How Important is Industry Experience when Selecting your Tech Vendor?

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Identifying and selecting a vendor for your tech project can be a daunting task – especially when it comes to emerging technologies or when implementing a tech solution for the first time. Organisations look for a certain degree of alignment with their tech vendors – in terms of products and pricing, sure, but also in terms of demonstrable areas of expertise and culture. Several factors are involved in the selection process – vendors’ ability to deliver, to match expected quality standards, to offer the best pricing, to follow the terms of the contract and so on. They are also evaluated based on favourable reviews from the tech buyer community.

Often businesses in a particular industry tend to have their unique challenges; for example, the Financial Services industries have their specific set of compliance laws which might need to be built into their CRM systems. Over the years, vendors have built on their industry expertise and have industry teams that can advise organisations on how their business requirements can be met through technology adoption. These experts speak in the language of the industry and understand their business and technology pain points. They are able to customise their product and service offerings to the needs of the industry for a single client – which can then be repeated for other businesses in that industry. Vendors arm themselves with a portfolio of industry use cases, especially when they are entering a new market – and this often gives them an upper hand at the evaluation stage. In the end, organisations want less customisations to keep the complexity and costs down.

Do organisations evaluate vendors on industry experience?

Ecosystm research finds that industry experience can be a significant vendor selection criterion for some tech areas (Figure 1), especially in emerging technologies such as AI. AI and automation applications and algorithms are considered to be distinctive to each industry. While a vendor may have the right certifications and a team of skilled professionals, there is no substitute for experience. With that in mind, a vendor with experience in building machine learning models for the Telecommunications industry might not be perceived as the right fit for a Utilities industry implementation.

Whereas, we find that cybersecurity is at the other end of the spectrum, and organisations perceive that industry expertise is not required as network, applications and data protection requirements are not considered unique to any industry.

Is that necessarily the right approach?

Yes and no. If we look at the history of the ERP solution, as an example, we find that it was initially meant for and deeply entrenched in Manufacturing organisations. In fact, the precursor to modern-day ERP is the Manufacturing Resource Planning (MRP II) software of the 1980s. Now, we primarily look at ERP as a cross-industry solution. Every business has taken lessons on inventory and supply chain management from the Manufacturing industry and has an enterprise-wide system. However, there are industries such as Hospitality and Healthcare that have their niche vendors who bundle in ERP features with their industry-specific solutions. This will be the general pattern that all tech solutions will follow: a) an industry use case will become popular; b) other industries will try to incorporate that solution, and in the process; c) create their own industry-specific customisations. It is important, therefore, for those who are evaluating emerging technologies to cast their net wide to identify use cases from other industries.

AI and automation is one such tech area where organisations should look to leverage cross-industry expertise. They should ask their vendors about their implementations in other allied industries and, in some cases, in industries that are not allied.

For cybersecurity, their approach should be entirely different. As companies move on from network security to more specific areas such as data security and emerging areas such as GRC communication, it will be important to evaluate industry experience. Data protection and compliance laws are often specific to industries – for example, while customer-focused industries are mandated on how to handle customer data, the Banking, Insurance, Healthcare and Public Sector industries have the need to store more sensitive data than other industries. They should look at solutions that have in-built checks and balances in place, incorporating their GRC requirements.

So, the answer to whether organisations should look for industry expertise in their vendors is that they should for more mature tech areas. An eCommerce company should look for industry experience when choosing a web hosting partner, but should look for experience in other industries such as Banking, when they are looking to invest in virtual assistants.

Are some industries more focused on industry experience than others?

Ecosystm research also sought to find out which industries look for industry expertise more than others (Figure 2). Surprisingly, there are no clear differences across industries. The Services, Healthcare and Public Sector industries emphasise marginally more on industry expertise – but the differences are almost negligible.

There are some differences when we look at specific tech areas, however. For example, industries that may be considered early adopters of IoT – Transportation, Manufacturing and Healthcare – tend to give more credit to industry experience because there are previous use cases that they can leverage. There are industries that are still formulating standards when it comes to IoT and they will be more open to evaluating vendors that have a successful solution for their requirement – irrespective of the industry.

The Healthcare Industry Example

Ecosystm Principal Analyst, Sash Mukherjee says, “In today’s fast-evolving technology market, it is important to go beyond use cases in only your industries and look for vendors that have a demonstrated history of innovation and experience in delivering measurable results, irrespective of the industry.” Mukherjee takes the example of the Healthcare industry. “No one vendor can provide the entire gamut of functionalities required for patient lifecycle management.  In spite of recent trends of multi-capability vendors, hospitals need multiple vendors for the hospital information systems (HIS), ERP, HR systems, document management systems, auxiliary department systems and so on. For some areas such as electronic health records (EHR) systems, obviously industry expertise is paramount. However, if healthcare organisations continue to look for industry expertise and partner with the same vendors, they miss out on important learnings from other industries.”

