Building Cloud-Based IoT Solutions and Serverless Web-Apps

Our Cloud Application Architect Afaque Hussain has been on his cloud journey for some years already. At Nordcloud he builds cloud-native IoT solutions in our Data-Driven team. Here’s his story!

aws cloud IoT internet of things web applications development

1. Where are you from and how did you end up at Nordcloud?

I’ve been living in Finland for the past 7 years and I’m from India. Prior to Nordcloud, I was working at Helvar, developing cloud-based, IoT enabled, lighting solutions. I’ve been excited about public cloud services ever since I got to know them and I generally attend cloud conferences and meetups. During one such conference, I met the Nordcloud team who introduced me to the company and invited me for an interview and since then, my Nordcloud journey has begun.

2. What is your core competence? On top of that, please also tell about your role and projects shortly.

My core-competence is building cloud-based web-services, that act as an IoT platform to which IoT devices connect and exchange data. Generally preferring Serverless computing and Infrastructure as Code, I primarily use AWS and Javascript (Node.js) in our projects.

My current role is Cloud Application Architect, where I’m involved in our customer projects in designing and implementing end-to-end IoT solutions. In our current project, we’re building a web-service using which our customer can connect, monitor and manage their large fleet of sensors and gateways. The CI/CD pipelines for our project have been built using AWS Developer Tools such CodePipeline, CodeBuild & CodeDeploy. Our CI/CD pipelines have been implemented as Infrastructure as Code, which enables us to deploy another instance of our CI/CD pipelines in a short period time. Cool!

3. What sets you on fire / what’s your favourite thing technically with public cloud?

The ever increasing serverless service offerings by public cloud vendors, which enables us to rapidly build web-applications & services.

4. What do you like most about working at Nordcloud?

Apart from the opportunity to work on interesting projects, I like my peers. They’re very talented, knowledgeable and ready to offer help when needed.

5. What is the most useful thing you have learned at Nordcloud?

Although I’ve learnt a lot at Nordcloud, I believe the knowledge of  the toolkit and best practices for cloud-based web-application development has been the most useful thing I’ve learnt.

6. What do you do outside work?

I like doing sports and I generally play cricket, tennis or hit the gym. During the weekends, I generally spend time with my family, exploring the beautiful Finnish nature, people or different cuisines. 

7. How would you describe Nordcloud’s culture in 3 words?

Nurturing, collaborative & rewarding.

8. Best Nordcloudian memory?

Breakfast @ Nordcloud every Thursday. I always look forward to this day. I get to meet other Norcloudians, exchange ideas or just catch-up over a delicious breakfast!

 

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    Nordcloud @ Smart Factory 2018 in Jyväskylä – 20-22.11.2018

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    Nordcloud at Smart Factory 2018 Jyväskylä

    Make sure to visit Nordcloud’s booth (C430) at the ‘Smart Factory 2018’ event, which is held in The Congress and Trade Fair Centre Jyväskylän Paviljonki, Jyväskylä between the 20-22.11.2018.

     

    Smart Factory 2018 is an event focused on how to utilise opportunities offered by digitalisation

    The event gathers together the themes of Industry 4.0 and the related technology, service and expertise offering. Smart Factory 2018 is targeted at all operators who are involved with changes associated with digital transformation in production activities and related new services and concepts. It strongly emphasizes the already known future-building themes, such as automation, machine vision, robotics, industrial internet and cybersecurity.

    You can register for the event here:

    Register to Smart Factory 2018

    See you there!

    Nordcloud at Smart Factory 2018

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      Cloud Computing News #2: Digital transformation in the cloud

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      This week we focus on digital transformation and IT transformation in the cloud.

       

      Campbell’s Drives IT Transformation on Azure

      Campbell Soup Co has partnered with Microsoft to modernize Campbell’s IT platform through the Azure cloud by streamlining workflows and driving efficiencies.

      “Campbell’s migration to Azure will increase our flexibility, agility and resiliency,” said Francisco Fraga, Campbell Soup’s CIO. “Azure will give us the ability to respond quickly to evolving business needs, introduce new solutions, and support our 24/7, always-on architecture. The Microsoft cloud is a proven, reliable and highly secure platform.”

      The Microsoft solution will provide additional benefits, including increased security, compliance and information protection. The move to Azure will allow Campbell to re-architect its data warehousing capabilities to be able to support the company’s data and analytics needs.

      Read the full article here

      Nordcloud is also Microsoft Gold Cloud Partner and Microsoft Azure Expert Managed Services Provider. Accelerate operations by moving IT to the public cloud with our solutions, you can find them here.

       

      Walmart Picked Microsoft To Accelerate Digital Transformation in the cloud

      According to Forbes, Walmart has signed a 5-year strategic partnership with Microsoft to accelerate digital transformation. This is an extension of an existing relationship between Walmart and Microsoft.

