Google Cloud Next’24 Top 10 Highlights of the First Day

Google Cloud Next’24 Top 10 Highlights of the First Day


Authors: Codento Consulting Team


Google Cloud Momentum Continues

The Google Cloud Next event is taking place this week in Las Vegas showcases a strong momentum with AI and Google Cloud innovations with more than 30 000 participants.

Codento is actively participating to the event in Las Vegas with Ulf Sandlund and Markku Pulkkinen and remotely via the entire Codento team. Earlier on Tuesday Codento was awarded as the Google Cloud Service Partner of the Year in Finland.

As the battle is becoming more fierce among the hyperscalers we can fairly observe that Google Cloud has taken a great position going forward:

  • Rapid growth of Google Cloud with a $36 Billion run rate outpacing its hyperscaler peers on a percentage basis
  • Continuous deep investments in AI and Gen AI progress with over a million models trained 
  • 90% of unicorns use Google Cloud showcasing a strong position with startups
  • A lot of reference stories were shared. A broad range of various industries are now using Google Cloud and its AI stack
  • And strong ecosystem momentum globally in all geographies and locally


Top 10 Announcements for Google Cloud Customers

Codento consultants followed every second of the first day and picked our favorite top 10 announcements based on the value to Google Cloud customers:

1. Gemini 1.5 Pro available in public preview on Vertex AI. It can now process from 128,000 tokens up to 1 million tokens. Google truly emphasizes its multi-modal capabilities. The battle against other hyperscalers in AI is becoming more fierce.

2. Gemini is being embedded across a broad range of Google Cloud services addressing a variety of use cases and becoming a true differentiator, for example:

  • New BigQuery integrations with Gemini models in Vertex AI support multimodal analytics, vector embeddings, and fine-tuning of LLMs from within BigQuery, applied to your enterprise data.
  • Gemini in Looker enables business users to chat with their enterprise data and generate visualizations and reports

3. Gemini Code Assist is a direct competitor to GitHub’s Copilot Enterprise. Code Assist can also be fine-tuned based on a company’s internal code base which is essential to match Copilot.

4. Imagen 2. Google came out with the enhanced image-generating tool embedded in Vertex AI developer platform with more of a focus on enterprise. Imagen 2 is now generally available.

5. Vertex AI Agent Builder to help companies build AI agents. This makes it possible for customers to very easily and quickly build conversational agents and instruct and guide them the same way that you do humans. To improve the quality and correctness of answers from models,  a process called grounding is used based on Google Search.

6. Gemini in Databases is a collection of AI-powered, developer-focused tools to create, monitor and migrate app databases.

7. Generative AI-powered security: number of new products and features aimed at large companies. These include Threat Intelligence, Chronicle to assist with cybersecurity investigations) and  Security Command Center.

8. Hardware announcements: Nvidia’s next-generation Blackwell platform coming to Google Cloud in early 2025 and Google Cloud joins AWS and Azure in announcing its first custom-built Arm processor, dubbed Axion

9. Run AI anywhere, generative AI search packaged solution powered by Gemma designed to help customers easily retrieve and analyze data at the edge or on-premises with GDC, this solution will be available in preview in Q2 2024.

10. Data sovereignty. Google is renewing its focus on data sovereignty with emphasis on partnerships, less to building its own sovereign clouds.

There were also a lot of new announcements in the domains of employee productivity and Chrome, but we shall leave those areas for later discussion.


So far the list of announcements has been truly remarkable. As we anticipate the coming days of the Next event we are eager to get deeper into the details and understand what all this means in practice.

What is already known convinces us that Google Cloud and its AI approach continues to be completely enterprise-ready providing capabilities to support deployments from pilot to production. 

To make all this real capable partners, like Codento, are needed to assist the entire journey: AI and data strategy, prioritized use cases, building the data foundation, implementing AI projects with strong grounding and integration, consider security and governance, and eventually build MLOps practices to scale the adoption.

For us partners, much anticipated news came in the form of a new specialization: Generative AI specialization will be available in June 2024. Codento is ready for this challenge with the practice and experience already in place.

To follow the Google Cloud Next 2024 event and announcements the best place is Google Cloud blog.


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Harnessing AI Power: Building the Next Generation Foundation

Harnessing AI Power: Building the Next Generation Foundation


Author: Antti Pohjolainen, Codento

Artificial Intelligence (AI), that field which imbues machines with the power to ‘think’,  is no longer solely the domain of science fiction.  AI and its associated technologies are revolutionizing the way businesses operate, interact with customers, and ultimately shape the future. AI will have to sit at the core if organizations wish to be truly future-proof and embrace sustainable growth.

Yet, building the infrastructure to handle AI-driven projects can be a significant challenge for those organizations not born ‘digital natives’. Here we’ll outline some strategic pathways towards an integrated AI future that scales your business success.


Beyond Hype: Real-World Benefits of an AI Foundation

AI sceptics abound, perhaps wary of outlandish promises and Silicon Valley hyperbole. Let’s cut through the noise and look at some solid reasons to build a future upon a NextGen AI Foundation:

  • Efficiency reimagined: Automation remains a prime benefit of AI systems. Think about repetitive manual tasks – they can often be handled more quickly and accurately by intelligent algorithms. That frees up your precious human resources to focus on strategic initiatives and complex problem-solving that truly drive the business forward.
  • Data-driven decisions: We all have masses of data – often, organizations literally don’t know what to do with it all. AI is the key to transforming data into actionable insights. Make faster, better-informed choices from product development to resource allocation.
  • Predictive powers: Anticipate customer needs, optimize inventory, forecast sales trends – AI gives businesses a valuable window into the future and the chance to act with precision. It mitigates risks and maximizes opportunities.

Take our customers BHG as an example. They needed to implement a solid BI platform to service the whole company now and in the future. With the help of Codento’s data experts, BHG now has a highly automated, robust financial platform in production. Read more here. 


Constructing Your AI Foundation: Key Considerations

Ready to join the AI-empowered leagues? It’s critical to start with strong groundwork:

  • Cloud is King: Cloud-based platforms provide the flexibility, scalability, and computing power that ambitious AI projects demand. Look for platforms with specialized AI services to streamline development and reduce overhead.
  • Data is The Fuel: Your AI systems are only as good as the data they’re trained on. Make sure you have robust data collection, cleansing, and governance measures in place. Remember, high-quality data yields greater algorithmic accuracy.
  • The Human Touch: Don’t let AI fears take hold. This isn’t about replacing humans but supplementing them. Re-skill, re-align, and redeploy your teams to work with AI tools. AI’s success relies on collaboration, and ethical AI development should be your mantra.
  • Start Small, Aim Big: Begin with focused proof-of-concept projects to demonstrate value before expanding your AI commitment. A well-orchestrated, incremental approach can help manage complexity and gain acceptance throughout your organization.


The Road Ahead: AI’s Power to Transform

It’s undeniable that building a Next Generation Foundation with AI requires effort and careful planning. But, the potential for businesses of all sizes is breathtaking.  Imagine streamlined operations, enhanced customer experiences, and insights that lead to unprecedented successes.

AI isn’t just the future – it’s the foundation for the businesses that will be thriving in the future. The time to join the AI revolution is now. The rewards are simply too great to be left on the table.


About the author: Antti  “Apo” Pohjolainen, Vice President, Sales, joined Codento in 2020. Antti has led Innofactor’s (Nordic Microsoft IT provider) sales organization in Finland and, prior to that, worked in leadership roles in Microsoft for the Public sector in Finland and Central & Eastern Europe. Apo has been working in different sales roles longer than he can remember. He gets a “seller’s high” when meeting with customers and finding solutions that provide value for all parties involved. Apo received his MBA from the University of Northampton. His final business research study dealt with Multi-Cloud. Apo has frequently lectured about AI in Business at the Haaga-Helia University of Applied Sciences.  


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Google Cloud Nordic Summit 2023: Three Essential Technical Takeaways

Google Cloud Nordic Summit 2023: Three Essential Technical Takeaways

Authors, Jari Timonen, Janne Flinck, Google Bard

Codento  participated with a team of six members in the Google Cloud Nordic Summit on 19-20 September 2023, where we had the opportunity to learn about the latest trends and developments in cloud computing.

In this blog post, we will share some of the key technical takeaways from the conference, from a developer’s perspective.


Enterprise-class Generative AI for Large Scale Implementtation

One of the most exciting topics at the conference was Generative AI (GenAI). GenAI is a type of artificial intelligence that can create new content, such as text, code, images, and music. GenAI is still in its early stages of development, but it has the potential to revolutionize many industries.

