gum there.” 

Heck, no! By the time I had finished expounding on the fact that this was simply not true, and that it had never been illegal to chew gum, and that it was merely illegal to distribute gum in Singapore, and that this restriction had been imposed in the ‘80s as a reaction to address the chronic problem at the time of chewing gum interfering in the closing of train doors in the brand new subway system, and even then there were exceptions for medical or therapeutic purposes… well, all I can assume is that the person was chastened enough to be a very short-term acquaintance.

How realities get distorted into strange myths

All right, I realize that I may have overreacted a bit. The Singapore myth is understandable because sometimes, precise facts are simply less attractive than lazier simplifications. Arresting people for chewing gum is more sensational than restricting the sale of the stuff. There are other similar parallel examples. The Star Wars movie line “Luke, I am your father” was never spoken, but it sounds much cooler than the actual line “No, I am your father”. “Play it again, Sam” (Casablanca) sounds much better than the actual line “Play it once, Sam, for old times’ sake, play As Time Goes”. And, “…you’ve got to ask yourself one question: ‘Do I feel lucky?’” (Dirty Harry) is not even in the same universe as the fake line “Do you feel lucky, punk?”. All this is indeed understandable (but still annoying in my book).

And then, this gets more irritating, because the reverse of this phenomenon also occurs i.e. Truths that are simply too strange to sound accurate. For instance, there are actual laws, rules, and regulations that exist, but are just too weird to be believed.

How facts, rules, and regulations can age poorly

I recall scratching my head about a few weird laws in Boston while living there in 2005. For example, there’s a law that states that duels can be carried out on Sundays as long as the Governor is present. Yes, really!

And oh, by the way, if you ended up losing the duel, and being featured as the guest of honor at your funeral, you could Rest In Peace, safe in the knowledge that there’s another strange law which states that mourners may not eat more than three sandwiches at a wake. Alternatively, if you were the sweaty victor in that duel, and felt like bathing afterward, you’d be out of luck. Yes, there’s another law that says that bathing is illegal unless a doctor gave you a prescription. And, of course, if that wasn’t strange enough, there’s a conflicting law that confidently stipulates that it’s illegal not to bathe before going to bed in Boston.

These aren’t frivolous legislations. They are actual laws that served very specific goals when they were enacted a long time ago. Remember that Massachusetts was one of the original thirteen colonies, and so when I say a long time ago, I really mean it. With time these regulations became unnecessary and outdated in a different context, but they were never removed from the law books. They still exist. Which brings us to our final point about strange myths in digital transformation.

Strange myths about digital transformation

Myths in the context of Digital Transformation exist for the same reason as any of the previous examples i.e. either they are lazy oversimplifications of actual facts, or they are previously true statements that are no longer true in a modern context. Regardless, their incorrect pronouncements remain. Computerworld has a short round-up of digital transformation myths that I found worth examining.

Myth 1: Transformation means completely recasting the business.

This one is simply a lazy oversimplification. Digital Transformation includes creating new business models, or smarter (digital) versions of existing products, or significantly more productive business operations. But, it’s inaccurate to assume that you need to move into a totally different business. Neither does it imply that you must become an online company. This myth is understandable because of old examples, such as the origin of Amazon which was initially little more than a website. However, today, Amazon is as much a distribution, logistics, and warehousing company as any retailer out there. It’s not in the online website business.

Myth 2: Digital transformation is a specific project or a single initiative.

This is another lazy oversimplification. Just because Digital Transformation is often a dramatic change, doesn’t mean that one big monolithic project is called for. On the contrary, a big and expensive initiative is most likely to fail, especially when compared to a portfolio of iterative and agile ideas. The Computerworld article shares the example of the banking industry, which has undergone a digital transformation by moving from a paper-based system of cash and checks to a nearly ubiquitous digital experience where people can move money via smartphones. This has happened over two decades.

Myth 3: The CIO role is safe because digital transformation relies on technology.

And finally, for those CIOs among us who are starting to equate the explosion of digital transformation work with job security, I have some bad news. Technology does drive Digital Transformation, but to be successful at it, you need strategic, organization, and change management skills to complement your technical competencies. There is a role that technology and technologists will play, but if we’re relying on that alone, then Houston, we have a problem. Which interestingly is another misquote. The accurate statement was “OK, Houston, we’ve had a problem here.”

