Leo Rajapakse

  Leo is TOGAF certified Enterprise architect with 16+ years of leadership experience in the IT industry. He has led teams ranging upto 20 members over his career to deliver various projects in enterprise architecture. He is particularly known for his...

[leorajapakse]

Beyond the hype key componente of an effective ai policy.

Beyond the hype: Key components of an effective AI policy

A robust AI policy is essential for businesses to navigate the ethical, legal and operational challenges of AI implementation. Here are some

[leorajapakse]

Data Led Organization

What is a Data Led Organization?

An organization that uses data to guide its decision-making. The results of your data analysis drive your decision-making; you must have faith in the data. As a result, data-driven businesses benefit from more significant revenue, better customer service, best-in-class operational efficiencies, and increased profitability.

How can one get to this level of data-driven panacea?

You want your data insights to be quickly disseminated across the organization, with employees able to analyze and learn from the accessible data. Such insights necessitate the collection of;

1. The appropriate data, 

2. The accuracy of the analysis, 

3. The inclusion of the senses in the decision,

4. The implementation of tangible measures to fulfill the potential. 

A data-driven organization must define a clear strategy or the direction in which the business is traveling and then establish a set of top-level metrics—key performance indicators (KPIs)—to track whether the company is on the right track and monitor progress. The responsibility for driving such top-level KPIs is delegated to divisions or business units, which may create additional KPIs unique to that business unit. Finally, the KPIs are driven by a collection of operational and diagnostic measures that monitor tasks, programs, tests, and projects.

How Predictive analysis helps?

Predictive analysis optimizes ad spend supply chain replenishment and customer turnover. They entail answering the "why" questions, providing suggestions and forecasts, and delivering a story based on the findings. You can realize the value of predictive analytics by knowing the primary use cases for the relevant sectors.

● Boost customer retention: Companies can make the required changes to improve customer happiness while maintaining income.

● Identify Profitable Customers: Predictive analytics allows businesses to optimize their marketing spending and focus on gaining customers who will create the most revenues and eventually have the highest lifetime values.

● Improve Customer Segmentation: Businesses may focus on the correct target audience, the proper segments, and even entire markets they were unaware existed.

● Improve Decision Making: Predictive analytics may also assist you in determining the best approach to communicate with your customers by studying all elements of consumer behavior, from purchasing habits to social participation, and choosing the ideal times and channels to contact these customers.

● Predictive Maintenance: In asset-intensive industries, firms can foresee and plan for maintenance operations and expenses in advance by employing IoT sensors in conjunction with predictive analytics.

● Risk Prediction and Quantification: Companies may identify and prioritize essential risks, analyze the possible impact, and decide on a course of action based on their severity by combining these analytics with a defined risk management methodology.

● Predict Demand and Optimize Pricing: Predictive analytics can modify pricing depending on demand and give targeted discounts, promotions, and segment-based pricing to consumers.

Best Practices for Predictive Analytics are as follows: 

1) Define Your Goals,

2) Put Together the Right Team and 

3) Plan Your Deployment.

Analytics insights should take seriously during their implementation because to be data-driven, an organization must have the necessary processes and culture to supplement or drive crucial business decisions with these insights, which will directly influence the business.

Culture in Data-Led Organization

The key, then, is culture. This multidimensional issue involves data quality and sharing, analyst hiring and training, communication, analytical, organizational structure, metric design, A/B testing, decision-making procedures, and other factors. We've streamlined ten data commandments to help you develop and sustain a data-driven culture.

1. A data-driven culture begins at (or near) the top.

2. You should prefer metrics with vigilance and creativity.

3. Employees need to understand coding and are conceptually competent in quantitative areas, and Executives of data-centric enterprises must be aware of data jargon.

4. Resolve fundamental data-access concerns as soon as possible.

5. Identify and quantify uncertainty.

6. Keep proofs of concept simple and sturdy, rather than complex and fragile.

7. Specialized training should be available at all times.

8. Use analytics to assist staff rather than simply customers.

9. Be willing to sacrifice flexibility for consistency, whether in the short run.

10. Develop the habit of explaining analytical decisions.

A data-driven company and culture can and should be established not only from the top-down but also from the bottom up. The best ideas come from the men closest to the data. They deal directly with the data sources, discover and resolve data-quality concerns, understand how to best augment the data, and frequently come up with outstanding product ideas.

Furthermore, they can assist in educating the rest of the organization to become more data literate. Part of that comes from honing their skills and putting them to use. Another factor is becoming more business savvy—learning the right questions to ask and business challenges to solve—and then "selling" their insights and suggestions to decision-makers by presenting a convincing case for what the finding or recommendation means to business and why it matters.

Benefits of Data Led Organization

There are significant implications and benefits to be had. According to a PwC blog article, data-driven firms have a 5%–6% higher production and productivity than their less-driven counterparts. They were also more efficient regarding asset use, return on equity, and market value. [7] Nucleus Research & analysis ROI analytics case studies find that analytics returns $13.01 for every dollar spent. So it pays to be data-driven! [6]

Every dataset, database, and spreadsheet has a narrative to tell. The goal is to find that story, or at least the story important to the organization, analyze it, and communicate it. An empirically supported tale is an accurate account with supporting facts and figures.

Thus, "narrative" is intended to capture the significant discoveries, features, or patterns in data, describe what caused them whenever possible, and spell out the future suggestions to the organization. There are three considerations to consider when deciding how to convey some data, information, and analysis:

First, what are you attempting to accomplish?

Second, who is your target audience?

Third, what is your preferred medium?