At the conference, Google Cloud announced that its GenAI toolset is ready for larger scale implementations. This is a significant milestone, as it means that GenAI is no longer just a research project, but a technology that 

can be used to solve real-world problems.

One of the key differentiators of Google Cloud’s GenAI technologies is their focus on scalability and reliability. Google Cloud has a long track record of running large-scale AI workloads, and it is bringing this expertise to the GenAI space. This makes Google Cloud a good choice for companies that are looking to implement GenAI at scale.


Cloud Run Helps Developers to Focus on Writing Code

Another topic that was covered extensively at the conference was Cloud Run. Cloud Run is a serverless computing platform that allows developers to run their code without having to manage servers or infrastructure. Cloud Run is a simple and cost-effective way to deploy and manage web applications, microservices, and event-driven workloads.

One of the key benefits of Cloud Run is that it is easy to use. Developers can deploy their code to Cloud Run with a single command, and Google Cloud will manage the rest. This frees up developers to focus on writing code, rather than managing infrastructure.

Google just released Direct VPC egress functionality to Cloud Run. It lowers the latency and increases throughput  for connections to your VPC network. It is more cost effective than serverless VPC connectors which used to be the only way to connect your VPC to Cloud Run.

Another benefit of Cloud Run is that it is cost-effective. Developers only pay for the resources that their code consumes, and there are no upfront costs or long-term commitments. This makes Cloud Run a good choice for all companies.


Site Reliability Engineering (SRE) Increases Customer Satisfaction

Site Reliability Engineering (SRE) is a discipline that combines software engineering and systems engineering to ensure the reliability and performance of software systems. SRE is becoming increasingly important as companies rely more and more on cloud-based applications.

At the conference, Google Cloud emphasized the importance of SRE for current and future software teams and companies. 

One of the key benefits of SRE is that it can help companies improve the reliability and performance of their software systems. This can lead to reduced downtime, improved customer satisfaction, and increased revenue.

Another benefit of SRE is that it can help companies reduce the cost of operating their software systems. SRE teams can help companies identify and eliminate waste, and they can also help companies optimize their infrastructure.



The Google Cloud Nordic Summit was a great opportunity to learn about the latest trends and developments in cloud computing. We were particularly impressed with Google Cloud’s GenAI toolset and Cloud

 Run platform. We believe that these technologies have the potential to revolutionize the way that software is developed and deployed.

We were also super happy

that Codento was awarded with the Partner Impact 2023 Recognition in Finland by Google Cloud Nordic team. Codento received praise for deep expertise in Google Cloud services and market impact, impressive NPS score, and  achievement of the second Google Cloud specialization.






About the Authors

Jari Timonen, is an experienced software professional with more than 20 years of experience in the IT field. Jari’s passion is to build bridges between the business and the technical teams, where he has worked in his previous position at Cargotec, for example. At Codento, he is at his element in piloting customers towards future-compatible cloud and hybrid cloud environments.

Janne Flinck is an AI & Data Lead at Codento. Janne joined Codento from Accenture 2022 with extensive experience in Google Cloud Platform, Data Science, and Data Engineering. His interests are in creating and architecting data-intensive applications and tooling. Janne has three professional certifications and one associate certification in Google Cloud and a Master’s Degree in Economics.

Bard is a conversational generative artificial intelligence chatbot developed by Google, based initially on the LaMDA family of large language models (LLMs) and later the PaLM LLM. It was developed as a direct response to the rise of OpenAI’s ChatGPT, and was released in a limited capacity in March 2023 to lukewarm responses, before expanding to other countries in May.


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100 Customer Conversations Shaped Our New AI and Apps Service Offering 

100 Customer Conversations Shaped Our New AI and Apps Service Offering 


Author: Anthony Gyursanszky, CEO, Codento



A few months back, in a manufacturing industry event: Codento  just finished our keynote together with Google and our people started mingling among the audience. Our target was to agree on a follow-up discussions about how to utilize Artificial Intelligence (AI) and modern applications for their business.

The outcome of that mingling session was staggering. 50% of the people we talked with wanted to continue the dialogue with us after the event. The hit rate was not 10%, not 15%, but 50%. 

We knew before already that AI will change everything, but with this, our  confidence climbed to another level . Not because we believed in this, but because we realized that so many others did, too.

AI will change the way we serve customers and manufacture things, the way we diagnose and treat illnesses, the way we travel and commute, and the way we learn. AI is everywhere, and not surprisingly, it is also the most common topic that gets executives excited and interested in talking. 

AI does not solve the use cases without application innovations. Applications  integrate the algorithms to an existing operating environment, they provide required user interfaces, and  they handle the orchestration in a more complex setup.


We address your industry- and role-specific needs with AI and application innovations 

We at Codento have been working with AI and Apps for several years now. Some years back, we also sharpened our strategy to be the partner of choice in Finland for Google Cloud Platform-based solutions in the AI and applications innovation space. 

During the past six months, we have been on a mission to workshop with as many organizations as possible about their needs and aspirations for AI and Apps. This mission has led us to more than a hundred discussions with dozens and dozens of people from the manufacturing industry to retail and healthcare to public services.

Based on these dialogues, we concluded that it is time for Codento to move from generic technology talks to more specific messages that speak the language of our customers. 

Thus, we are thrilled to introduce our new service portfolio, shaped by those extensive conversations with various organizations’ business, operations, development, and technology experts.

Tailored precisely to address your industry and role-specific requirements, we now promise you more transparent customer foresight, smarter operations, and increased software intelligence – all built on a future-proof, next-generation foundation on Google Cloud. 

These four solution areas will form the pillars of Codento’s future business. Here we go.


AI and Apps for Customer Foresight

As we engaged with sales, marketing and customer services officers we learned that the majority is stuck with limited visibility of customer understanding and of the impact their decisions and actions have on their bottom line. AI and Apps can change all this.

For example, with almost three out of four online shoppers expecting brands to understand their unique needs, the time of flying blind on marketing, sales, and customer service is over.

Codento’s Customer Foresight offering is your key to thriving in tomorrow’s markets.  

  • Use data and Google’s innovative tech, trained on the world’s most enormous public datasets, to find the right opportunities, spot customers’ needs, discover new markets, and boost sales with more intelligent marketing. 
  • Exceed your customers’ expectations by elevating your retention game with great experiences based on new technology. Keep customers returning by foreseeing their desires and giving them what they want when and how they want it – even before they realize their needs themselves. 
  • Optimize Your Profits with precise data-driven decisions based on discovering your customers’ value with Google’s ready templates for calculating Customer Lifetime Value. With that, you can focus on the best customers, make products that sell, and set prices that work. 


AI and Apps for Smart Operations 

BCG has stated that 89% of industrial companies plan to implement AI in their production networks. As we have been discussing with the operations, logistics and supply chain directors, we have seen this to be true – the appetite is there.

Our renewed Smart Operations offering is your path to operational excellence and increased resilience. You should not leave this potential untapped in your organization. 

  • By smart scheduling your operations, we will help streamline your factory, logistics, projects, and supply chain operations. With the help of Google’s extensive AI tools for manufacturing and logistics operations, you can deliver on time, within budget, and with superior efficiency. 
  • Minimize risks related to disruptions, protect your reputation, and save resources, thereby boosting employee and customer satisfaction while cutting costs.  
  • Stay one step ahead with the power of AI, transparent data, and analytics. Smart Operations keeps you in the know, enabling you to foresee and tackle disruptions before they even happen. 


AI and Apps for Software Intelligence 

For the product development executives of software companies, Codento offers tools and resources for unleashing innovation. The time to start benefiting from AI in software development is now. 

Gartner predicts that 15% of new applications will be automatically generated by AI in the year 2027 – that is, without any interaction with a human. As a whopping 70% of the world’s generative AI startups already rely on Google Cloud’s AI capabilities, we want to help your development organization do the same. 

  • Codento’s support for building an AI-driven software strategy will help you confidently chart your journey. You can rely on Google’s strong product vision and our expertise in harnessing the platform’s AI potential. 
  • Supercharge your software development and accelerate your market entry with cutting-edge AI-powered development tools. With Codento’s experts, your teams can embrace state-of-the-art DevOps capabilities and Google’s cloud-native application architecture. 
  • When your resources fall short, you can scale efficiently by complementing your development capacity with our AI and app experts. Whether it’s Minimum Viable Products, rapid scaling, or continuous operations, we’ve got your back. 