Digital transformation dominates most CIO priority lists pertaining to questions such as:  How will digital transformation affect IT infrastructure? Will technology live on-premise or in the cloud? Depending on where that data lives, an organization requires different skill sets. If you’re building these resources in-house, then you need an infrastructure as well as people to build it, manage it, and run it.

As you consider implementing a digital transformation strategy, it is helpful to understand and adopt an event-driven data approach as a part of the cultural and technical foundation of an organisation. One definition of event-driven data architecture describes it as one that supports an organisation’s ability to quickly respond to events and capitalise on business moments. The shift to digital business is also a shift from hierarchical, enterprise-centric transaction processing to more agile, elastic, and open ecosystem event processing.

Nearly all business-relevant data is produced as continuous streams of events. These events include mobile application interactions, website clicks, database or application modifications, machine logs and stock trades for example. Many organisations have adopted an event-centric data strategy to capitalise on data at the moment it’s generated. Some examples include King, the creators of the mobile game Candy Crush Saga that uses stream processing and Apache Flink to run matchmaking in multi-player experiences for some of the world’s largest mobile games. Also, Netflix runs its real-time recommendations by streaming ETL using Apache Flink and event stream processing. And when advertising technology company, Criteo needed real-time data to be able to detect and solve critical incidents faster, they adopted stream processing and introduced an Apache Flink pipeline in their production environment.

So should we all adopt a stream-first mindset? Maybe, but it’s not as simple as that.

There are a number of considerations to take into account when transitioning to real-time data processing – anything from the purely technical to organisational requirements. Developers need to be prepared to support and build upon a faster, more distributed architecture designed to deliver continuous value to its users. In addition, a solid data strategy, clear vision and adequate training are required.

So what differences can we highlight between a traditional and an event-centric data strategy? What should CIOs and IT leaders keep in mind while going through such a transition? Let’s take a closer look…

There are new responsibilities for the IT department
When you change to event stream processing, this affects how your business perceives IT and data systems. Your IT department will take on additional responsibilities. Your infrastructure will enable multiple tiers of the organisation to access and interpret both real time and historical data independent of heavy, centralised processes. Making the most of this approach requires stricter control over how data is processed and applied to avoid people getting stranded with piles of meaningless information.

Your SSOT (single source of truth) is recalibrated
Your data strategy will ultimately impact the outlook of data authority as well as the level of chaos within your organization stemming from increased data creation. From the single-point data store in a monolithic data architecture, your focus will change to a stream processor, making data and event-driven decisions as you react to events in real time or using sensor data to find the cause of a system failure that might impact the operation of your business.

Data is constantly on the move
In monolithic architectures, data is at rest. But in event stream processing, data is “in flight” as it moves continuously through your infrastructure, producing valuable outcomes when data is most valuable: as soon as it is generated. You need to reimagine your systems and infrastructure to handle large volumes of continuous streams of data and make appropriate data transformations in real time.

Your focus is reacting to data
Your data infrastructure opens a different perspective, moving from a “preserving-my-data” to a “reacting-to-my-data” state of mind. Stream processing enables your digital business to act upon events immediately as data is generated, providing an intuitive means of deriving real-time business intelligence insights, analytics, and product or service customisations that will help differentiate your company from its competition. Therefore, your system needs to focus on endorsing this continuous flow while minimising the tradeoffs required to process it.

Figure 1: data at rest – focus on preserving the data

Figure 2: data “in-flight”- focus on reacting to my data in real time

A change in culture is needed
Adopting an event-driven architecture requires careful planning and groundwork in order to drive a successful transition. For a successful transition, both cultural and technical considerations should be taken into account. It expands way beyond the data infrastructure teams and requires the early involvement of multiple departments within the organisation. A ‘new’ data approach requires CIOs to align with their IT and data leaders on a shared vision. This is very important whilst the enterprise evolves from a passive request/response way of gathering data insights to an active, real-time data-driven way of operating.

Stream processing with Apache Flink enables the modern enterprise to capitalise an event-centric data architecture, and leverage the value of stream processing: understanding the world as it manifests in real time through powerful, distributed and scalable data processing.

If you want to learn more about the latest developments in the stream processing space, the upcoming Flink Forward conference in San Francisco is a great source of thought leadership and inspiration about how to use stream processing to power a real time business of tomorrow.

What is the future of AI in Europe and what does it take to build an AI solution that is attractive to investors and customers at the same time? How do we reimagine the battle of “AI vs Human Creativity” in Europe? 