A Data-driven organization is a vision. You can be more data-driven, collect high-quality data, have more testing, and conduct more testing. Furthermore, you can improve your decision-making process. Data-drivenness necessitates flexibility and iteration on an organizational level; you may find it necessary to reorganize your data teams and their place in the business hierarchy.

References

1. Nucleus Research, “Analytics pays back $13.01 for every dollar spent,” O204 (Boston, MA: Nucleus Research, 2014), 5.

2. Siroker, D., and P. Koomen, A/B Testing (Hoboken: John Wiley & Sons, 2013).

3. O’Neil, C., and R. Schutt Doing Data Science (Sebastopol, CA: O’Reilly, 2014).

4. Sharon, M., Thinking With Data (Sebastopol, CA: O’Reilly, 2014).

5. Siegel, E., Predictive Analytics (Hoboken: John Wiley & Sons, 2013).

6. https://nucleusresearch.com/research/single/analytics-pays-back-13-01-for-every-dollar-spent/

7.https://www.pwc.com/us/en/services/consulting/analytics.html #datascience #data #dataanalytics #analytics #computing #computer #computerscience #cloudcomputing #humanresources

[leorajapakse]

Smart Cities

Top 10 Smart Cities, Key Features, What can we learn from it ?

Smart City

A smart city employs information and communication technology (ICT) to boost operational efficiency, communicate information with the public, and improve the quality of government services and citizen welfare. It is "one that makes the best use of all the interconnected information available today to better understand and regulate its operations, as well as optimize the use of limited resources."[1][2]

Smart cities can extend beyond the information and technology revolution. By gathering and exchanging information with high-tech support, a smart city can help achieve the objective of social and environmental sustainability and develop inclusive and livable communities. The ultimate goal of smart city development is to enhance social governance, improve city quality, and become more people-oriented.[3]

Top 10 Smart Cities

·      New York

New York City is one of the most iconic cities in the world. The city has long attracted tourists to experience its rich culture and history, as well as its iconic skyline. New York City is also a hub for innovation and entrepreneurship, with some of the world’s top universities, tech companies, and venture capital firms located there. New York City has been at the forefront of the smart city movement. In 2020, New York City initiated a smart city pilot initiative with hundreds of smart sensors and developed technology across various districts. The obtained data enabled it to manage services such as waste management and collection more efficiently. In addition, online charging stations are replacing phone booths to promote and enhance connectivity for the population. Other programs including smart technology include NYC Challenges, LinkNYC, Midtown in Motion, and Cyber NYC.

·      San Francisco

As Silicon Valley continues to shape the future of technology and innovation, San Francisco is at the center of it all. The city has long been at the forefront of technological innovation. Today, it’s also one of the most connected cities in the world, with extensive efforts underway to make it a “smart city”. The city’s Department of Emergency Management has been at the forefront of the smart city movement. The department has been piloting sensors and other smart technologies to improve its emergency response capabilities. This includes the ability to automatically detect and respond to fires using cameras, along with sensors to detect the chemical makeup of smoke. Together, these technologies can help to provide more accurate information about the location and nature of fires, including their chemical makeup.

·      Chicago

Chicago is one of the most innovative smart cities in the world as well as one of the most popular cities in the United States. O'Hare International Airport, one of the busiest air hubs in the world, is based in Chicago. It has been at the forefront of smart city innovation, including the adoption of autonomous vehicles. It is one of the world's most popular hubs for passengers and aircraft. Chicago is utilizing various smart city technologies and methods to continue its growth and development. For example, its Open Data platform aims to address and overcome the global COVID-19 pandemic's digital divide, and its urban sensing network seeks to improve air quality, congestion, climate change, and pollution.

·      Barcelona

Barcelona is one of the most iconic cities in Spain, and it’s also one of the most connected cities in the world. The city has a long history as a hub for entrepreneurship, tourism, and culture, with millions of people traveling to the city each year. Barcelona has also been a hub for digital innovation since the early days of the internet. Barcelona has been at the forefront of the smart city movement, taking advantage of new technology to reduce operational costs and improve the experience of city residents. For example, the city has been testing sensors and other smart technologies to reduce energy costs and improve air quality in public areas, including parks. The city’s municipal government has been working to integrate new technologies with existing city systems to improve the experience of city residents. For example, the government has integrated its public transportation systems with transportation apps like Google Maps to improve the user experience of city residents.

·      Melbourne

Melbourne has been one of the most connected cities in Australia for many years, thanks in part to its status as a major hub for e-commerce. The city has been at the forefront of the smart city movement, taking full advantage of new technology to improve the municipal services provided to city residents. One of the major initiatives in the city has been to leverage sensors and other smart technologies to improve air quality. The city’s municipal government has partnered with companies to deploy sensors across the city that can monitor air quality, traffic flow, and a variety of other metrics. The city has also been partnering with a wide range of companies to integrate new technologies into its municipal services. For example, the city’s transportation department has partnered with Amazon to pilot Alexa Skill which can help city residents find information about public transportation.

·      Berlin  

Berlin has a long history of being an entrepreneurial hub and a center for innovation. Today, the city has been at the forefront of the smart city movement, taking full advantage of new technologies to improve the quality of life for residents. One of the major initiatives in Berlin has been to leverage sensors and other smart technologies to improve urban air quality. The city’s Department of Environment has partnered with various technology providers to deploy sensors across the city that can monitor air quality. The city has also been working to integrate new technologies with its systems to improve municipal services. For example, the city’s transportation department has partnered with companies to pilot a smart assistant service that can help city residents find information about public transportation.