Talking about industries that have influenced and will influence the Healthcare industry in the very near future, Mukherjee says, “Healthcare providers have learnt a lot from the Manufacturing industry – and several organisations have evaluated and implemented Lean Healthcare and Six Sigma to improve clinical outcomes. The industry has also learnt from the Retail and Hospitality industries on how to be customer focused. In the Top 5 Healthtech trends for 2020, I had pointed out the similarities between the Financial and Healthcare industries (stringent regulations, process-based legacy systems and so on). As the Healthcare industry focuses on value-based outcomes, governments introduce more regulations around accountability and transparency, and people expect the experience that they get out of their retail interactions, Healthtech start-ups will become as mainstream as Fintech start-ups.”

 

It is time for tech buyers to re-evaluate whether they are restricting themselves by looking at industry use cases, especially for emerging technologies. While less industry customisations mean easier deployments, it may also hamper innovation.

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Ecosystm Analyses: Differences in Tech Priorities between Product-focused and Customer-focused Industries

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Traditional industry practices tend to divide industries into two distinct buckets – firms that are primarily focused on product design and improvement, and those that define their strategy based on customer services. Over the years, the lines distinguishing these organisations have all but disappeared. To be able to succeed in today’s competitive world, you need to continually improve your product offering – even for organisations in industries such as Manufacturing and Wholesale – and the best way to do so is to keep a firm eye on your customers. Likewise, unless you have a robust product, you will not be able to retain customers. As an example, online reviews are often critical of budget airlines, but the successful ones manage to hold on to their loyal customers doing what they set out to do – by not offering the best airline food service but by continuing to provide affordable airfares to places where their customers want to go.  The Ecosystm CX study finds that even the most product-focused industries today, have improving customer experience (CX) as a key business priority (Figure 1).  The two groups of industries tend to have similar priorities – the only major difference being customer-focused industries invest in more initiatives to promote customer loyalty.

 

In 2016, Caterpillar showed the way forward to industries that have primarily been product-focused. They started investing in technology that is not just focused on solving, but actually predicting customer issues to improve service. Even industries such as Agriculture are increasingly becoming customer-focused, as more citizens become conscious of where and how their food has been produced. Freight Farms is a good example of customer-centricity in the industry – focusing on technology to grow food in environments not considered conducive to farming such as urban localities and places with extreme climates.

 

Investing in the Right Technologies

Looking at the Top 5 CX trends for 2020, we find that technologies such as Cloud and AI, and solutions such as robust knowledge management are true enablers of positive CX. So how do these two groups differ when it comes to investments in these technology areas? Customer-focused industries are slightly more enthusiastic about their Cloud investments, but only marginally (Figure 2). Obviously, they invest more in knowledge management solutions, both for CX as well as improved employee experience (EX). But surprisingly, product-focused industries also tend to invest in knowledge management, for several reasons ranging from product improvement to after-sales support.

 

Where product-focused industries really lead is in their investments in AI/Analytics – which ties in with our observation that automation is the stepping stone for AI investments across industries. The applications of AI/Analytics are very distinct for the two groups (Figure 3). Product-based industries focus on automation and optimisation and have a clear asset focus. However, it is heartening to see some customer-centric solutions such as market segmentation. On the other hand, the top AI/Analytics application for customer-focused industries is billing management, which might significantly improve CX but falls under the purview of Finance & Operations in most organisations.

 

Securing Data and Building Trust

No organisation can ignore the seriousness of data breaches – whether customer data or intellectual property. Public cloud is going to be the true enabler of Digital Transformation (DX), from both cost and agility angles. Security has always been a key concern around public cloud adoption, even though organisations would mostly benefit from the robust and evolving security features of public cloud providers rather than having a go at securing their systems and data in-house and on-premises. The perception on public cloud security has changed over the years (Figure 4), but customer-focused industries appear to be savvier about the shared responsibility SLAs most public cloud providers have in place.

 

Which brings us to another important question – how much sensitive data do these organisations store on public cloud (Figure 5). Probably because they hold more customer data and must follow industry and country compliance laws that mandate how customer data should be stored and accessed, nearly a third of customer-focused organisations store sensitive data on-premises only. While their cloud adoption may be slightly higher than product-based industries, they are also more wary of storing sensitive data on the public cloud.

 

The differences in strategies between customer-focused and product-focused industries might have blurred over the past decade – both groups focusing on customer-centric products. Their technology priorities are still clearly distinct, however. It is important to bear this difference in mind – both for tech buyers who are looking at use cases across all industries when it comes to emerging technology adoption; as well as for tech vendors who now have to engage with stakeholders beyond the IT department.

 


NB: For the purpose of this blog, industries have been classified as follows: Product-focused Industries – Energy & Utilities, Manufacturing-based industries, Resource & Primary industries, Transport & Logistics, Wholesale and Construction; Customer-focused Industries – Banking & Financial industries, Retail & eCommerce, Healthcare, Government, Professional Services, Media & Telecommunications


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5 recommendations to accelerate implementation of IIoT Edge computing solutions in Manufacturing

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Is IIoT Edge Computing solution a real Internet of Things (IoT) trend for 2019?