Is there any company that is not using AI or isn’t AI-enabled in some way? Whether it is startups or corporates, it is no news that AI is boosting digital transformation across industries at a global level and hence it has traction not only from investors but is also the focus of government initiatives across countries. But where does Europe stand with the US and China in terms of digitization and how collective effort could push AI as an important pan-European strategic topic? 

First things first: According to McKinsey, the potential of Europe to deliver on AI and catch up against the most AI-ready countries such as the United States and emerging leaders like China is large. If Europe on average develops and diffuses AI according to its current assets and digital position relative to the world, it could add some €2.7 trillion, or 20 per cent, to its combined economic output by 2030. If Europe was to catch up with the US AI frontier, a total of €3.6 trillion could be added to collective GDP in this period.

What comprises the AI landscape and is it too crowded?

I recently attended a dedicated panel on “AI vs Human Creativity ” as a part of the first day of the Noah conference 2019 in Berlin.  Moderated by Pamela Spence, Partner, Global Life Sciences Industry leader, EY, the discussion started with an open question on whether the AI landscape is too crowded? According to a report by EY, there are currently 14000 startups globally which can be associated with the AI landscape. But what does this mean when it comes to the nature of these startups? 

 Minoo Zarbafi, VP Bertelsmann Investments Digital Partnerships, added perspective to these numbers,” There are companies that are AI-enabled and then there are so-called AI-first companies. I differentiate because there are almost no companies today that are not using AI in their processes. From an investor perspective, we at Bertelsmann like AI-first companies which are offering a B2B platform solution to an unsolved problem . For instance, we invested in China in two pioneer companies in the domain of computer vision that are offering a B2B solution for autonomous driving.” Minoo added that from a partnership perspective Bertelsmann looks at AI companies that can help on the digital transformation journey of the company. “The challenge is to find the right partner with the right approach for our use cases. And we actively seek the support of European and particularly German companies from the startup ecosystem when selecting our partners”, she pointed out. 

The McKinsey report too states that one positive point to note is that Europe may not need to compete head to head but rather in areas where it has an edge (such as in business-to-business [B2B] and advanced robotics) and continue to scale up one of the world’s largest bases of technology developers into a more connected Europe-wide web of AI-based innovation hubs.

Growing share of funding from Series A and beyond reflect increased maturity of the AI ecosystem in Europe. Pamela Spence from EY noted, “One in 12 startups uses AI as a part of their product or services, up from 50 about six years ago. Startups labelled as being in AI attract up to 50 per cent more funding than other technology firms. 40 per cent of European startups that are claimed as AI companies actually don’t use AI in a way that is material to their business.”

AI and human creativity go hand-in-hand

Another interesting and important question is how far are we from the paradigm of clever thinking machines? Why should we be afraid of machines?  Hans-Christian Boos, CEO & Founder, Arago compares how machines were earlier supposed to do tasks which are too tedious or expensive and complex for humans. “The principle of machine changes with AI. It used to earlier just automate tasks or standardise them. Now, all you need is to describe what you want as an outcome and the machine will find that outcome for you- that is a different ballgame altogether. Everything is result-oriented,” he says.

Minoo Zarbafi adds that as human beings, we have a limited capacity for processing information. “With the help of AI, you can now digest much more information which may cause you to find innovative solutions that you could not see before. One could say, the more complexity, the better the execution with AI. At Bertelsmann, our organisation is decentralised and it will be interesting to see how AI leverages operational execution.”  

AI and the Political Landscape

Why discuss AI when we talk about the digital revolution in Europe? According to the tech.eu report titled ‘Seed the Future:  A Deep Dive into European Early-Stage Tech Startup Activity’, it would be safe to say that Artificial Intelligence, Machine Learning and Blockchain lead the way in Europe. The European Commission has identified Artificial Intelligence as an area of strategic importance for the digital economy, citing it’s cross-cutting applications to robotics, cognitive systems and big data analytics. In an effort to support this, the Commission’s Horizon 2020 funding includes considerable funding AI, allocating €700M EU funding specifically.

Chiara Sommer, Investment Director, Intel Capital reflected on this by saying, “In the present scenario, the implementation of AI starts with workforce automation with a focus on how companies could reduce cost and become more efficient. The second generation of AI companies focuses on how products can offer solutions and solve problems like never before. There are entire departments can be replaced by AI. Having said that, the IT industry adopts AI fastest, and then you have industries like healthcare, retail, a financial sector that follow.” 

