The Journey to Managed Enterprise IoT – Part 3 – Beyond the use case

<This blog was previously published at the Atos Thought Leadership website – it was written by Philip Griffiths>

In the 1st blog in the series, an overview was shared of the journey to managed enterprise IoT, which we divide into three levels of maturity. In my second post you will learn more about the first level: Enabling the use case.

Once you have determined the IoT use case you need to ensure that the solution is secure, accurate and predictable – i.e. it delivers sustainable value – in the face of increasing quality of devices, edges, continuous data flows, and technologies. This is enabled through ‘non-functional requirements’ (NFRs) and spans both execution qualities and evolution qualities. This is going ‘beyond the use case’.

By focusing on ‘how’ the use case is delivered, instead of ‘what’, you can realize the following benefits:

  • Quality: If your products and services cannot extract value, the data, and opportunities, it is lost.
  • Risk: Complex IoT use cases can put business continuity at risk; reduce it through strong NFRs.
  • Productivity: A use case has little value if it stops working after 6 months or in peak load periods.
  • Future-proofing: Your system should be built for low cost and simple improvements / evolution.

Realizing this require you to define, discuss, document, and design your NFRs as you enable the use case and beyond it. Extending the timeline from the previous post, the following activities take around 12-24 months:

  1. Project: Design solution, install and implement IoT solution from core to edge, integrate into your existing business systems as well as ensure system security – including building ecosystem of partners.
  2. Business Platform: Scale-up and industrialize the use case to a full platform, do further roll-outs, integrate into your existing business and enhance parts of the system to delivery sustainable value.

Below is a non-exhaustive list of topics and example questions to be considered for NFRs:

NFR Topic Example Questions
Execution Availability Does your E2E use case need an operational uptime of 99.99% or 95%?
Continuity What are you E2E RTO/RPO? What are the business impact / cost of your use cases being down for 1 or 60 minutes? How does this impact backup or disaster recovery? How do you enable HA, backup or disaster recovery in a distributed architecture?
Manageability How will you update millions of devices? When and how to push new functionality? How to handle a million+ devices all calling home sick? Will you have 24×7 operations?
Interoperability  How will data be handled across different silos? Do your platforms work together?
Performance How quickly do you need to access the data? Does it need to be processed at edge?
Resilience How will the system do backups and ensure service continuity? How will you ensure high uptime of distributed architecture? How will errors be handled ‘gracefully’?
Security How will data from connected objects be trusted? How will you ensure security in new and high risk environments? How will you reduce attack surface? How will you ensure internal and external compliance, auditability as well as alignment to standards?
Usability How will you reduce the head count of managing such E2E complexity? How will you optimize latency issues to improve real-time outcomes and / or use experience?
Evolution Maintainability How will defects be corrected? Will the system and components have self-healing? How will uptime be ensured without sending people / parts onsite?
Modularity Will your system be built on principles of separate independent functionality?
Scalability Will it scale up/down to meet peak demands?

 By focusing on ‘how’ you enable value from and not just ‘what’ the use case is you will derive much greater long term value for your business. To do this, you need to define, discuss, document and design them into the use case from the start. If you are not already doing this I suggest you look at facilitating it as soon as possible.

I have spoken to many clients who have rolled out IoT solutions which are ‘the future of the businesses’. It is therefore unfortunate when they stop working effectively or if the operations team only know of problems when they are informed by the customer. It is normally at this point that they ask for expertise on going ‘beyond the use case’.  A few common outcomes are re-building the app, creating a new platform, facilitating rapid scalability or enabling an operations team with E2E monitoring; either way, it costs time and money that were not predicted in the business case.

The journey to managed enterprise IoT – Part 2 – Enabling the use case

<This blog was previously published at the Atos Thought Leadership website – it was written by Philip Griffiths>

In my previous blog, I shared our overview of the journey to managed enterprise IoT, which we divide into three levels of maturity. Here I’ll explain the first level: Enabling the use case. For each company to transform data – the world’s most valuable resource – into business outcomes, they first need to work out how data and IoT will improve the business. This could be by enhancing customer experience and improving your internal organization. Once the strategy is picked, a use case can be developed and tested with speed and agility to measure the outcome and validate its value. This is ‘enabling the use case’.

By taking advantage of data, companies can deliver a multitude of benefits by delivering IoT projects. Examples include:

  • Customer Experience: Using data to understand market demands, behavior and buying trends and develop new products and services your customer’s will love to use
  • Business Reinvention: Market agility with new business models, products, services and revenue streams
  • Operational excellence: Gain efficiency and agility with data-driven business processes
  • Trust & Compliance: Unleash the power of analytics to protect your assets

To realize these benefits, 3 key activities need to be developed:

  1. Strategy & Ideation(S&I): Explore market changes, customer needs, business problems, opportunities and available data to select IoT use cases that enable data insights and sustainable business value. This includes identifying the business processes, any applicable standards and users.
  2. Proof of Value (PoV): Execute rapid prototyping to test and prove that the use case delivers value – the best approach is a limited scope and time frame. If it holds value, you should also develop a high level architecture for the future.
  3. Business Case (BC): Define the BC for the next steps – including investment costs and a genuine ROI – and business model that will be used while actively looking ‘beyond the use case’ – see next blog piece.

Across each of these topics, agility and an exploratory nature are critical. We expect the use case enablement to take anywhere between 2-4 months from the initial workshop, detailed study and developing PoV with BC – if it is then rolled out as a project this could take another 6-12 months. There should be a few deliverables (non-exhaustive list):

  • Strategy & Ideation: Data strategy, process scoping, value assessment, connectivity definition, high level plan for PoV and BC, management presentation.
  • Proof of Value: Requirements, built and tested PoV with results, use case feasibility report, project solution architecture, management presentation.
  • Business Case: Strategy map, benefits profile (with KPIs), project costing, business case, investment performance analysis, project plan, management presentation.