Nextgen Foundation to enable AI and Apps

While the business teams are moving ahead with AI and App  initiatives related to Customer Foresight, Smart Operations, and Software Intelligence   IT functions are often bound to legacy IT and data  architectures and application portfolios. This creates pressure for the IT departments to keep up with the pace.

All the above-mentioned comes down to having the proper foundation to build on, i.e., preparing your business for the innovations that AI and application technologies can bring. Moving to a modern cloud platform will allow you to harness the potential of AI and modern applications, but it is also a cost-cutting endeavor.BCG has studied companies that are forerunners in digital and concluded that they can save up to 30% on their IT costs when moving applications and infrastructure to the cloud. 

  • Future-proof your architecture and operations with Google’s secure, compliant, and cost-efficient cloud platform that will scale to whatever comes next. Whether you choose a single cloud strategy or embrace multi-cloud environments, Codento has got you covered. 
  • You can unleash the power and amplify the value of your data through real-time availability, sustainable management, and AI readiness. With Machine Learning Ops (MLOps), we streamline your organization’s scaling of AI usage. 
  • We can also help modernize your dated application portfolio with cloud-native applications designed for scale, elasticity, resiliency, and flexibility. 


Sharpened messages wing Codento’s entry to the Nordic market 

With these four solution areas, we aim to discover the solutions to your business challenges quickly and efficiently. We break the barriers between business and technology with our offerings that speak the language of the target person. We are dedicated to consistently delivering solutions that meet your needs and learn and become even more efficient over time.  

Simultaneously, we eagerly plan to launch Codento’s services and solutions to the Nordic market. Our goal is to guarantee that our customers across the Nordics can seize the endless benefits of Google’s cutting-edge AI and application technologies without missing a beat.

About the author:

Anthony Gyursanszky, CEO, joined Codento in late 2019 with more than 30 years of experience in the IT and software industry. Anthony has previously held management positions at F-Secure, SSH, Knowit / Endero, Microsoft Finland, Tellabs, Innofactor and Elisa. Hehas also served on the boards of software companies, including Arc Technology and Creanord. Anthony also works as a senior consultant for Value Mapping Services. His experience covers business management, product management, product development, software business, SaaS business, process management, and software development outsourcing. Anthony is also a certified Cloud Digital Leader.


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My Journey to the World of Multi-cloud: Conclusions and Recommendations, Part 4 of 4

#NEXTGENCLOUD: My Journey to the World of Multi-cloud: Conclusions and Recommendations, Part 4 of 4


Author: Antti Pohjolainen, Codento


This is the last part of my four blog post series covering my journey to the world of multi-cloud. The previous postings are Part 1, Part 2, and Part 3.



The leading research topic that my study attempts to address is what are the business benefits of using multi-cloud architecture? According to the literature analysis, the most significant advantages include cost savings, avoiding vendor lock-in, and enhancing IT capabilities by utilizing the finest features offered by several public clouds. 

According to the information acquired from the interviews, vendor lock-in is not that much of a problem. The best features of various public clouds should be utilized, according to some respondents. Implementing a multi-cloud may result in cost savings. Still, it appears that the threat of doing so is being used as a bargaining chip during contract renewal talks to pressure the current public cloud vendor for more affordable costs.

The literature review and the interviews revealed that the most pertinent issues with multi-cloud architecture were its increased complexity, security, and skill requirements. Given that the majority of the businesses interviewed lacked stated selection criteria, the research’s findings regarding hyperscaler selection criteria may have been the most unexpected. Finally, there is a market opportunity for both Google Cloud and multi-cloud.

According to academic research and information gleaned from the interviews, most customers will choose multi-cloud architecture within the purview of this study. The benefits of employing cloud technologies should outweigh the additional labor required to build a multi-cloud architecture properly, although there are a number of dangers involved. 

According to the decision-makers who were interviewed, their current belief is that a primary cloud will exist, which will be supplemented by services from one or more other clouds. The majority of workloads, though, are anticipated to stay in their current primary cloud.



It is advised that businesses evaluate and update their cloud strategy regularly. Instead of allowing the architecture to develop arbitrarily based exclusively on the needs of suppliers or outsourced partners, the business should take complete control of the strategy.

The use of proprietary interfaces and technologies from cloud providers should be kept to a minimum by businesses unless there is 1) a demonstrable economic benefit, 2) no other technical alternatives, such as other providers not offering that capability, and 3) other technical issues, such as significant performance gains. Businesses can reduce the likelihood of a vendor lock-in situation by heeding this advice.

If a business currently only uses cloud services from one hyperscaler, proofs-of-concept with additional cloud providers should be started as soon as a business requirement arises. If at all possible, vendor-specific technologies, APIs, or services should be avoided in the proof-of-concept implementations.

Setting up policies for cloud vendor management that cover everything from purchase to operational governance is advised for businesses. Compared to dealing with a single hyperscaler, managing vendors in a multi-cloud environment needs more planning and skill. 

Additionally, organizations are recommended to have policies and practices in place to track costs because the use of cloud processing is expected to grow in the upcoming years.


Final words

This blog posting concludes the My Journey To The World Of Multi-cloud series. We here at Codento would be thrilled to help you in your journey to the world of multi-cloud. Please feel free to contact me to get the conversation started. You will reach my colleagues or me here.



About the author: Antti  “Apo” Pohjolainen, Vice President, Sales, joined Codento in 2019. Antti has led Innofactor’s (Nordic Microsoft IT provider) sales organization in Finland and, prior to that, worked in leadership roles in Microsoft for the Public sector in Finland and Eastern Europe. Apo has been working in different sales roles for longer than he can remember. He gets a “seller’s high” when meeting with customers and finding solutions that provide value for all parties involved. 


Please check our online event recordings to learn more:

My Journey to the World of Multi-cloud: Insights Derived from the Interviews, Part 3 of 4

My Journey to the World of Multi-cloud: Insights Derived from the Interviews, Part 3 of 4


Author: Antti Pohjolainen, Codento



This is the third part of my four blog post series covering my journey to the world of multi-cloud. The previous postings are here: Part 1 and Part 2.

This post describes some of the insights I gained from the actual interviews. As explained in Part 1, I had the opportunity to interview 11 business leaders and subject-matter experts.  


Benefits of using a multi-cloud infrastructure

Based on the information gathered from the interviews, clients in Finland mostly use one public cloud to handle most of their business workloads. According to current thinking, if the existing cloud provider does not offer a particular service, unique point solutions from other clouds could be added to support the cloud. Thus the complementing technological capabilities from other  cloud providers are the primary justification for creating a multi-cloud architecture.

Contrary to academic literature (for more information, please see Part 2), which frequently lists economics as one of the main multi-cloud selection criteria, the overwhelming majority of interviewees did not regard multi-cloud as a significant means to drive  cost-savings

Cost savings are difficult to estimate, and based on the interviews, most of the companies are currently not experts in tracking costs associated with cloud processing. Pricing plans vary between the hyperscalers, and the plans are deemed to change often.

Additionally, the interviewees expressed no concern regarding a potential vendor lock-in scenario. That conclusion is important since vendor lock-in is regarded in academic literature as an important, perhaps the most critical, issue for businesses.


Challenges and risks identified in multi-cloud environments

The most significant barrier to multi-cloud adaption, according to a number of interviewers who represented all groups studied, is a lack of skills and capabilities. This results from two underlying factors:

  1. Customers often engage in learning about a single cloud or, at best, a hybrid cloud architecture, and
  2. The current partner network appears to focus mostly on one type of cloud architecture rather than multi-cloud capabilities.

Finland has an exceptionally high level of outsourcing IT services. The interviews provided evidence that Finland’s high outsourcing rate has a substantial negative impact on cloud services.

The hosting of customers’ IT infrastructure in data centers and on servers owned by the hosting provider generates a sizeable portion of business for IT operations outsourcing partners. They have made investments in buildings and IT equipment, so they stand to lose money if clients use cloud computing widely. 

The replies gathered were divided between security and privacy issues. Some interviewees ranked cloud security as the top deterrent to using cloud computing for mission-critical applications. None of the IT service providers contacted, though, thought this was a valid worry. 

The public sector – the central government in particular – has been dragging its feet with the cloud adaptation. There are unclear government-wide policies on how to deploy cloud processing, according to some people interviewed, who thought that government organizations were delaying their choice to adapt to the cloud.

Many of those surveyed believed that because there are no established, clear government-wide regulations on how to deploy cloud processing, government organizations were delaying their choice to adapt to the cloud.