·      Singapore  

Singapore is an iconic city that has a long history of transformation. The city is known around the world for its unique blend of East and West cultures and its open-minded approach to innovation. Singapore has been at the forefront of the smart city movement, taking full advantage of new technologies to transform the city into a more livable and sustainable urban environment. One of the major initiatives in Singapore has been to leverage sensors and other smart technologies to improve urban air quality. The city’s National Environment Agency has partnered with various technology providers to deploy sensors across the city, including in public parks, that can monitor air quality and traffic flow. The city has also been working to integrate new technologies with city systems to improve municipal services. With an aging population, the government focuses on digital technology and programs to boost productivity in the country's advanced economy. As a result, smart technology is already being integrated into the country's homes.

·      Tokyo

Tokyo is one of the most technologically advanced cities in the world, and it is aiming to become a smart city. The city is making strides to reduce its carbon footprint with initiatives like reducing energy use in public buildings, developing more renewable energy sources, and increasing energy efficiency in transportation. Tokyo is also working to increase its water efficiency and reduce the amount of food that goes to waste. Additionally, the city is investing in improving internet access and digital services for its residents.

·      Dubai

Dubai is the place where the desert meets the sea. It has long been one of the most important commercial hubs in the Middle East, but in recent years it has been working hard to make itself a smart city. Dubai knows that it is critical to creating a smart infrastructure. This includes installing smart technology in the city’s transport system, using sensors in its buildings, and making it easier to access information. It also means reducing pollution by increasing the use of electric vehicles. Dubai is keen to make it easy for people to access information, regardless of where they are. Dubai just completed a seven-year digital transformation of all government and financial services, including communications, urban planning, transportation, and many more. Artificial Intelligence is being used in vehicles, dramatically reducing fatigue-related traffic collisions. The police have taken advantage of automation, and the city already has three autonomous police stations.

·      Shanghai

Shanghai is a major economic hub in Asia, and it is home to one of the most ambitious smart cities in the world. The city plans to invest $100 billion in smart city initiatives by 2025. Shanghai’s smart city initiatives focus on areas like sustainable energy, integrated healthcare, and intelligent transportation. The city is also one of the few that is working to build a digital economy.

The 7 Essential Elements of a Successful Smart City Strategy

Smart Cities throughout the world are under comparable pressures:

●       Rapid urbanization refers to the strain that increasing people place on systems, infrastructure, and housing.

●       Congestion and mobility issues

●       Changes in the climate

●       Water, air, sanitation, and pollution

●       Energy and environmental sustainability

●       Technology is rapidly evolving.

●       Competence and the potential to recruit global talent

Key Application Sectors of Smart Cities;

Governance

Smart city governance analyzes a smart city's complete public domain activities and services. It investigates how political and societal governance systems may translate into digitally running smart cities. Smart city governance is critical for the management of smart cities so that citizens are better informed about the operations of their smart city and the entire system is more transparent.

A fundamental examination of smart city social governance is required to ensure that smart city development serves a city's social governance aims. It is also critical to systematically assess the state of smart city development for leaders to make better, more informed decisions aligned with city development goals and to give enhanced standards and guidelines for smart city development.

Furthermore, smart city governance activities seek to address the effects of smart city technology on inhabitants who must deal with the digital infrastructure that has been deployed. Some necessities focus on the importance of Governance in Smart cities.

Identifying concrete benefits for citizens

Explaining the technology's intended purpose and funding

Concerns related to data security and encryption

Consumer Confidence

Farming

Intense urbanization is projected in big megacities with increased food consumption. It will shift the global diet toward high-value animal proteins and meat-based quick meals combined with growing income levels.

The world faces challenges in meeting food and agriculture.[5] Food insecurity is still rising, affecting approximately 26% of the global population.[6] The consequent increase in food demand emphasizes the need for novel agricultural models.

The following causes contribute to the exponential growth in food consumption [6]:

dietary changes leading to increased consumption of processed meat and dairy products

changes in arable lands caused by climate change and increasing water scarcity, which may result in the loss of nearly half of current farm productivity by 2050

farmer population reduction due to urbanization

large-scale over-farming

soil degradation practices primarily affect small family businesses and communities.

This tendency raises the general population's percentage of obesity-related disorders while also increasing the strain on livestock farming operations, which has a significant Greenhouse Gas footprint and excessive energy and water resource requirements.

Healthcare

Healthcare is an essential requirement for every community on the planet. Smart healthcare responds intelligently to the needs of the healthcare ecosystem by utilizing technology. The global IoT-based healthcare market is predicted to increase at a 29.9 percent CAGR to $322.2 billion by 2025, owing to the increasing proliferation of smart cities worldwide.[7]

Smart healthcare has numerous advantages for smart cities, which apply to individuals, governments, the medical community, and vendors:

Smart healthcare helps to meet these expectations by providing a pleasant healthcare experience.

The use of technology in healthcare also improves diagnosis and treatment accuracy, resulting in greater efficiency of the medical and nursing staff.

It also increases patient engagement.

The use of digital technologies improves communication across departments in a healthcare organization, resulting in streamlined services.

Innovative health features like RTLS badging can track patients and hospital employees to improve service speed and patient safety.

Finance & Banking

Finance, among other factors, is the backbone of creating a smart city. All choices and operations are dependent on money. A modern, web-based financial system can bring about positive improvements in a city, making it a desirable place to live. The following characteristics should include in the Smart city finance and banking;

Modern Banking system: The banking structure and functioning must be robust enough to adequately meet all citizens' needs.