The first step in getting more value from your business data is to brainstorm and assess the IoT opportunities that could enable real business benefits. In starting the journey you take one step closer to delivering internal and external changes within your organization based upon the data you already have. I recommend holding a discovery workshop to identify the benefits you will enable and your next steps. Sounds pretty simple and straightforward, doesn’t it?

My customers often find that some data is more valuable than others. Out of the hundreds or thousands of data points that could be gathered, only a small handful will give the highest likelihood of accurately determining a business outcome (following an 80/20 rule). Clustering, co-occurrence and classification analysis techniques can help you to determine which data points produce the greatest value and therefore what you should focus on.

Check out my next blog where I’ll address the 2nd maturity level: ‘Beyond the use case’.

I would like to add a special thanks to Philip Griffiths (@ThePGriffiths). Philip was until recently, the strategic partner manager for the IoT practice and took the initiative to write this blog-series that you are reading now.

4 ways the Industrial Internet of Things can enable digital transformation

Disclosure: This post was previously published on Atos Ascent and was co-authored by Mr. Andrea Sorrentino (who wrote a significant part of the text below) – minor format and content edits have been applied to fit it to this website.

Today the manufacturing industry faces one of the biggest challenges in modern times: how to embrace the next industrial revolution. The technological disruption which has arisen from “Industry 4.0”, the current trend of automation and data exchange in manufacturing technologies, has drastically changed how we see the world today. Moreover, the highly competitive landscape poses urgent questions of change management that manufacturing companies need to address quickly.

Nowadays, in a more inter-connected world, companies need to adopt the right tools to get closer to the market and become more competitive. The manufacturing industry needs to adopt big data to innovate, to optimize their processes, and improve yields. But how should managers approach this technological revolution and work towards creating a smarter factory?

Machinery performance can now be measured with small sensors connected to the internet, monitoring where efficiency can be optimized. For example, workers will be able to foresee machinery malfunctioning, and intervene in a timely manner. Gartner’s latest forecast predicts 20.4 billion things will be connected by 2020, which will completely change the way we work in several sectors. In the meantime, machine-to-machine communication is fast becoming a reality, and the Internet of Things (IoT) represents only the first step in that process. In this post, I look at four ways in which managers in the manufacturing industry can exploit IoT solutions for commercial benefits:

1. Understand how your company can profit from IoT solutions

Industrial standards for IoT are still not clear, in part due to the wide range of flexible solutions that can be developed in any industry. Factory and operations managers need to define a series of objectives to understand how best they can benefit from connected devices. This is because while it is relatively easy to collect data, it is difficult to understand how to cluster it to avoid complexity in analysis. It is key to define first what type of data can be useful to increase efficiency, rather than trying to analyse huge amounts of data which is not necessarily relevant. By honing in on the aspects which will make the most difference to the business, in terms of profit, subsequent analysis becomes much simpler.

2. Clarify your security strategy

Security is a fundamental aspect to take into consideration when adopting any IIoT (The Industrial Internet of Things) solution. Consumers still hesitate when purchasing IoT products, in part due to safety and privacy concerns. Similarly, manufacturers are hesitant to adopt IoT solutions since data and app platforms can be subject to cyber-attacks as well. Operational processes and risks need to be coordinated around these safety issues, a topic Atos explored in their Journey 2020 report.

The IIoT is fundamentally changing the cyber-security landscape; the old logic of go-to-market quickly to gain market share over competitors does not apply anymore. Any device connected to the internet can become a weapon for hackers, and IT companies need to effectively secure their infrastructure before delivering to clients. Having real-time security analytics and a cyber-resilient system are essential when deploying IIoT solutions to protect against any potential attack.

3. Consider sustainability

Sooner rather than later, companies need to consider how they will ensure that their IoT solutions remains sustainable i.e. future proof. Their IoT strategy needs to define, among other things, what type and the expected amount of data they need and how to manage it effectively to minimize potential negative impacts. It is also crucial to understand how to manage potentially millions of connected devices, and how to build a scalable and reliable, distributed computing environment around the production factory.

Sustainability in the sense of Corporate Social Responsibility (CSR) also offers opportunities. It is currently one of the hottest topics in the manufacturing industry. The adoption of sustainable strategies is something many manufacturers are beginning to take into consideration, since brand reputation depends on companies working to reduce the level of emissions and waste they generate.

IIoT will enable the creation of what researchers have termed a ‘circular economy’; the concept that puts re-usability and recyclability at the center of any type of process. Through IIoT solutions, managers will be able to extend the lifespan of machinery, thus cutting energy costs. Therefore, the development of smart-factories would likely result in a more ecological manufacturing industry, thus drastically reducing the impact of industrial processes in the environment.

4. Get out of your comfort zone

The industry is still in an early stage when it comes to IIoT adoption, but some pioneers are taking steps to ensure they are future leaders of the industry 4.0 era. The opportunity is out there, and decision-makers need to act rapidly to advance in this next wave of technology change.

It is fundamental to assess your own capabilities and role within the IoT ecosystem: will you push data or will you pull data? In a push model, you need to look at the smartness of your devices and data platform. If you pull data you need to look at your data analytics capabilities so you know when to ask for data and what data you need. Companies cannot bear the risk connected to data management for the entire production chain, therefore, it is necessary to build a partner ecosystem of buyers and vendors that co-operate for creating secured, efficient and scalable end-to-end solutions, leading to real added value in the production chain.