Some interviewed people expressed concern that their company or customer lacked a clear cloud strategy, cloud service selection standards, or cloud service implementation strategy. This worry was raised by interviewers from all three groups.

Companies would benefit from having a clearly articulated plan and a list of selection criteria when considering adding new capabilities to their existing cloud architecture because more people are becoming involved in choosing cloud services 


What’s next in the blog series?

The final blog post of the series will be titled “Conclusion and recommendations”. Stay tuned!

About the author: Antti  “Apo” Pohjolainen, Vice President, Sales, joined Codento in 2019. Antti has led Innofactor’s (Nordic Microsoft IT provider) sales organization in Finland and, prior to that, worked in leadership roles in Microsoft for the Public sector in Finland and Eastern Europe. Apo has been working in different sales roles for longer than he can remember. He gets a “seller’s high” when meeting with customers and finding solutions that provide value for all parties involved. 


Please check our online event recordings to learn more:

My Journey to the World of Multi-cloud: Benefits and Considerations, Part 2 of 4

My Journey to the World of Multi-cloud: Benefits and Considerations, Part 2 of 4


Author: Antti Pohjolainen, Codento



This is the second part of my four blog post series covering my journey to the world of multi-cloud. The previous post explained the background of this series.

This post briefly presents what academic literature commonly lists as the benefits and challenges of multi-cloud architecture.


Benefits of using a multi-cloud infrastructure

Academic literature commonly names the following benefits derived from multi-cloud architecture:

  • Cost savings
  • Better IT capabilities
  • Avoidance of vendor lock-in

Cost savings is explained by the fact that hyperscalers have fierce market share competition, which has resulted in decreasing computing and storage costs. 

Increased availability and redundancy, disaster recovery, and geo-presence are often listed as examples of better IT capabilities that can be gained by using cloud services provided by more than one hyperscaler. 

Perhaps the most important reason, at least from an academic literature point of view, to implement a multi-cloud architecture is the avoidance of vendor lock-in. Having services only from one hyperscaler creates a greater dependency on a vendor compared to a situation where there is more than one cloud service provider.

Thus, the term “vendor lock-in”. Typically, switching from one cloud service provider to another means considerable expenses, as switching providers often necessitates system redesign, re-deployment, and data movement. 

To summarize, by choosing the best from a wide range of cloud services, multi-cloud infrastructure promises to solve the issue of vendor lock-in and lead to the optimization of user requirements.


Challenges with multi-cloud infrastructure

Implementing a multi-cloud infrastructure comes with a number of challenges that should be addressed in order to reap full benefits. The following paragraphs deal with the most commonly referenced challenges found in the academic literature.

When data, platforms, and applications are dispersed over numerous places, such as different clouds and enterprise data centers, new challenges emerge. Managing different vendors to ensure visibility across all applications, safeguarding various systems and databases, and managing spending add to the complexity of a multi-cloud strategy. 

Complexity increases as the needs and requirements of each vendor are typically different, and they need to be addressed separately. As an example, hyperscalers frequently require proprietary interfaces to access resources and services. 

Security is generally speaking more complex to be implemented in a multi-cloud environment than in one cloud provider architecture. 

Multi-cloud requires specific expertise, at least from technical and business-oriented personnel as well as from the vendor management teams. Budgets for hiring, training and multi-cloud strategy investments are increasing, forcing businesses to develop new knowledge and abilities in areas like maintenance, implementation, and cost optimization. 

Furthermore, it is said that using cloud computing can promote innovations, change the role of the IT department from routine maintenance to business support, and boost internal and external company collaborations. Thus, the role of IT may need to be adjusted when implementing a multi-cloud architecture.

The vendor management or procurement teams may need to learn new skills and methods to be able to select the suitable hyperscaler for different needs. Each hyperscaler has different services and pricing plans, and understanding those require expertise that might not be needed when working with only one hyperscaler.


What’s next in the blog series?

In the next post, I will discuss what I learned from the interviews I conducted for this research project.  Stay tuned!

About the author: Antti  “Apo” Pohjolainen, Vice President, Sales, joined Codento in 2019. Antti has led Innofactor’s (Nordic Microsoft IT provider) sales organization in Finland and, prior to that, worked in leadership roles in Microsoft for the Public sector in Finland and Eastern Europe. Apo has been working in different sales roles for longer than he can remember. He gets a “seller’s high” when meeting with customers and finding solutions that provide value for all parties involved. 


Please check our online event recordings to learn more:

My Journey to the World of Multi-cloud: Benefits and Considerations, Part 1 of 4

My Journey to the World of Multi-cloud: Benefits and Considerations, Part 1 of 4


Author: Antti Pohjolainen, Codento




This is the first of my four blog posts covering my journey to the world of multi-cloud.

While working as the Vice President for Sales at Codento, I have always been passionate about developing my understanding of why customers choose specific business or technological directions. 

This was one of the drivers why I started my part-time MBA (Master of Business Administration) studies in the fall of 2020, together with 20 other part-time students.  The MBA program was offered by The University of Northampton, which is available from the Helsinki School of Business (Helbus).

The final business research project was the program’s culmination, and the paper was accepted in October 2022. The title of my research project was “Multi-cloud – business benefits, challenges, and market potential”.

This series of blog postings highlight some of the findings from that research paper. 

Definition of multi-cloud architecture 

Multi-cloud is an architecture where cloud services are accessed across many cloud providers (Mezni and Sellami, 2017). Furthermore, the term refers to an architecture where several cloud computing and storage services are used in a single heterogeneous architecture (Georgios et al., 2021).

Trying to have a tight focus on my research, I limited the research to scenarios where only public cloud services based on Infrastructure as a Service (IaaS) and Platform as a Service (PaaS) were included. Thus, Software as a Service – for example, email such as – would not be included in the research. The following figure illustrates SaaS, Paas, and IaaS components:

Figure 1. SaaS, PaaS, IaaS Components. Source: Nasdaq (2017).


Research rationale, research questions, and research methodology 

I wanted to understand better the business benefits available from multi-cloud architecture. 

My employer – Codento Oy – is the vanguard of the Finnish companies providing services based on Google Cloud, and in most cases, Google Cloud would be a second or third cloud provider for our customers. Thus, multi-cloud expertise is vital to our customer discussions and implementation projects. 

To further narrow the scope of the research project, the focus of the paper was set to small to mid-size Finnish companies and public sector organizations. 

The main research question the project wanted to find an answer to was “What are the business benefits of using multi-cloud architecture?”

The secondary questions were 

  • What are the most relevant challenges of using multi-cloud architecture?
  • What factors influence the selection of public cloud providers (also known as hyperscaler)? and finally,
  • What is the market potential for multi-cloud solutions where Google Cloud is one component in the next three years?

A qualitative approach methodology was selected to have deep conversations with several IT and business leaders from different organizations. 

Three different groups of persons were interviewed:

  • Customers
  • IT service companies
  • Hyperscalers

Altogether, 11 interviews took place in July and August 2022:

  • IT service providers: CEO, CTOs
  • Hyperscalers: Cloud team lead, account manager
  • Customers:  CEO, CIO, CTOs

The findings of the study will be opened in subsequent blog posts 2-4. Stay tuned!


About the author: Antti  “Apo” Pohjolainen, Vice President, Sales, joined Codento in 2019. Antti has led Innofactor’s (Nordic Microsoft IT provider) sales organization in Finland and, prior to that, worked in leadership roles in Microsoft for the Public sector in Finland and Eastern Europe. Apo has been working in different sales roles for longer than he can remember. He gets a “seller’s high” when meeting with customers and finding solutions that provide value for all parties involved. 


Please check our online event recordings to learn more:

Leading through Digital Turmoil

Leading through Digital Turmoil

Author: Anthony Gyursanszky, CEO, Codento



Few decades back during my early university years I bacame familiar with Pascal coding and Michael Porter’s competitive strategy. “Select telecommunication courses next – it is the future”,  I was told. So I did, and the telecommunications disruption indeed accelerated my first career years.

The telecom disruption layed up the foundation for an even greater change we are now facing enabled by cloud capabilities, data technologoes, artificial intelligence and modern software. We see companies not only selecting between Porter’s lowest cost, differentation, or focus strategies, but with the help of digital disruption, the leaders utilize them all simultaneously.

Here at Codento we are in a mission to help various organization to succeed through digital turmoil, understand their current capabilities, envision their future business and technical environment, craft the most rational steps of transformation towards digital leadership, and support them throughout this process with advise and capability acceleration. In this process, we work closely with leading cloud technology enablers, like Google Cloud.