Smart Market: A smart market is required for smart buyers who transact using a digital model. Such organizations employ payment software and portals in various retail complexes and stores. In general, smart cities have more modern stores than small cities or towns.

Public payments: Governments in smart cities attempt to make it easier for citizens to pay their electricity bills, taxes, rentals, and other fees through e-payment methods so that the public can use resources appropriately and safely.

Rental taxi services and deliveries: It is undeniable that all urban and smart cities rely heavily on-spot upon delivery and cab services.

Today, more than 80% of food deliveries or other things are done through online payments, and the same is valid for taxi services, which accept payments through numerous applications and websites.[8]

Energy Management

One of the most pressing concerns in smart cities is energy optimization. These cities use many networked devices to operate autonomously, which consumes a lot of energy. This problem can solve by cutting-edge technologies. These are the Internet of Things (IoT), 5G, and cloud computing for energy efficiency in smart cities.

These Internet of Things devices are intended to make ordinary tasks more accessible and more efficient while also addressing issues related to:

●       Public safety

●       Transit congestion

●       Environmental concerns

A few examples of IoT devices used in smart cities are:

Smart utility meters

Smart grids

Smart air quality monitors

Smart waste management systems.

Smart cities use the latest technology to boost economic growth and quality of life, achieved in four stages:

●       Gathering real-time data,

●       Analyzing the data to obtain insights into municipal operations,

●       Presenting the analyzed results to decision-makers,

●       Taking measures to improve city operations.

Transportation

Smart transportation is defined as a method of incorporating new technologies into transportation networks. Cloud computing, wireless connectivity, location-based services, computer vision, and other mobility-enhancing techniques are all part of this.[10] Smart transportation definition can condense into the three aspects of smart transportation:

Management,

Efficiency,

Safety.

Smart transportation makes moving around a city more convenient, cost-effective (for both the city and the individual), and safer. There are various advantages to smart technology and the benefits it brings to mobility inside a smart city:

Safer

Better managed,

More efficient,

Cost-effective

Gives immediate insights

Security

Considerations for the Environment

The resilience of the Supply Chain

Education

Smart education is "a learning approach tailored to new generations of digital natives." Smart education, as opposed to traditional classroom teaching techniques, is an interactive, collaborative, and visual style that enables teachers to adjust to students' talents, interests, and learning preferences.

Smart cities require education facilities and school systems that ensure kids gain cutting-edge skills such as;

Digital literacy,

Imaginative thinking,

Effective communication,

Teamwork, and

The ability to develop high-quality projects.

To attain this lofty aim, educators must focus technology on the critical components of student achievement.

Smart classroom technology aids in the streamlining of the teaching process by:

●       Assisting teachers in better preparing and enriching their lectures

●      Reacting flexibly to the demands of students and classroom settings

●       Resulting in enhanced efficiency and better teaching performance.

Retail and Logistics

Smart retail encompasses a wide range of technical solutions that enable the traditional brick-and-mortar store to transition into an interactive point of sale digitally. It includes;

Sensors,

Cloud computing

Data analytics

Predictive analytics

Automation

Artificial intelligence

Augmented reality wearable devices

Mobile devices

Offline stores will be able to employ connected gadgets and remote control capabilities hitherto reserved for internet retailers.

In terms of augmented infrastructure and technology, smart retail and logistics provide enormous potential for smart cities. Smart city leaders, managers, and stakeholders can work together to create customized retail and logistics solutions for their urban space.

Manufacturing & Construction

Manufacturing has been the critical driver of innovation, progress, and wealth in countries worldwide, accounting for 15.6 percent of global GDP in 2018.[14] During the early industrialization era, most modern-day advanced economies increased their growth and development. However, with the recent rise of the fourth industrial revolution, sometimes known as "Industry 4.0",[15] traditional manufacturing processes and organizational and commercial structures are being challenged and disrupted.

With smart becoming the norm, we will eventually transition from contemporary cities to smart cities and from current best practices in manufacturing to smart manufacturing. The function of smart manufacturing in smart cities is vast and varies by definition of the topic. Understanding smart manufacturing techniques and applications, on the other hand, demands a comprehensive approach.

The intelligent design of urban street landscape rises and evolves swiftly with engineering management in smart city construction under Industry 4.0. Meanwhile, thanks to the advancement of information technology represented by the Internet, "intelligent" development has become the new trend in modern society.

Buildings

Intelligent buildings are critical to a future in which cities are secure, smart, and sustainable, and building owners can capture and control their energy consumption. Many thousands of buildings and residences have already benefited from smart building technology in energy consumption.

As emerging technologies advance, additional chances for buildings to utilize new technological processes appear. For example, IoT allows us to link an expanding number of previously unconnected gadgets. Connectivity is the key to unlocking the true potential of intelligent buildings by allowing new information from infinite sources to be processed and aggregated, resulting in a new degree of intelligence.

So, what are the problems of modernizing current building infrastructure, and how may smart, IoT-based technologies help solve them?

Financing: Upgrading a building may appear to be an expensive outlay, but advances in IoT mean that this is no longer the case.

Maintenance: Predictive fault-finding can reduce maintenance time and labor while reducing downtime for costly equipment or services.

Design: While older buildings may not have been designed with smart technology, the latest technology, from I.P. door entry systems to light switches to energy management systems, is usually compact and lightweight, making it easy to fit even the most difficult-to-reach places.[18]

Public Safety & Security

The smart city movement is supplying the infrastructure required to improve public safety. However, smart city efforts are not necessarily designed with public safety. Instead, many cities concentrate on municipal issues such as lighting, traffic, and parking.