Why are some companies absorbing AI technologies while most others are not? Among the factors that stand out are their existing digital tools and capabilities and whether their workforce has the right skills to interact with AI and machines. Only 23 percent of European firms report that AI diffusion is independent of both previous digital technologies and the capabilities required to operate with those digital technologies; 64 percent report that AI adoption must be tied to digital capabilities, and 58 percent to digital tools. McKinsey reports that the two biggest barriers to AI adoption in European companies are linked to having the right workforce in place.

It is certainly a collective effort of industries, the government, policy makers, corporates to have effective and impactful use of AI. Instead of asking how AI will change society Hans-Christian Boos rightly concludes, “We should change the society to change AI.”

Note: The quotes used in this article are derived from a panel discussion at NOAH Conference Berlin 2019.

Design thinking continues to be all the rage amongst organizations of all kinds – from academia to startups, to agencies and consultancies, or large enterprises. The concept is popular today not because it’s new per se, but its approach to problem-solving fits well with the digital transformation that companies are going through.

Artificial Intelligence and Machine Learning powered solutions are forcing businesses to reorganize entire business processes, and the design thinking orthodoxy helps in these journeys. For many companies, there’s a vast opportunity to develop such solutions that leverage advanced data analytics – both for internal use and for end consumers. When developing these solutions, design thinking creates a clear vision and understanding of what the company is creating a solution for.

Applying design thinking when building advanced data analytics solutions both for internal company teams and consumers places a priority on what is needed from a human interaction perspective. It complements what is technically possible and adds incremental value to the bottom line.

With this in mind, here are three design thinking best practices for companies ideating and developing new big data solutions – and why they are important:

Always begin by asking what users actually need
When designing solutions that leverage massive amounts of enterprise or consumer data, the first thing to ask is what the end user actually needs. While economic viability and profitability inevitably play a role in all business decisions, it’s crucial to take a step back and place the focus on the end-user. Whether designing solutions that leverage big for consumers (e.g. Netflix or Spotify recommendation engines, Apple Health App) or solutions for enterprise users (Tableau, IBM Watson), it’s important to first define what the end users truly need and what it’s trying to accomplish to satisfy that need.

It would be counterintuitive for design decisions to be driven exclusively by the CTO or CEO. Instead, these should be influenced by the people who are actually going to be using the solution, which requires a mind-shift. Although businesses might require a certain kind of functionality, this does not mean they should automatically proceed with an idea. A business use case or requirement does not necessarily translate to value for the end user. For this reason, adopting a user-centric approach where the user is placed before the business is a logical strategy. The pressure to please leadership can be a stumbling block in design thinking, but in the end, if you satisfy end users, business results will naturally follow.

Adopt a holistic view and avoid tunnel vision
Observing a step-by-step process is important when it comes to design thinking. Adhering to well-thought-out steps and guidelines helps uncover the needs and desires of users. When approaching design thinking, it’s important to have a holistic view rather than to focus on one particular use case. Dedicating time to carry out thorough research facilitates a better and broader understanding of the solution’s potential. Avoiding tunnel vision and being receptive to different ways of approaching any single problem will ultimately save resources because the earlier the team is able to identify an issue, the easier and less costly it is to change direction. The general rule of thumb is that it costs $1 to change something in the requirement phase, $10 in the design phase, and $100 in the development phase. For this reason, it is advantageous to spend ample time in the requirement phase to determine feasibility and save money early on in the entire process.

A more agile approach is not only more transparent but lets you shift your roadmap quicker, go through many iterations of design and iterate faster. In design thinking, you go through many iterations. If it doesn’t work, it takes a fraction of the time and money to shift early on then it would take to fully develop the solution and understand new requirements later on.

Understand the importance of domain expertise and collaboration
It’s inevitable to face challenges in the design phase of any big data project. To overcome these obstacles, design and business teams must collaborate with subject matter experts to gain knowledge on a particular vertical or topic. It’s also important to be cognizant of the processes and applications already in place for a particular company within the context of it’s vertical – be it financial services, healthcare, media/entertainment, etc. The ideation process is key because it allows teams to work with subject matter experts to identify what the business or domain needs are.

Lastly, coming into the design process with an open mind sets you up for success. If your product or company goes through the process and arrives at the same offerings year over year, it won’t make any progress. Having an open mind opens up the possibility of breaking down barriers and discovering new features that weren’t previously thought of without that specific domain knowledge.