In this article, I will open up the journey towards digital leadership based on our experiences and available global studies.


What we mean by digital transformation now?

Blair Franklin, Contributing Writer, Google Cloud recently published a blogpost

Why the meaning of “digital transformation” is evolving. Google interviewed more than 2,100 global tech and business leaders around the question: “What does digital transformation mean to you?”

Five years ago the dominant view was “lift-and-shift” your IT infrastructure to the public cloud. Most organizations have now proceedded with this, mostly to seek for cost saving, but very little transformative business value has been visible to their own customers.

Today, the meaning of “digital transformation “has expanded according to Google Cloud survey. 72% consider it as much more than “lift-and-shift”. The survey claims that there are now two new attributes:

  1. Optimizing processes and becoming more operationally agile (47%). This in my opinion,  provides a foundation for both cost and differentiation strategy.
  2. Improving customer experience through technology (40%). This, in my opinion, boosts both focus and differentiation strategy.

In conclusion, we have now moved from “lift-and-shift” era to a “digital leader” era.


Why would one consider becoming a digital leader?

Boston Consulting Group and Google Cloud explored the benefits of putting effort on becoming “a digital leader” in Keys of Scaling Digital Value 2022 study. According to the study, about 30% of organizations were categorized as digital leaders. 

And what is truly interesting, digital leaders tend to outperform their peers: They bring 2x more solutions to scale and with scaling they deliver significantly better financial results (3x higher returns on investments, 15-20% faster revenue growth and simlar size of cost savings)

The study points out several characteristics of a digital leader, but one with the highest correlation is related how they utilize software in the cloud:  digital leaders deploy cloud-native solutions (64% vs. 3% of laggers) with modern modular architecture (94% vs. 21% laggers).

Cloud native means a concept of building and running applications to take advantage of the distributed computing offered by the cloud. Cloud native applications, on the other hand, are designed to utilize the scale, elasticity, resiliency, and flexibility of the cloud.

The opposite to this are legacy applications which have been designed to on-premises environments, bound to certain technologies, integrations, and even specific operating system and database versions.


How to to become a digital leader?

First, It is obvious that the journey towards digital leadership requires strong vision, determination, and investments as there are two essential reasons why the progress might be stalled:

  • According to a Mckinsey survey a lack of strategic clarity cause transformations to lose momentum or stall at the pilot stage.
  • Boston Consulting Group research found that only 40% of all companies manage to create an integrated transformation strategy. 

Second, Boston Consulting Group and Google Cloud “Keys of Scaling Digital Value 2022” study further pinpoints a more novel approach for digital leadership as a prerequisite for success. The study shows that the digital leaders:

  • Are organized around product-led platform teams (83% leaders vs. 25% laggers)
  • Staff cross-functional lighthouse teams (88% leaders vs. 23% laggers)
  • Establish a digital “control tower” (59% leaders vs. 4% laggers)

Third, as observed by us also here at Codento, most companies have structured their organizations and defined roles and process during the initial IT era into silos as they initially started to automate their manual processes with IT technologies  and applications. They added IT organizations next to their existing functions while keeping business and R&D functions separate.

All these three key functions have had their own mostly independent views of data, applications and cloud adoption, but while cloud enables and also requires seemless utilization of these capabilities ”as one”, companies need to rethink the way they organize themselves in a cloud-native way.

Without legacy investments this would obviously be a much easier process as “digital native” organizations, like Spotify, have showcased. Digital natives tend to design their operations ”free of silos” around cloud native application development and utilizing advanced cloud capabilities like unified data storage, processing and artificial intelligence.

Digital native organizations are flatter, nimbler, and roles are more flexible with broader accountability ss suggested by DevOps and Site Reliability Engineering models. Quite remarkable results follow successful adoption. DORA’s, 2021 Accelerate: State of DevOps Report reveals that peak performers in this area are 1.8 times more likely to report better business outcomes.


Yes, I want to jump to a digital leadr train. How to get started?

In summary, digital leaders are more successful than their peers and it is difficult to argument not to join that movement.

Digital leaders do not only consider digital transformation as an infrastructure cloudification initiative, but seek competitive egde by optimizing processes and improving customer experience. To become a digital leader requires a clear vision, support by top management and new structures enabled by cloud native applications accelerated by integrated data and artificial intelligence. 

We here at Codento are specialized in enabling our customers to become digital leaders with a three-phase-value discovery approach to crystallize your:

  1. Why? Assess where you are ar the moment and what is needed to flourish in the future business environment.
  2. What? Choose your strategic elements and target capabilities in order to succeed.
  3. How? Build and implement your transformation and execution journeys based on previous phases.

We help our clients not only throughout the entire thinking and implementation process, but also with specific improvement initiatives as needed.

To get more practical perspective on this you may want to visit our live digital leader showcase library:

You can also subscribe to our newsletters, join upcoming online-events and watch our event recordings


About the author: Anthony Gyursanszky, CEO, joined Codento in late 2019 with more than 30 years of experience in the IT and software industry. Anthony has previously held management positions at F-Secure, SSH, Knowit / Endero, Microsoft Finland, Tellabs, Innofactor and Elisa. Gyursanszky has also served on the boards of software companies, including Arc Technology and Creanord. Anthony also works as a senior consultant for Value Mapping Services. Anthony’s experience covers business management, product management, product development, software business, SaaS business, process management and software development outsourcing. Anthony is also a certified Cloud Digital Leader.


Contact us for more information on our  Value Discovery services.

Codento Community Blog: Six Pitfalls of Digitalization – and How to Avoid Them

Codento Community Blog: Six Pitfalls of Digitalization – and How to Avoid Them

By Codento consultants



We at Codento have been working hard over the last few months on various digitization projects as consultants and have faced dozens of different customer situations. At the same time, we have stopped to see how much of the same pitfalls are encountered at these sites that could have been avoided in advance.

The life mission of a consulting firm like Codento is likely to provide a two-pronged vision for our clients: to replicate the successes generally observed and, on the other hand, to avoid pitfalls.

Drifting into avoidable repetitive pitfalls always causes a lot of disappointment and frustration, so we stopped against the entire Codento team of consultants to reflect and put together our own ideas, especially to avoid these pitfalls.

A lively and multifaceted communal exchange of ideas was born, which, based on our own experience and vision, was condensed into six root causes and wholes:

  1. Let’s start by solving the wrong problem
  2. Remaining bound to existing applications and infrastructure
  3. Being stuck with the current operating models and processes
  4. The potential of new cloud technologies is not being optimally exploited
  5. Data is not sufficiently utilized in business
  6. The utilization of machine learning and artificial intelligence does not lead to a competitive advantage

Next, we will go through this interesting dialogue with Codento consultants.


Pitfall 1: Let’s start by solving the originally wrong problem

How many Design Sprints and MVPs in the world have been implemented to create new solutions in such a way that the original problem setting and customer needs were based on false assumptions or otherwise incomplete?

Or that many problems more valuable to the business have remained unresolved when they are left in the backlog? Choosing a technology between a manufactured product or custom software, for example, is often the easiest step.

There is nothing wrong with the Design Sprint or Minimum Viable Product methodology per se: they are very well suited to uncertainty and an experimental approach and to avoid unnecessary productive work, but there is certainly room for improvement in what problems they apply to.

Veera also recalls one situation: “Let’s start solving the problem in an MVP-minded way without thinking very far about how the app should work in different use cases. The application can become a collection of different special cases and the connecting factor between them is missing. Later, major renovations may be required when the original architecture or data model does not go far enough. ”

Markku smoothly lists the typical problems associated with the conceptualization and MVP phase: “A certain rigidity in rapid and continuous experimentation, a tendency to perfection, a misunderstanding of the end customer, the wrong technology or operating model.”

“My own solution is always to reduce the definition of a problem to such a small sub-problem that it is faster to solve and more effective to learn. At the same time, the positive mood grows when something visible is always achieved, ”adds Anthony.

Toni sees three essential steps as a solution: “A lot of different problem candidates are needed. One of them will be selected for clarification on the basis of common criteria. Work on problem definition both extensively and deeply. Only then should you go to Design Sprint. ”


Pitfall 2: Trapped with existing applications and infrastructure

It’s easy in “greenfield” projects where the “table is clean,” but what to do when the dusty application and IT environment of the years is an obstacle to ambitious digital vision?