      This new agreement will see the companies collaborating on machine learnings, AI and data-platform solutions that span customer-facing projects as well as those aimed at optimizing internal operations.

      3 focus ares of the partnership are:

      1. Digital transformation:  Walmart will have the full range of Microsoft cloud solutions, move hundreds of existing applications to cloud-native architectures, migrate of a significant portion of walmart.com and samsclub.com to Azure to grow and enhance the online customer experience.
      2. Innovation: Walmart will build a global IoT platform on Azure.
      3. Changing way of working at Walmart: Walmart is investing in its people with a phased rollout of Microsoft 365.

      More on Walmart´s digital transformation in Forbes.

      Read also our blog post on how to accelerate digital transformation with culture and cloud here.

      Our data driven solutions that will make an impact on your business you can find here.

       

      Gartner identifies 6 barriers to becoming a digital business

      According to a recent survey by Gartner, companies embracing digital transformation are finding that digital business is not as simple as buying the latest technology but requires changes in systems and culture.

      Gartner lists six barriers that CIOs must overcome to transform their business:

      1. A Change-Resisting Culture. Digital innovation requires collaborative cross-functional and self-directed teams that are not afraid of uncertain outcomes.
      2. Limited Sharing and Collaboration. Issues of ownership and control of processes, information and systems make people reluctant to share their knowledge. But it is not necessary to have everyone on board in the early stages.
      3. The Business Isn’t Ready. When a CIO wants to kick-off a transformation, they find that the business doesn’t have the resources or skills needed.
      4. The Talent Gap. Markus Blosch, research vice president at Gartner, says: “There are two approaches to breach the talent gap — upskill and bimodal.”
      5. The Current Practices Don’t Support the Talent. Highly structured and slow traditional processes don’t work for digital.
      6. Change Isn’t Easy. Gartner advocates adopting a platform-based strategy which supports continuous change.

      Read more about the survey on Gartner Newsroom.

      Read also our blog post on how to support cloud and digital transformation here.

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        What is data-driven service design?

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        For a few years now, I’ve been engaged in a personal passion project of explicating what the increasing abundance of data can do for design. My most recent definition of data-driven design is that it means digitalisation and automation of design research. In future, data-driven design will possibly reach out to decision making and generative design. But we’re not there yet.

        As I’ve written over the years about the concept and tools of data-driven design, my musing around the topic has been somewhat limited. As I’m operating in a digital design and development company context, design has referred to interaction design: user interface design decisions and how to best implement certain features.

        I have left several design domains with little attention. In this article, I will venture a bit beyond my home turf. I’ll change the question and think about what should we build, instead of how we create it. This question takes a step to a higher abstraction level, that commonly associated with service design. In the following, I’ll consider what the big data world could offer for service design.

        Why → What → How
        (business design → service design → interface design)

        What good can more data do for service design?

        Service design is a bit of niche area of its own originating from 1980’s. Starting from the design of banking services, it has since slowly grown to be recognised profession serving the development of many physical touch points. But nowadays professionals calling themselves service designers also regularly deal with digital touch points.

        In the few visual depictions of what is the overall field of design visualised below, service design is totally missing from the left one (based Dan Saffer) illustrating UX design and occupies a small segment of human-centred design. But I assure you, it still exists, even thou it is clearly far out of the spotlight of more recent disciplines of digital design.

        How about the use of data in this domain? The public examples of data-driven service design are rare. For instance, the global Service Design Network chapter Netherlands was apparently among the first to host a session specifically aimed at sharing experiences with data in service design.

        The short story written about the data-driven service design event gives an opinion I can readily agree with: quantitative data must complement, challenge and give a foundation for qualitative data.

        Service design requires a mix of research inputs

        The long-term experience design specialist Kerry Bodine puts it as “service design requires a mix of research inputs.” She has expressed a great concern of over-reliance on big data methods without the complementary qualitative insights. This relationship has been previously highlighted by Pamela Pavliscak under the terms big and thick data, in order to highlight their contemporary nature.

        In other words, data-driven design means using more data, particularly quantitative, in the design process.

        A side note: a term that may seem relevant to data-driven service design is service analytics. Service analytics, in my opinion, are a subset of traditional analytics areas: web analytics, market intelligence, and business intelligence. For instance, in Sumeet Wadhwa’s article on the topic, service analytics are presented foremost as a tool quantify, track and manage service design efforts, not so much inspire or help to find new design opportunities. Thus they are not a creative driver for the design process.

        “Data” for transformational design is embodied in designers, not the customer

        Data can’t solve or even easily be used to support all design decisions.  Given that people are naturally resistant to change, defending any major change using backward-looking data is not going be easy. In a recent post, frog founder Hartmut Esslinger provided strong criticism for misinterpretations of “big” data.