Indeed, public safety must be a hallmark of every smart city strategy from the start. Therefore, municipal governments must guarantee that issues concerning public safety are considered in the decision-making process surrounding smart city implementation, lighting, and traffic flow.

Environment

Our cities are consuming a rising quantity of natural resources while producing increasing garbage and pollution worldwide. Pollution causes air and water pollution, and the consequences are felt on a global scale.

The concept of smart cities is still evolving in many ways, but one of the essential components of these cities is environmental sustainability. As a result, we may begin building cities better suited to meet today's urban concerns by utilizing advanced technologies;

●        Lower Carbon Emissions

●       Encourage Energy Efficiency

●       Improve Solid Waste Management in Cities

●       Real-time Energy and Environmental Monitoring and Management

●       Smart Cities Can Encourage Citizen Participation

References

[1] "Smart city technology revolutionizes infrastructure." https://www.ibm.com/industries/government/infrastructure-citizen-services

[2] "What is a Smart City? Definition and Examples", https://www.twi-global.com/technical-knowledge/faqs/what-is-a-smart-city

[3] "Smart Cities: Bringing the voice of the people and innovation to urban governance

". JANUARY 1, 2018. https://www.undp.org/china/news/smart-cities-bringing-voice-people-and-innovation-urban-governance?utm_source=EN&utm_medium=GSR&utm_content=US_UNDP_PaidSearch_Brand_English&utm_campaign=CENTRAL&c_src=CENTRAL&c_src2=GSR&gclid=CjwKCAjw_ISWBhBkEiwAdqxb9gc_zDN14_5DRJCLekhTI_9KERKmdHKehCNqScN-OI9kDBJi1VnUexoC5RUQAvD_BwE

[4] "What is Smart City Governance." By Vagisha Arora, July 16, 2021. https://www.planetcrust.com/what-is-smart-city-governance

[5] The Food and Agriculture Organization (FAO), (2020), Tracking progress on food and agriculture-related SDG indicators. A report on the indicators under FAO custodianship http://www.fao.org/sdg-progress-report/en/

[6] Broom D. and K. Breene, (2020), "This is why food security matters now more than ever," World Economic Forum, 23-24 November https://www.weforum.org/agenda/2020/11/food-security-why-it-matters/

[7] "Smart Cities Need Smart Healthcare," Published on April 13, 2021. https://www.linkedin.com/pulse/smart-cities-need-healthcare-sanjay-das/?trk=public_profile_article_view

[8] "THE KEY ROLE OF FIN TECH IN DEVELOPING SMART CITIES" By ImarticusJuly 30, 2021. https://blog.imarticus.org/the-key-role-of-fintech-in-developing-smart-cities/

[9] "Energy Optimization for Smart Cities Using IoT" by Mamoona Humayun, Mohammed Saleh Alsaqer&NzJhanjhi. Received November 12, 2021, Accepted January 28, 2022, Published online: February 13, 2022. https://doi.org/10.1080/08839514.2022.2037255

[10] "What Makes Transportation Smart? Defining Intelligent Transportation" December 22,2020. https://www.iotforall.com/what-makes-transportation-smart-defining-intelligent-transportation

[11] "An Introduction to Smart Transportation: Benefits and Examples." 2022. https://www.digi.com/blog/post/introduction-to-smart-transportation-benefits

[12] "SMART EDUCATION FOR SMART CITIES: VISUAL, COLLABORATIVE & INTERACTIVE

". February 27, 2019, Written by Jon Glasco. https://hub.beesmart.city/en/solutions/smart-people/smart-education/viewsonic-smart-education-for-smart-cities

[13] "The Capacity Of Smart Retail And Logistics – Smart Cities Have Just Begun To Realise It." February 16, 2018. https://smartcity.press/smart-city-retail-strategy/

[14] " Manufacturing, value added (% of GDP) Data," https://data.worldbank.org/indicator (accessed: March 2020).

[15] V. Alcácer, V. Cruz-Machado, Eng. Sci. Technol. Int. J. 2019, 22, 899.

[16] "Smart Manufacturing for Smart Cities—Overview, Insights, and Future Directions

." Suvarna, Lennart Büth, Johannes Hejny, Mark Mennenga, Jie Li, Yen Ting Ng, Christoph Herrmann, Xiaonan Wang. July 8, 2020, https://doi.org/10.1002/aisy.202000043

[17] "Construction of Smart City Street Landscape Big Data-Driven Intelligent System Based on Industry 4.0" by Zhe Li, YuKun He, XinYi Lu, HengYi Zhao, Zheng Zhou, and YinYin Cao. December 14, 2021. https://www.hindawi.com/journals/cin/2021/1716396/

[18] "The role of smart buildings in developing smart cities," March 22, 2021. https://www.smart-energy.com/industry-sectors/iot/the-role-of-smart-buildings-in-the-development-of-smart-cities/

[19] "Public Safety is at the Heart of the Smart City Movement." by Bob Carter. August 16, 2019. https://www.securityinfowatch.com/secured-cities/article/21089764/public-safety-is-at-the-heart-of-the-smart-city-movement

[20] "5 Epic Ways Smart Cities Can Help the Environment" by Mapanauta, August 2, 2018. https://mapanauta.medium.com/5-epic-ways-smart-cities-can-help-the-environment-7192d77ff702

[21] "The Top 10 Smart Cities Worldwide." By Sajana Samarasinghe. June 18, 2021. https://businesschief.com/top10/top-10-smart-cities-worldwide.