Olli-Pekka starts: “Software is not ready until it is taken out of production. Until then, more or less money will sink in, which would be nice to get back, either in terms of working time saved, or just as income. If the systems in production are not kept on track, then the costs that will sink into them are guaranteed to surpass the benefits sooner or later. This is due to inflation and the exponential development of technology. ”

“A really old system that supports a company’s business and is virtually impossible to replace,” continues Jari T. “The low turnover and technology age of it means that the system is not worth replacing. The system will be shut down as soon as the last parts of the business have been phased out. ”

“A monolithic system comes to mind that cannot be renewed part by part. Renewing the entire system would be too much of a cost, ”adds Veera.

Olli-Pekka outlines three different situations: “Depending on the user base, the pressures for modernization are different, but the need for it will not disappear at any stage. Let’s take a few examples.

Consumer products – There is no market for antiques in this industry unless your business is based on the sale of NFTs from Doom’s original source code, and even then. Or when was the last time you admired Win-XP CDs on a store shelf?

Business products – a slightly more complicated case. The point here is that in order for the system you use to be relevant to your business, it needs to play kindly with other systems your organization uses. Otherwise, a replacement will be drawn for it, because manual steps in the process are both expensive and error-prone. However, there is no problem if no one updates their products. I would not lull myself into this.

Internal use – no need to modernize? All you have to do here is train yourself to replace the new ones, because no one else is doing it to your stack anymore. Also, remember to hope that not everyone who manages to entice you into this technological impasse will come up with a peek over the fence. And also remember to set aside a little extra funds for maintenance contracts, as outside vendors may raise their prices when the number of users for their sunset products drops. ”

A few concepts immediately came to mind by Iiro: “Path dependency and Sunk cost fallacy. Could one write own blog about both of them? ”

“What are the reasons or inconveniences for different studies?” ask Sami and Marika.

“I have at least remembered the budgetary challenges, the complexity of the environments, the lack of integration capacity, data security and legislation. So what would be the solution? ”Anthony answers at the same time.

Olli-Pekka’s three ideas emerge quickly: “Map your system – you should also use external pairs of eyes for this, because they know how to identify even the details that your own eye is already used to. An external expert can also ask the right questions and fish for the answers. Plan your route out of the trap – less often you should rush blindly in every direction at the same time. It is enough to pierce the opening where the fence is weakest. From here you can then start expanding and building new pastures at a pace that suits you. Invest in know-how – the easiest way to make a hole in a fence is with the right tools. And a skilled worker will pierce the opening so that it will continue to be easy to pass through without tearing his clothes. It is not worth lulling yourself to find this factor inside the house, because if that were the case, that opening would already be in it. Or the process rots. In any case, help is needed. ”


Pitfall 3: Remaining captive to current policies

“Which is the bigger obstacle in the end: infrastructure and applications or our own operating models and lack of capacity for change?”, Tommi ponders.

“I would be leaning towards operating models myself,” Samuel sees. “I am strongly reminded of the silo between business and IT, the high level of risk aversion, the lack of resilience, the vagueness of the guiding digital vision, and the lack of vision.”

Veera adds, “Let’s start modeling old processes as they are for a new application, instead of thinking about how to change the processes and benefit from better processes at the same time.”

Elmo immediately lists a few practical examples: “Word + Sharepoint documentation is limiting because “this is always the case”. Resistance to change means that modern practices and the latest tools cannot be used, thereby excluding some of the contribution from being made. This limits the user base, as it is not possible to use the organisation’s cross-border expertise. ”

Anne continues: “Excel + word documentation models result in information that is widespread and difficult to maintain. The flow of information by e-mail. The biggest obstacle is culture and the way we do it, not the technology itself. ”

“What should I do and where can I get motivation?” Perttu ponders and continues with the proposed solution: “Small profits quickly – low-hanging-fruits should be picked. The longer the inefficient operation lasts, the more expensive it is to get out of there. Sunk Cost Fallacy could be loosely combined with this. ”

“There are limitless areas to improve.” Markku opens a range of options: “Business collaboration, product management, application development, DevOps, testing, integration, outsourcing, further development, management, resourcing, subcontracting, tools, processes, documentation, metrics. There is no need to be world-class in everything, but it is good to improve the area or areas that have the greatest impact with optimal investment. ”


Pitfall 4: The potential of new cloud technologies is not being exploited

Google Cloud, Azure, AWS or multi-cloud? Is this the most important question?

Markku answers: “I don’t think so. The indicators of financial control move cloud costs away from the depreciation side directly higher up the lines of the income statement, and the target setting of many companies does not bend to this, although in reality it would have a much positive effect on cash flow in the long run. ”

Sanna comes to mind a few new situations: “Choose the technology that is believed to best suit your needs. This is because there is not enough comprehensive knowledge and experience about existing technologies and their potential. Therefore, one may end up with a situation where a lot of logic and features have already been built on top of the chosen technology when it is found that another model would have been better suited to the use case. Real-life experience: “With these functions, this can be done quickly”, two years later: “Why wasn’t the IoT hub chosen?”

Perttu emphasizes: “The use of digital platforms at work (eg drive, meet, teams, etc.) can be found closer to everyday business than in the cold and technical core of cloud technology. Especially as the public debate has recently revolved around the guidelines of a few big companies instructing employees to return to local work. ”

Perttu continues: “Compared to this, the services offered by digital platforms make operations more agile and enable a wider range of lifestyles, as well as streamlining business operations. It must be remembered, of course, that physical encounters are also important to people, but it could be assumed that experts in any field are best at defining effective ways of working themselves. Win-win, right? ”

So what’s the solution?

“I think the most important thing is that the features to be deployed in the cloud capabilities are adapted to the selected short- and long-term use cases,” concludes Markku.


Pitfall 5: Data is not sufficiently utilized in business

Aren’t there just companies that can avoid having the bulk of their data in good possession and integrity? But what are the different challenges involved?

Aleksi explains: “The practical obstacle to the wider use of data in an organization is quite often the poor visibility of the available data. There may be many hidden data sets whose existence is known to only a couple of people. These may only be found by chance by talking to the right people.

Another similar problem is that for some data sets, the content, structure, origin or mode of origin of the data is no longer really known – and there is little documentation of it. ”

Aleksi continues, “An overly absolute and early-applied business case approach prevents data from being exploited in experiments and development involving a“ research aspect ”. This is the case, for example, in many new cases of machine learning: it is not clear in advance what can be expected, or even if anything usable can be achieved. Thus, such early action is difficult to justify using a normal business case.

It could be better to assess the potential benefits that the approach could have if successful. If these benefits are large enough, you can start experimenting, look at the situation constantly, and snatch ideas that turn out to be bad quickly. The time of the business case may be later. ”


Pitfall 6: The use of machine learning and artificial intelligence will not lead to a competitive advantage

It seems to be fashionable in modern times for a business manager to attend various machine learning courses and a varying number of experiments are underway in organizations. However, it is not very far yet, is it?

Aleksi opens his experiences: “Over time, the current“ traditional ”approach has been filed quite well, and there is very little potential for improvement. The first experiments in machine learning do not produce a better result than at present, so it is decided to stop examining and developing them. In many cases, however, the situation may be that the potential of the current operating model has been almost completely exhausted over time, while on the machine learning side the potential for improvement would reach a much higher level. It is as if we are locked in the current way only because the first attempts did not immediately bring about improvement. ”

Anthony summarizes the challenges into three components: “Business value is unclear, data is not available and there is not enough expertise to utilize machine learning.”

Jari R. wants to promote his own previous speech at the spring business-oriented online machine learning event. “If I remember correctly, I have compiled a list of as many as ten pitfalls suitable for this topic. In this event material, they are easy to read:

  1. The specific business problem is not properly defined.
  2. No target is defined for model reliability or the target is unrealistic.
  3. The choice of data sources is left to data scientists and engineers and the expertise of the business area’s experts is not utilized.
  4. The ML project is carried out exclusively by the IT department itself. Experts from the business area will not be involved in the project.
  5. The data needed to build and utilize the model is considered fragmented across different systems, and cloud platform data solutions are not utilized.
  6. The retraining of the model in the cloud platform is not taken into account already in the development phase.
  7. The most fashionable algorithms are chosen for the model. The appropriateness of the algorithms is not considered.
  8. The root causes of the errors made by the model are not analyzed but blindly rely on statistical accuracy parameters.
  9. The model will be built to run on Data Scientist’s own machine and its portability to the cloud platform will not be considered during the development phase.
  10. The ability of the model to analyze real business data is not systematically monitored and the model is not retrained. ”

This would serve as a good example of the thoroughness of our data scientists. It is easy to agree with that list and believe that we at Codento have a vision for avoiding pitfalls in this area as well.