        His examples very neatly illustrate conservative interpretation bias of data. For instance, in a 2001 Motorola case, the company discarded a touchscreen smartphone concept (later known as the iPhone) because market intelligence data clearly showed people wanted to buy phones akin to those designed by Nokia! Clearly, the data-based insight was inferior to a “designer-based” insight about what you should create.

        Solving this challenge is not easy. I’ve personally helped to articulate one user acceptance testing approach called resonance testing originating from American design company Continuum. This method presents a quite specific procedure to investigate quantitatively consumers reactions to ‘what’ questions. However, this method is dependent upon face-to-face interactions and does not thus really fall within the domain of data-driven design as defined at the start.

        Tools for data-driven service design

        The data-driven or data-informed design does not identify any particular design approach. However, I see that it requires a certain prototypical process to support it. First and foremost, it always requires real data. Representative data must be collected, analysed, inferences made and brought to bear upon design decisions and new designs.

        Data-driven design always requires real data

        What kind of data and which tools of analysis will help service designers to decide what needs to be created? In my previous writing, I’ve proposed a taxonomy of the different types of tools available for data-driven design. Starting from there, we can observe that we have three categories of tools that hold a promise in this direction. They are active data collection solutions, user recordings, and heat maps.

        Once more the origin of these tools is within the digital domain, in the web and mobile apps, but it is more important to bear in mind that they are very heavily related to the foremost revision or assessment of existing features. They can give a glimpse of what else your customers might love, what they fail to achieve or which part of service they neglect.

        Passive records from use sessions on digital or physical touch points can be revealing, but active data collection – from co-design to all manners of classical qualitative research has been the core of service design research. But are there any qualitative research methods that can scale, to provide the automation aspect I attach to data-driven design approach?

        Different types of surveys naturally scale well. Especially digital environments offer unprecedented opportunities to target and trigger surveys, making them much more powerful than they were in the past.  Of course, they are limited by the structure of their insight. But free, open-ended can be very intuitive and applicable in data-driven design if we can also provide the tools that automate the analysis of the inputs, not just collection. Sentiment analysis alone, as criticised by Boden above, is a weak method. Segmentation and automated summaries can add value to aggregate figures alone. This is bit futuristic but already feasible (see also Zendesk’s approach to data in automating customer service).

        Insights from the local industry insiders

        I had a chance to talk with Petteri Hertto, a long-term specialist in quantitative research, about the topic. He is a service designer currently working at Palmu agency in Helsinki, Finland. He says that too many projects feel obliged to gather quantitative data without good reasons. They end up with data that is non-actionable from a design point of view.

        Petteri has personally transformed from a quantitative data specialist to a designer that sees value in both types of data. “The best uses of quantitative data lie in proofing new ideas and verifying a business case around it,” he believes. Petteri has documented a model of value measurement his agency prefers in a Touchpoint article (Touchpoint magazine is the journal published by Service Design Network).

        Are there any new tools specifically for data-driven service design?

        I further pressed Petteri on whether any (quantitative) design research tools have appeared in the past 10 years that would resemble my definition of data-driven design.

        He recounted that there are few radically new developments. In the design approach favoured by their agency, they use the same tools as UX designers, including those data-intensive ones. However, he named one novel survey tool made possible by mobile technologies. It addresses several deficiencies of validity in traditional research.

        Crowst is a Finnish startup which provides surveys targeted on verified user behaviour in the physical world, improving the quality of input.

        Then again, this is an incremental improvement over existing tools, not a radically novel approach with unforeseen data masses, new level of insight or scalability.

        Can data reveal what the customer needs?

        Are we back to square one in terms of answering the question of what does the customer want? Yes and no. I believe a thoughtful analysis of big data can serve three purposes in service design:

        1. Identify opportunities for new experiences & features
        2. Inspire solution creation
        3. Validate solutions*

        * difficult to validate without a detailed implementation and answering the how question

        However, the data about yesterday can’t really tell us what is going to happen tomorrow. We have to more or less make the future available today through scenarios and prototypes which can generate the data that illustrates the future.

        Recap

        Data-driven design in user interface level is in good speed, but the need for qualitative insight still dominates service design. Contemporary service designs acknowledge the potential – and danger – in big data, but the tools to transform the potential into a revolution in the ways of working is still missing.

        It is evident service designers must be comfortable with working with data as big as it comes. However, ready-made tools and methods are far fewer than in user interface design. Answering the fundamental question “what to design” is notoriously difficult with data that describes things of the past.

        I believe it is and will be possible even to a greater extent than we can today imagine in a couple of years. Join the revolution today!

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