[22] "The 7 Elements of a Successful Smart City Strategy." https://cityinnovatorsforum.com/7-elements-of-a-successful-smart-city-strategy/

[leorajapakse]

Cloud Computing Reshaping the IT Budget

Companies are rapidly adopting Cloud Computing saving money and increasing profits which they are re-investing on their core business capabilities. Cloud is a pervasive technology adapted from early stage start-ups to Fortune 500. Cloud Computing help start-ups to bootstrap on a shoe-string budget. The power of the cloud computing pricing model comes from turning CAPEX expenditure into OPEX. With no upfront costs start-ups can spin up servers on a low budget and scale-up as they grow. Large companies on the other hand save from this on-demand usage basis model. For example let’s take a 100 compute node Big Data Cluster used for only 1 hour per day for Data Analytics, the machines would be turned on-demand for the computational needs thereby having a large cost savings as opposed to having dedicated hardware. Rackspace Hosting together with Manchester Business School conducted a study of 1300 organisations in the UK and US revealed that:- • 56 per cent have been able to increase profits through using cloud services • 49 per cent have been able to grow their business through use of the cloud • 60 per cent say that cloud computing has reduced the need for their IT team to maintain infrastructure, giving them more time to focus on strategy and innovation.

References 1. 88 per cent of cloud users point to cost savings according to Rackspace Survey https://blog.rackspace.com/newsarticles/88-per-cent-of-cloud-users-point-to-cost-savings-according-to-rackspace-survey/ 2. The Economic and Strategic Benefits of Cloud Computing https://www.computereconomics.com/custom.cfm?name=postPaymentGateway.cfm&id=1931

[leorajapakse]

Enterprise Architecture - Common Mistakes by Beginners

Acknowledgements

Thank you to everyone in the “TOGAF for Architecture” group who contributed to this article.

This article concentrates on common mistakes made by Enterprise Architecture beginners and advice on how to be a better EA, and have a better Enterprise Architecture practice. Lack of Understanding - Not developing an effective understanding of the key stakeholders and their motivations. - Not knowing that other disciplines have great material (i.e. Systems Engineering is very rigorous but not necessarily suited to enterprise architecture).  - Trying to do even IT-architecture (let alone a proper 'architecture of the enterprise') without a very solid understanding of 'the business of the business'. - Lacking the business skills. - Going by the book only, not taking some courses on EA and thus losing the opportunity to really understand the concept and getting feedback from experience EA practitioners. - Not understanding that Information Systems are the prime tools to manage and operate an organization. In other words information management is the focus of EA. - Inventory errors (confusion about components and classifications). - Within an EA team, the strengths and weaknesses of the staff have to blended and directed to leverage strength and mitigate weakness. if the team is inexperienced, then the problems are compounded. Lack of good judgement - Treating EA as a project (rather than as an ongoing capability). - Need to be more aligned to business needs at times rather than theoretically perfect approach to solve a problem. - Stakeholder errors (confusion about the 'real' stakeholder, too much/too little time with the right/wrong stakeholder). - Not having the insight to draw upon multiple methodologies in the tailoring phase to create useful artefacts. - "Deliver white éléphants". A recent EA projects in public sector with easily two thousands person-days investment and the solution is not used today. They made a damned good and costly picture of the organisation at that time, but it didn't evolved and it's now put on a shelf. Too much unusable documentation. It's a pity for ratepayers and don't give good press for EA projects.

 Failing to perform due diligence - Failing to realize that some EA work is already going on in the company but under a different name and as a consequence failing to interlock with them. Failing to Plan - Not developing a communication plan to address the concerns of those stakeholders. - Failing to understand how EA is IT centric! (it's the IT assets, which - due to complexity - require architectural attention in addition to regular planning work) and ending up making EA too general and diluted. - Adopting a specific EA framework without any tailoring and thereby preventing the EA practice from delivering 'fit-for-purpose' architecture. Instead, it would become a 'systematic' replica of documentation. - Selecting a methodology and applying it without tailoring it to the near term objectives. The type of project requires immediate business benefits quickly or the project will be unplugged rapidly. It’s a fact. Thinking big too fast enters in tedious project that never finish or brings any result. EA has to evolve as the benefits it brings. Reaching a first phase that returns benefits even to one only or few business units is preferable. - Defining a serial process, when an iterative process is needed. - Allowing the stakeholders to have fuzzy scope/purpose/objectives for the EA work. - Not realizing that the time frame between the as-is and to-be models can or should have interim steps with their own gap analysis. - Trying to do the whole architecture in one big multi-year hit (rather than as small-scale actions in context of a larger picture, and in conjunction with existing change-projects). Failure in prioritization When you take an organisation verticals (HR, Finance, IT, Marketing) trying to move through an entire vertical in the Business Architecture for example and then moving to Data Architecture in the BDAT stack. Problem is the effort involved in fully defining the enterprise at the business level is usually so great and so costly that stakeholders get scared and pull the plug before ever moving to the next level. Whereas if you take the vertical zig-zag you explore one line of business or business capability in all BDAT levels, then repeat for other lines of business, zig zagging up and down. Doing this allows you to bite off chunks of Business capability that can be tackled in a reasonable amount of time and cost and lead to tangible results quickly. This encourages the stakeholders with real results that they want to see more of.