Summary – Avoid pitfalls in a timely manner

To prevent you from falling into the pitfalls, Codento consultants have promised to offer two-hour free workshops to willing organizations, always focusing on one of these pitfalls at a time:

  1. Digital Value Workshop: Clarified and understandable business problem to be solved in the concept phase
  2. Application Renewal Workshop: A prioritized roadmap for modernizing applications
  3. Process Workshop: Identifying potential policy challenges for the evaluation phase
  4. Cloud Architecture Workshop: Helps identify concrete steps toward high-quality cloud architecture and its further development
  5. Data Architecture Workshop: Preliminary current situation of data architecture and potential developments for further design
  6. Artificial Intelligence Workshop: Prioritized use case descriptions for more detailed planning from a business feasibility perspective

Ask us for more information and we will make an appointment for August, so the autumn will start comfortably, avoiding the pitfalls.


Piloting Machine Learning at Speed – Utilizing Google Cloud and AutoML

Piloting machine learning at speed – Utilizing Google Cloud and AutoML


Can modern machine learning tools do one-weeks work in an afternoon? The development of machine learning models has traditionally been a very iterative process. The traditional machine learning project starts with the selection and pre-processing of data sets: cleaning and pre-processing. Only then can the actual development work of the machine learning model be started.

It is very rare, virtually impossible, for a new machine learning model to be able to make sufficiently good predictions on the first try. Indeed, development work traditionally involves a significant number of failures both in the selection of algorithms and their fine-tuning, in technical language in the tuning of hyperparameters.

All of this requires working time, in other words, money. What if, after cleaning the data, all the steps of development could be automated? What if the development project could be carried through at an over-paced sprint per day?


Machine learning and automation

In recent years, the automation of building machine learning models (AutoML) has taken significant leaps. Roughly described in traditional machine learning, the Data Scientist builds a machine learning model and trains it with a large dataset. AutoML, on the other hand, is a relatively new approach in which the machine learning model builds and trains itself using a large dataset.

All the Data Scientist needs to do is tell you what the problem is. This can be a problem with machine vision, pricing or text analysis, for example. However, Data Scientists will not be unemployed due to AutoML models. The workload shifts from fine-tuning the model to validating and using Explainable-AI tools.


Google Cloud and AutoML used to sole a practical challenge

Some time ago, we at Codento tested Google Cloud AutoML-based machine learning tools [1]. Our goal was to find out how well Google Cloud AutoML tool solves the Kaggle House Prices – Advanced Regression Techniques challenge [2].

The goal of the challenge is to build the most accurate tool possible to predict the selling prices of real estates based on their properties. The data set used in the building of the pricing model contained data on approximately 1,400 real estates: In total 80 different parameters that could potentially affect the price, as well as their actual sales prices. Some of the parameters were numerical, some were categorical.


Building a model in practice

The data used was pre-cleaned. The first phase of building the machine learning model was thus completed. First, the data set, a file in csv format, was uploaded as is to Google Cloud BigQuery data warehouse. The download took advantage of BigQuery’s ability to identify the database schema directly from the file structure. The AutoML Tabular feature found in the VertexAI tool was used to build the actual model.

After some clicking, the tool was told which of the price predictive parameters were numeric and which were categorical variables. In addition, the tool was told which column contains the predicted parameter. It all took about an hour to work. After that, the training was started and we started waiting for the results. About 2.5 hours later, the Google Cloud robot sent an email stating that the model was ready.


The final result was a positive surprise

The accuracy of the model created by AutoML surprised the developers. Google Cloud AutoML was able to independently build a pricing model that predicts home prices with approximately 90% accuracy. The level of accuracy per se does not differ from the general level of accuracy of pricing models. It is noteworthy here, however, that the development of this model took a total of half a working day.

However, the benefits of GCP AutoML do not end there. It would be possible to integrate this model with very little effort into the Google Cloud data pipeline. The model could also be loaded as a container and deployed in other cloud platforms.


Approach which pays off in the future as well

For good reason, tools based on AutoML can be considered the latest major development in machine learning. Thanks to the tools, the development of an individual machine learning model no longer has to be thought of as a project or an investment. Utilizing the full potential of these tools, models can be built with an approximately zero budget. New forecasting models based on machine learning can be built almost on a whim

However, the effective deployment of AutoML tools requires a significant initial investment. The entire data infrastructure, data warehouses and lakes, data pipelines, and visualization layers, must first be built with cloud-native tools. Codento’s certified cloud architects and data engineers can help with these challenges.



Google Cloud AutoML, 

Kaggle, House Prices – Advanced Regression Techniques,


The author of the article is Jari Rinta-aho, Senior Data Scientist & Consultant, Codento. Jari is a consultant and physicist interested in machine learning and mathematics, with extensive experience in utilizing machine learning in nuclear energy. He has also taught physics at several universities and led international research projects. Jari’s interests include ML-Ops, AutoML, Explainable AI and Industry 4.0.


Ask more about Codento’s AI and data services:

Single or Multi-Cloud – Business and Technical Perspectives

#NEXTGENCLOUD: Single or Multi-Cloud – Business and Technical Perspectives


Author: Markku Tuomala, CTO, Codento


Traditionally, organizations have chosen to focus all their efforts on single public cloud solutions when choosing architecture. The idea has often been to optimize the efficiency of capacity services. In practice, this means migration of existing applications to the cloud – without changes to the application architecture.

The goal is to concentrate the volume on one cloud service provider and thereby maximize the benefits of operating Infrastructure Services and service costs.


Use Cases as a Driver

At our #NEXTGENCLOUD online event in November 2021, we focused on the capabilities of the next generation cloud and what kind of business benefits can be achieved in the short term. NEXTGENCLOUD thinking means that the focus is on solving the customer’s need with the most appropriate tools.

From this perspective, I would divide the most significant use cases into the following category:

  • Development of new services
  • Application modernizations

I will look at these perspectives in more detail below.


Development of New Services

The development of new services is started by experimenting, activating future users of the service and iterative learning. These themes alone pose an interesting challenge to architectural design, where direction and purpose can change very quickly with learning.

It is important that the architecture supports large-scale deployment of ready-made capabilities, increases service autonomy, and provides a better user experience. Often, these solutions end up using the ready-made capabilities of multiple clouds to get results faster.


Application Modernizations

The clouds are built in different ways. The differences are not limited to technical details, but also include pricing models and other practices. The different needs of applications running in an IT environment make it almost impossible to predict which cloud is optimal for business needs and applications. It follows that the right need is determined by an individual business need or application, which in a single cloud operating environment means unnecessary trade-offs as well as technically sub-optimal choices. These materialize in terms of cost inefficiency and slowness of development.

In the application modernization of IT environments, it is worth maximizing the benefits of different cloud services from the design stage to avoid compromises, ensure a smooth user experience, increase autonomy, diversify production risk and support future business needs.


Knowledge as a bottleneck?

Is there knowledge in all of this? Is multi-cloud technology the biggest hurdle?

It is normal for application architects and software developers to learn more programming languages ​​than new treatment methods for doctors or nurses. The same laws apply to the development of knowledge of multi-cloud technologies. Today, more and more of us have been working with more cloud technology and taking advantage of ready-made services. At the same time, technology for managing multiple clouds has evolved significantly, facilitating both development and cloud operations.


The author of the blog Markku Tuomala, CTO, Codento, has 25 years of experience in software development and cloud, having worked for Elisa, Finland’s leading telecom operator. Markku was responsible for the cloud strategy for Telco and IT services and was a member of Elisa’s production management team. The key tasks were Elisa’s software strategy and the management of operational services for business-critical IT outsourcing. Markku drove customer-oriented development and played a key role in business growth, with services such as Elisa Entertainment, Book, Wallet, self-service and online automation. Markku also led the change of Elisa’s data center operations to DevOps. Markku works as a senior consultant for Codenton Value Discovery services.


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Part 2. The Cloud of the Future

Part 2. The cloud of the future – making the right choices for long-term competitiveness

Author: Jari Timonen, Codento Oy

# NEXTGENCLOUD – the cloud of the future – is the frame of reference on which we at Codento believe in building the long-term success of our customers.

As the cloud capabilities of mainstream suppliers evolve at an accelerating pace, it is extremely important to consider the potential of these new features when making the right choices and clarifying plans.