Conclusion
Design thinking should be a collaborative and informed process that requires teams to put themselves in the shoes of the end-users. It’s important to always place the focus back on the end-users, understand their workflow, and show how a new functionality or solution to leverage and visualize big data would add value to them. Teams should also adopt a holistic view and collaborate with each other, rely on domain experts, and embrace an open-mind to ensure maximum success. Design thinking when building any big data solution can help uncover the true needs of the end user, reduce risk and costs associated with product development, and turn incremental changes into solutions that can potentially transform or disrupt an industry

What are the fears around data while exploring potential use cases to demonstrate the value for the ‘smart citizen’? Here is a look.

With The United Nations reporting that two-thirds (68%) of the world’s population are expected to live in cities by 2050, scientists are seeking new and innovative ways to improve the quality of life in our urban jungles. With a recent death in the UK linked to illegal levels of air pollution, it’s more important than ever to utilise technology that drives progress and innovates to develop a more sustainable future – creating smart cities.

However, with a large proportion (68%) of the UK public unclear about what a smart city is or the benefits it can bring, it’s obvious that further education is needed. In a post-GDPR world, citizens are increasingly aware of the vast amount of data being collected throughout their day-to-day activities. Once an understanding is established that these smart initiatives save time, money, and provide peace of mind, citizens will be more open to working with their government bodies to future-proof their communities.  

Getting smart about cities

With the aim to provide a more liveable and responsive environment, the smart city industry is projected to be worth $400 billion by 2020, Citywise reports. Underpinned by real-time data, truly smart cities understand how demand patterns change and are able to respond with faster, lower-cost solutions. Benefits from this approach include improvements to safety and congestion for efficient traffic management, healthcare advances from patient experience to data-driven public health interventions, and air-quality monitoring and energy-use optimisation to minimise environmental impact. All alongside further social connectedness and civic participation, new job opportunities provided through e-careers, as well as reduced cost of living thanks to improvements such as dynamic electricity pricing and usage tracking.

To provide these benefits, smart city initiatives must gather the relevant data from multiple sources. Sensors and beacons, communication networks, and open data portals – which can be introduced by city councils and governments to the existing infrastructure – are primary sources. For people management, smartphone data is invaluable in both gathering and providing instant information about transit, traffic, health services, safety alerts, and community news. Other sources include connected networks and devices – such as home-security systems, personal-alert devices, and lifestyle wearables – which offer value that many city stakeholders are willing to pay for. Mobility applications also provide greater value, thanks to the rise in popularity of e-hailing services like Uber and Lyft, and e-bikes or scooter schemes.

From byte to yotta

The benefits this data holds are visible through the deployment of connectivity resources available to many citizens now. But the data is held in several separate silos, each relating to a specific aspect of urban life. To improve the city as a whole and realise its smart potential, an interconnected data system is needed; one that integrates big data from multiple sources – state and citizen.

From traffic and pollution sensors to shared bike schemes, extreme amounts of data can already be collected, processed, and analysed in real-time and at scale. However, to provide a truly holistic citywide view of these, a combination of multiple sources is needed. Once this is achieved, advancement such as improving the daily commute via smart-mobility application can be implemented through networks of internet of things (IoT) sensors on physical assets. Real-time information can then be relayed via mobile apps or digital signage, enabling commuters to efficiently adapt their routes on the move.

This smart approach to modern cities also has impacts on crime levels, with a data-driven policing strategy utilising real-time mapping to cut emergency response rates. For a population of five million, this could mean saving up to 300 lives per year. To protect the environment, citizens and cities can work together to optimise the use of finite resources. The use of sensors is particularly key for the environment, by identifying sources of pollution to enable cities to arm their citizens with real-time protective measures so they can make the best decisions for their health. This digital feedback loop also works for conserving water, with leaking pipes one of the biggest water waste contributors. By collecting data on the health of city infrastructure and its surrounding areas insights can be gleaned, for example, on soil moisture levels to identify the waterlogged environments that surround a leak.

Identifying a truly smart citizen

It’s not only about installing digital interfaces in traditional infrastructure or streamlining city operations. Smart cities are primarily opportunities to use technology and data purposefully to make better decisions, and deliver a better quality of life for citizens.

By establishing channels for two-way data communication that feeds into the data infrastructure, a truly smart city can respond more dynamically to how resource demand is changing. For this smart future to become a reality, governments and councils need a reliable big data source to base long- and short-term decisions on – to safeguard the future health of the urban ecosystem. Only once a holistic view of the city is achieved can stakeholders make the key decisions and positive changes needed to ensure the future sustainability of its metropolitan environment.