Failing in Communication - Failing to set and communicate some guiding principles  - Lack of Information brevity. Creating too much information for a time-poor audience  - Use of architecture jargon, and a lack of use of familiar business terminology  - Failing to understand the significance of confidentiality. Many business plans are kept confidential and can't be reflected in widely distributed material (like EA artefacts) so appropriate management is needed. A symptom of this is the inexperienced analyst / consultant who doesn't grasp that the business manager they have engaged with may not be able to tell them the whole story due to confidentiality.  - Not tailoring the communication artefacts to the expected audience and the frequency of delivery - Selecting tools that are difficult to publish to corporate websites  - Not defining or publishing a taxonomy (how artifacts relate) or an ontology (artifact definitions and uses) 

Failing to Market - What is an Enterprise Architecture – Poor Education to Stakeholders about what value EA will provide in the long run. - It's hard to make things change and change can seriously hit the bottom line so resistance to change comes with the territory. You're now dealing with people that make decisions and that carries responsibilities. I try and respect that, and understand that the current state is often a consequence of a myriad of decisions taken in isolation many years ago that were probably right at the time. Warm people up to new ideas, talk over coffee, and don't avoid the difficult stakeholders. Be prepared to sell your ideas/findings in 30 seconds or 30 minutes - these CxO types are busy. I have no problem walking people out of the building for a couple of minutes of airtime! 

Failing to Execute - Concentrating on the as-is (the "we have to understand where we are before we can plan to move forward" fallacy)  - Failing to interlock properly with the business decision makers (often - paradoxically - by creating a "business architecture" which is how the EA team think the business ought to look but is actually detached from actual strategies and plans).  - Trying to do too much all at once. EA is something that evolves. A key to success is to identify the aspects of EA which are important to support planned / intended business change and focus on them.  - Not selecting, modifying and creating a set of reference architectures (part of tailoring)  - More Change or Transformation Centric: Advisory based on change rather than EA as a base recommending change (Considering Customers, Organization as a whole, Departmental level changes and last but not the least Decision at various levels aligned to Organizational Goals)  - Process rigidity (forcing the 'correct' sequence) and errors (can’t start, can’t finish, get stuck)

Failing to Mitigate Risk - Thinking risk is not concern of EA 

Failure in Governance - Failing to implement effective governance  - Reinventing the wheel when reference material (architectures) already exist - Trying to act as the 'architecture police'  - Trying to tell solution-architects how to do their job 

Failure in Information Management - Not creating reusable artefacts when documenting/defining the enterprise  - Lack of Repositories for EA Artifacts – Visualization of Process and Data Interactions and relation to organizational Goals at various levels can be accessed at certain repositories and further constant updates in Enterprise Architecture is not there in relation to recent goals and objectives

Quality Issues - Failing to customise (and be pragmatic about the use of) a methodology or framework  - Deliverable errors (artifacts at the wrong level of detail, wrong focus, poor execution) Lack of Emotional Intelligence and Perception - Inability to understand the "human psyche" -  Stubborn in their beliefs and fail to empathize with management and their superiors - I know it all philosophy, trying to show they are smarter, trying to show the light to everyone in the company from the CEO downwards - Visualization of a holistic view- EA gets focused to silos and sometimes considered as an increment to IT Architecture  - Ivory Tower Architect syndrome: EA advisory and consulting is not consistent and aligned to Goals and Objectives of an Enterprise  - Vanity/arrogance, thinking that knowing EA makes you better than the business/IT people that hired you, thus not really listening to them and doing your EA. - Non-architect mind-set. In other words, the architect should adopt the mind-set that enables him/her to capture the 'full picture' and 'long-term' approaches rather than 'on-spot' solutions.  - A belief that Enterprise Architects should be telling the business stakeholders how to enable their business capabilities. - Lack of soft skills of the practitioner

Some good advice from experienced EAs - Get good architectural principles to start off with. Standards, policies, patterns etc. can take a very long time to collect. You can start to govern with principles as an absolute minimum. Anything less is sage advice and wisdom. Collect the others as you go along, and get the right people to own them.  - Communicate with Purpose - Be a good "political" leader. Try not to be abrasive, keep egos out and work out good solution acceptable as a team; do not seek "self glory" - Follow the budget. EA is hard without a solid mandate and the best way to ensure that is to align arch governance with project governance - budget milestones are a great way of ensuring projects deliver.  - EA is about bringing business value through IT. If you endeavour on 1+-year efforts on defining the EA, you're on the wrong path. Do the 20% that gives you the 80% of the answers needed. - Keep it Simple - Stay evergreen - yourself and your EA. Constantly listen to the business, listen to the IT, and ensure that your EA is giving value also today. Govern hard when it makes sense, but listen to when the EA is out-dated, and modify it to still meet needs. - Adopt and Adapt  - Don't worry about failing, and fail fast when you do. Second time around you'll do a better job by learning from your mistakes. The hard fact is a lot of EA teams struggle the first time they try. Divide and conquer can work.. Keep your raw data, take lots of notes, and take time to understand why something isn't working.  - Use their language. By all means geek out when you're talking to your peers, but when you're talking to your stakeholders talk to them in their language. Words like "(business) service", "product" and the trinity of "Vision/Goals/Objectives" mean different things to different people. You have enough to sell them, don't waste it arguing what a Product is. - Love the Ops team. The teams running the systems day to day are usually the closest to them and can be a goldmine of information for your current state analysis. - Don't ignore the Project Management Office. They often have a great view of all the finance spend and you can often find the guerrilla IT lurking in far corners of some functions.  - Enable, don't disable projects. I've worked with EA's who love to say no. I've worked with better ones that suggest alternatives, or point out opportunities to help projects move forwards.  - Use a model and build a repository. Don't get hung up on which one - they all have their strengths and weaknesses . Unless you have a real need don't make it more complicated than it needs to be. It doesn't need to be fancy. Excel is better than nothing.  - Keep track of your architectural debt. Waivers are a fact of life in EA. If a programme isn't implementing your "target" architecture for good reasons (like time or money), try and at least steer it in a way that increments the capability in the right direction and can be easily replaced when the time comes.  - Enjoy the job. Not many people get to solve problems and attack hard challenges all day.