We at Codento feel that developing a vision in this area is our key role. In cooperation with technology suppliers and customers, we support customers’ business and enable application innovation and modernization.

In our two-part blog series and the upcoming # NEXTGENCLOUD event, we’re opening up our key insights.

  • Part 1: The cloud of the future: shortcut to business benefits
  • Part 2: The cloud of the future: long term competitiveness through technology

In this blog, we discuss how the cloud architecture of the future will enable long-term competitiveness.

The target architecture is the support structure of everything new

The houses have load-bearing walls and separately lighter structures for justified reasons. What kind of structures are needed in cloud architectures?

The selection of functional structures is guided by the following factors.

Identification of functional layers

  • Selection of services suitable for the intended use
  • Loose integration between layers
  • Comprehensive security

Depending on the capabilities of each public cloud provider, a unique target architecture can be defined. In multi-cloud solutions, respectively, a multi-cloud architecture with multi-cloud capabilities.

Future architecture with Google Cloud technologies should consider the following four components:

  • Data import and processing (Ingestion and processing)
  • Data Storage
  • Applications
  • Analytics, Reporting and Discovery

There are a number of different alternative and complementary cloud services available in each section that address a variety of business and technical challenges. It is noteworthy in architecture that no service plays a central or subordinate role to other services.

The cloud solutions and services of the future are part of the overall architecture. Services that may be phased out or replaced will not impose a large-scale change burden on the overall architecture.

New generation cloud enables cloud computing

When designing a target architecture, the capabilities offered by the cloud to decentralize computing and data storage closer to the consumer or user of the data must be considered.

In the early days of the Internet, application code was run solely on servers. This created scalability challenges as user numbers increased. Later, when reforming application architectures, parts of the application were distributed to different computers, especially in terms of user interfaces. This facilitated server scalability and reduced the risk of unplanned downtime. Most of the application code visible to the user is executed on phones, tablets, or computers, while business logic is executed in the cloud.

A similar revolution is now taking place in cloud computing capacity.

In the future, all workloads will not only be driven in the large service centers of cloud services, but will also be driven closer to the customer. Examples of such solutions are e.g. applications requiring analytics, machine learning, and other computing power, such as the Internet of Things.

Some applications require such low latency that it requires computing power close to the customer. The close geographical location of the data center may not be enough, but local computing capacity is needed for edge computing.

The smart features of the cloud enable new applications

The cloud has evolved from a virtual machine-centric mindset that optimizes initial cost and capacity to smarter services. Using these smart services allows you to focus on the essential, i.e. generating business value. The development of new generation cloud capabilities and services will accelerate in the future.

Increasingly, we will see and leverage cloud-based smart applications that effectively leverage the capabilities of the next generation of clouds from the edge of the web to centralized services.

With modern telecommunication solutions, this enables customers to take on a whole new kind of service, with an architecture far into the future. Examples include extensive support for the real-time requirements of Industry 4.0, self-driving cars, new healthcare services, or a true-to-life virtual experience.

Sustainable and renewable cloud architecture, the utilization of edge computing and the use of smart services are all part of our # NEXTGENCLOUD framework.

The author of the blog, Jari Timonen, is an experienced software professional with more than 20 years of experience in the IT field. Jari’s passion is to build bridges between the business and the technical teams, where he has worked in his previous position at Cargotec, for example. At Codento, he is at his element in piloting customers towards future-compatible cloud and hybrid cloud environments.

Part 1. The Cloud of the Future

Part 1. The cloud of the future – a shortcut to  business benefits?

Author: Jari Timonen, Codento

#NEXTGENCLOUD – the cloud of the future – is the frame of reference on which we at Codento believe in building the long-term success of our customers.

As the cloud capabilities of mainstream suppliers evolve at an accelerating pace, it is extremely important to consider the potential of these new features when making the right choices and clarifying plans.

We at Codento feel that developing a vision in this area is our key role. In cooperation with technology suppliers and customers, we support customers’ business and enable application innovation and modernization.

In our two-part blog series and the upcoming # NEXTGENCLOUD event, we’re opening up our key insights:

  • Part 1: The cloud of the future: shortcut to business benefits
  • Part 2: The cloud of the future: long term competitiveness through technology

In this blog, we discuss how the cloud of the future will enable you to achieve business benefits quickly.

At the start, open-mindedness is valuable

Reflecting on business perspectives related to cloud services requires a multi-level review. This reflection combines the desired business benefits, the characteristics of the applications, and the practices and goals of the various stakeholders.

How do we combine rapid uptake of innovation with cost-effectiveness? Through the right choices and implementations, new business can be supported and developed both faster and more efficiently. From an application perspective, it is about the capabilities of the technical cloud platform to enable the desired benefits. From the perspective of processes and practices, the goals are transparency, flexibility, automation and scalability.

The robustness benefits of a cloud require cloud-capable applications

Modernizing applications that are important to business is a key step in achieving business benefits. Many customers have not fully achieved their intended cloud benefits in first-generation cloud solutions. Some of the disappointments are related to the so-called lift-and-shift cloud transition where applications are moved almost as is to the cloud. In this case, almost the only potential benefit lies in the savings in infrastructure costs. Cloud-based applications are, in principle, the only real sustainable way to achieve the vast business benefits of the cloud.

Stability cloud support for applications

The cloud of the future will support business applications at many different levels:

  • Cost-effective run environment
  • Platform-as-a-Service (PaaS) and Software-as-a-Service (SaaS) services to replace business applications or parts thereof
  • Value-added functionalities such as cost-effective analytics and reporting

Examples of such cloud technologies that support business applications include:

  • Google Cloud Anthos / Google Kubernetes Engine (Hybrid, Multi and Single Cloud Environments)
  • Google Cloud BigQuery (Data Warehouse)
  • Google Data Studio (Reporting)
  • Google Cloud Looker (Enterprise-Level Analytics)

Cloud capabilities and identifying new opportunities

Most organizations have built their first-generation cloud capabilities based on a single cloud technology. At the same time, the range of alternative possibilities has grown and, through practical lessons, the so-called multi-cloud path.

Both paths of progress require a continuous and rapid ability to innovate and innovate throughout the organization to achieve cloud business benefits.

Strong business support is needed on this journey. Innovation takes place in collaboration with the developers, architects and the organization that guides them. Those involved need realistic financial opportunities to succeed. Active interaction between different parties is important for success. It is important to create a culture where you can try, fail, try again and succeed.

Innovation is supported by an iterative process familiar from agile development methods, during which hypotheses are made and tested. These results are reflected in the functionalities, operating methods and productizations put into practice in the future.

The cloud of the future and the three levels of innovation

Innovation in the cloud now and in the future can be roughly divided into three different areas:

  • Business must be timely, profitable and forward-looking. Innovation creates new business or accelerates an existing one.
  • The concept ensures that we are doing the right things. This must be validated by the customers and judged to be as accurate as possible. Customer means a target group that can consist of internal or external users.
  • Technical capability creates the basis for all innovation and future productization. The capability grows and develops flexibly and agilely with the business.

The cloud of the future will support the three paths mentioned above even more effectively than before. New services enabling the platform and API economy are growing in the cloud, reducing the time required for maintenance.

The fastest way to get business benefits is through MVP

Cloud development must be relevant and value-creating. This sounds obvious, but it’s not always so.

Value creation can mean different things to different people. Therefore, a Minimum Viable Product (MVP) approach is a good way to start implementation. MVP is a way of describing the smallest value-producing unit that can be implemented and exported to production. Many times here, old thought patterns create traps: “All features need to be ready in order to benefit.” However, if we start to go through the product, then we find that there are things that are not needed in the first stage.

These can include changes to your profile, full-length visual animations, or an extensive list of features. MVP is also a great way to validate your own plans and evaluate the value proposition of the application.

The cloud supports this by providing tools for innovation and development as well as almost unlimited capacity. This development will continue in the cloud of the future, giving new applications a better chance of succeeding in their goals.

And finally

Thus, the fastest and most likely acceleration of success to business benefits is through #NEXTGENCLOUD thinking, cloud-enabled applications, and the MVP business model. The second part of the blog will later discuss more technology perspectives and the achievement of long-term benefits.

The author of the article, Codento’s Lead Cloud Architect, Jari Timonen, is an experienced software professional with over 20 years of experience in the IT industry. Jari’s passion is to build bridges between the business and technical teams, where he has worked in his previous position at Cargotec, for example. At Codento, he is at his element in piloting customers towards future-compatible cloud and hybrid cloud environments.