As large hardware manufacturers like Cisco, HPE, Dell and more are building specific, robust and secure infrastructure for the edge, it is believed that there will be a lot of money flowing in the IIoT Edge computing world.

The Development and implementation of Edge-Machine Learning solutions is a complex process and requires a combination of rich industry experience, knowledge of automation (PLCs, SCADAS, HMIs), electrical & mechanical engineering along with unique Edge Computing distributed system. This is used by Data Scientists to develop Machine Learning algorithms which can be utilised by IIoT applications in the manufacturing industry.

For organisations looking to implement these solutions, it is always a good idea to know more on adoption and ask for the continuation of a pilot project for more than a year.

Below are the top 5 things that one should follow to accelerate implementation of IIoT edge computing solutions in the Manufacturing industry –

1)    Get help to find the needle in the haystack

With the fragmented ecosystem of IIoT vendors and companies talking about the Industrial Internet or Industry 4.0, the challenge that always appears in front of the customers is to ask for free pilots from the manufacturers.

It is not just finding the needle (IIoT best or cheaper solution) in the haystack (ecosystem), it is how this needle matches with your business and technology strategies.

I know, I am selling myself, but my recommendation to you is to get advice from independent IIoT experts.

2)    Avoid OT Vendor Lock-In: We need machine data availability

Powerful Edge Analytics-Machine Learning applications require data exchange with the Programmable Logic Controllers (PLCs) of the manufacturers. By looking at the specifications we may think that it will be an easy task to extract the data from PLCs going through different ways or manufacturer’s help-guides. However, the problem is vendor lock-ins, most of the top PLC manufacturer’s do not allow “easy” data access and extraction methods neither to the customers nor to any third parties.

It is not a question of protocols, it is a question of vendor lock-in and data availability.

Customers must seek and claim for open-source solutions to avoid vendor lock-in during the long run. The open source can better lead to the path of innovation in their manufacturing plants.

3)    Edge Computing and Machine Learning: The last frontier to break between IT/OT

In my article “IT and OT, Friends or Foes in the Industrial Internet of Things?” I was optimistic about the quick convergence of Information Technology (IT) and Operations Technology (OT), I was wrong. If you visit and inspect a manufacturing plant floor, you will see how much progress is still to be made.

Edge Analytics is a key component in the integration of IT & OT and requires a knowledge of both to make it work. The lack of skills & knowledge in the IT and OT fields impact the business & operations and creates a dilemma on which department should lead the Edge Analytics projects.

Manufacturing companies need a role with authority (Chief IIoT Officer or CIIoT) and resources to lead the IT/OT convergence strategy.

4)    Do not stop by the dilemma of Edge: To Cloud or NOT to Cloud

When I wrote in 2016 “Do not let the fog hide the clouds in the Internet of Things”, the hype around Edge Computing and Machine Learning started. There was a confusion about fog computing and edge computing and how this layer will impact the IoT architecture, especially cloud workloads.

Today, many cloud vendors offer IoT platforms and tools that combine the Cloud and the Edge application development, machine learning and analytics at the edge, governance, and end to end security. On the OT side, companies like Siemens have launched MindSphere, an open cloud-based IoT operating system based on the SAP HANA cloud platform.

Manufacturers should continue to deploy and develop Edge Computing – Machine Learning applications to monitor the health of their machines or to improve their asset maintenance or to monitor the quality control of their plant floor processes and shouldn’t stop because of the fear of the integration of their platform with the Public or Hybrid Cloud environment.

Edge Computing solutions help manufacturers to improve their competitiveness without the Clouds but make sure your Edge IIoT solution is ready for easy integration with the Clouds.

 5)    Connected Machines is the only way for new Business Models

Security is another major obstacle for the adoption of IIoT in the manufacturing industry. Manufacturers have been reluctant to open their manufacturing facilities to the Internet because of the risks of cyber-attacks.

In a fast-moving era where platforms and services require products and machines connected, every manufacturing factory should be able to tap into machine data remotely and make it available for Machine vendors. This requires every Edge Computing / Machine Learning system to be built with the capability to share data remotely via open and secure protocols/standards like MTConnect and OPC-UA.

Having machines connected is the first step to make machines smarter, to build smarter factories and to flourish new business models as Remote Equipment Monitoring.

Key Takeaway

The benefits of using Edge Computing / Machine Learning solutions are very attractive to the manufacturers because it offers minimal latency, conserve network bandwidth, improve operations reliability, offers quick decision-making ability, gather data, and process the collected data to gain insights. The ROI in such IIoT solutions is very attractive.

To get these benefits and to grace IIoT journey, manufacturers have to step-up and accept to receive tangible and innovative business value.

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