[leorajapakse]

TOGAF in a nutshell

TOGAF is an Enterprise Architecture Framework. It is focusedon Strategic Planning, Execution, Governance of an IT Vision and coming up with a roadmap for Technology Transformation.

Preliminary Phase

Select the Architecture Framework TOGAF TRM or III-RM this forms the basis of the foundation architecture. Next establish Architecture Principles. Business, Data, Application and Technology Principles should be established. The principles should be robust, consistent and stable. An example set of Data Principles: - i. Data is an Asset, Data is Shared, Data is Accessible.

Architecture Vision The Architecture Vision is the elevator pitch statement that could sell the proposed development to stakeholders.

Eg:- 1. Improve the Return on Capital Deployed, Increase Operating Efficiency, Improve the Quality of Service Level across the Technology Landscape. Business Architecture Describe the Baseline (As-Is) and Target (To-Be) Business Architecture. The Architecture could be tabulated as BPMN Models, UML Use Case diagrams. Information Systems Architecture Describe the Baseline (As-Is) and Target (To-Be) Application Architecture, and describe the Baseline (As-Is) and Target (To-Be) Data Architecture. These would consist diagrams for Application Eg:- Portfolio Catalog, Interface Catalog and for Data Eg:- Conceptual and Logical Diagrams, Data Migration Diagram, Data Life Cycle Diagram to name a few.

Technology Architecture

Describe the Baseline (As-Is) and Target (To-Be) Technology Architecture, Environment Location Diagram, Platform Decomposition Diagram are some of the Artefacts produced from this exercise.

Opportunities and Solutions Perform gap analysis and arrive at solutions. You may choose to build a new system, obtain a COTS product or Decommission and existing system. A new system would be represented by a Solution Buiding Block, while existing systems would be ABBs (Architecture Building Blocks). Migration Planning Plan the migration from the Baseline to Target Architecture (Business, Data, Application and Technology) and produce a detailed Implementation Plan and Migration Plan.

Implementation Governance The information for management of various development projects is brought together.

Architecture Change Management Continual monitoring of environment for changes. Ensure changes to the architecture are managed in a cohesive manner. New Requests for Architecture work are created as required. What is an Architecture Viewpoint ? TOGAF documentation for Data Architecture a Planner would require a Data Entity View, the Designer would need Logical Data View, Standards View, System Engineering View the Builder would need Physical Data View. TOGAF provides a set of Catalogs and Diagrams to address different viewpoints.

[leorajapakse]

Enterprise Architecture Tools

1. Sparx Enterprise Architect This is my favorite tool, it comes with a TOGAF Repository, UML, Archimate, BPMN, modelling can be done using Sparx EA. This is an enterprise grade tool. The top tool of choice for any EA practice.

2. Archi This tool is an open source Archimate modelling tool. It is a very user friendly tool, and a top quality open source tool. I would recommend Archi for anyone looking for a tool for Archimate drawings for free.

3. Bizagi Process Modeler Bizagi is an Open Source BPMN Modelling tool. While it may not be the most user friendly option, it does pack  the benefits of a free BPMN tool.

4. CA ERwin The Computer Associates ERwin tool is an ERD (Entity Relationship Diagram) tool, which is capable of delivering data models, for data architecture. This is an intuitive tool for anyone with knowledge on ERDs.

[leorajapakse]

Introduction to Enterprise Architecture

What is Enterprise Architecture ? Enterprise architecture (EA) is a discipline for proactively and holistically leading enterprise responses to disruptive forces by identifying and analyzing the execution of change toward desired business vision and outcomes. – Gartner

Enterprise architecture (EA) is “a well-defined practice for conducting enterprise analysis, design, planning, and implementation, using a holistic approach at all times, for the successful development and execution of strategy. – Wikipedia

Current Enterprise Architecture Frameworks TOGAF, Zachman, AGA, FEAF, DODAF.

TOGAF

The TOGAF framework has the Architecture Development Method an Iterative approach at its core. An architecture blueprint of the baseline and target architectures for Business, Data, Application, and Technology is created with the migration plan arriving at an Architecture Roadmap.

Zachman

The Zachman Framework has 6 perspectives (Planner, Owner, Designer, Builder, Subcontractor); the framework asks 6 basic questions (What, How, Where, Who, When, Why). This a simple but effective framework used by different industry sectors.

FEAF (Federal Enterprise Architecture Framework)

FEAF is based on 5 reference models Performance Reference Model (PRM), Business Reference Model (BRM), Service Component Reference Model (SRM), Data Reference Model (DRM), Technical Reference Model (TRM).

AGA (Australian Government Architecture)

AGA is based on FEAF (Federal Enterprise Architecture Framework), AGA consists of 5 inter-related reference models Performance, Business, Services, Data and Technology. AGA is mainly used in the Government Sector.

DODAF (Department of Defense Architecture Framework)

This Framework is specifically geared towards complex systems. DODAF includes a Design Process and Artefacts which cover viewpoints.

Which framework is best for Banking and Finance? 1. TOGAF 2. Zachman

TOGAF is heavily used in the Banking and Finance industry and is a good fit for the industry. The ADM iterative approach is appropriate for the complex operating environments of Banking and Finance.

The following is a matrix developed based on my research on the EA frameworks and industries that these are used in.