• Leadership & investment

• Skills, knowledge & collaboration

• Customer engagement

• Workforce effectiveness

• Governance

• Data management

• Analytics methods

• Deployment and use

• Connected assets

• Distributed intelligence

• Security

• Management processes

People and culture

Analytics, data and technology

Operational infrastructure

Take the next steps to IIoT digital transformation | 1

Take the next steps to IIoT digital transformation Steps and recommendations to accelerate industrial digital transformation.

You’ve completed the ARC Industrial Analytics Assessment. Next, you’ll want to discover how to accelerate your progress and create a competitive edge by applying AI to your IoT initiatives. The great news is that wherever you are on the maturity continuum, there’s always a best practice you can adopt to take you further. Read on to find recommendations for your organization.

What’s the secret to industrial success in the future?Understanding how to drive value with IIoT transformation…

Don’t be fooled, digital transformation takes more than technology deployment. In fact, truly moving your business forward requires change across three components of industrial operations:

1 2 3

IMMATURE MATURE

Take the next steps to IIoT digital transformation | 2

RecommendationsYour three scores highlight where your organization has gaps and opportunities, organized around people & culture, analytics, data & technology and operational infrastructure.

Find your recommendations here

They’re specifically organized by these three groups to help you take fast, relevant actions that accelerate your progress.

About the scores

Group score 4-6 (Low): you’re just starting out, so you will likely find most, if not all, recommendations useful.

Group score 7-9 (Medium): you’re progressing, so you will likely have implemented a few recommendations while others will be logical next steps.

Group score 10-12 (High): you’re an industrial analytics leader! ARC has never seen an organization scoring higher than 10.

Where to begin?Interpreting your score

IIoT analytics maturity is a three-stage continuum:

Moving along the maturity scale requires you to progress in each of these three components of industrial operations.

Where are you now? When you completed the assessment, you received a score for each of these components. Together they tell you where you are on the continuum.

Discover and inform Identify and transition Transformed

IIoT Analytics Maturity Stages

Discover and inform

Infrastructure Control automation

Analytics Dashboards

Workforce Tribal knowledge

Business models Siloed

Identify and transition

Infrastructure Becoming connected

Analytics Some distributed

intelligence

Workforce Acquiring digital skills

Business models Collaborative

Transformed

Infrastructure Smart and connected

Analytics Distributed intelligence

Workforce Blended skills

Business models Customer first

Industrial Analytics Assessment score: 4-6

Industrial Analytics Assessment score: 7-9

Industrial Analytics Assessment score: 10-12

Take the next steps to IIoT digital transformation | 3

RECOMMENDATIONS

People and culture

1. Find a manageable starting point

Many immature organizations struggle to identify what is a sensible starting point. ARC believes it’s where data is available, yet problems recur unsolved. This typically means key, root cause data points are missing, which can be captured via IIoT, and/or an ineffective analysis applied. This will help you improve: leadership and investment, skills, knowledge, and collaboration.

2. Target areas where hidden costs may exist

These include supply chains, suppliers, and customer demand management. Costs are unknowingly passed to customers, reducing your ability to optimize value. Analytics, particularly machine learning, can help, as the outcomes will improve your ability to compete. These hidden costs can be identified in well-known operational processes, offering a way to prove return on investment. This will help you improve: leadership and investment, and customer engagement.

3. Rethink the ‘why’ when it comes to scaling and ROI issues

This is an acute problem at the medium maturity level. Issues with scale and ROI indicate two key missteps. First, when the organization is too technology-centric during planning and requirements gathering. Secondly, when the organization is not committed to experimenting in order to understand what needs to be learned to execute IIoT analytics at speed and scale. This will help you improve: skills, knowledge and collaboration.

4. Integrate people throughout the process

Include workforce issues in your digital transformation roadmap. Provide clear expectations as to what roles look like after change occurs and how those individuals involved contribute value. This will help you improve: workforce effectiveness.

5. Share success and capture how it was achieved

Success stories are the foundation of organizational knowledge and will help win executive buy-in. They’ll show your peers that improvement is possible, while enabling them to learn how to avoid mistakes. This will help you improve: workforce effectiveness.

Many challenges with people and culture are rooted in the industrial tradition of transactional, risk-averse decision making, as well as the range of comfort/discomfort levels with digital change across the workforce. You can’t change this overnight, but you can take these steps:Category Definition

Leadership & investment

Senior executive attitudes toward digital transformation, including their willingness to invest in it.

Skills, knowledge & collaboration

The individual and group capacity of the workforce to support digital transformation.

Customer engagement

How customer satisfaction and needs influence operational processes and performance.

Workforce effectiveness

The way performance is measured and how success is achieved.

Take the next steps to IIoT digital transformation | 4

1. Modernize IT and data philosophies by revisiting governance policy

Design data management so that it can move away from replication to governing principles that facilitate broad use of data by many organizational personas. This will help you improve: governance and data management.

2. Value flexibility in technology

For low or medium maturity, the complexity of IIoT ecosystems requires that analytics solutions support myriad uses cases, disparate user requirements, agnostic interoperability, capacity to manage data at rest and in motion, and a breadth of analytics methods. This will help you improve: analytics methods, deployment and use.

3. Have a model management strategy

Don’t overlook the valuable IP in your existing models. Use a modern analytics engine to speed and improve model insights, providing an initial comfort level. As you deploy analytics, match model use with operational requirements; you may need technology that can support model use in the cloud, at the edge, across networks (fog), on-premises, or a combination.

As you operationalize and build analytics, leverage model management tools that will help you accumulate organizational analytics wisdom. This will ensure you utilize best practices, manage models, and have a means to retrain, reuse, and discard them as needed. This will help you improve: governance, analytics methods, deployment and use.

4. Think ‘productization’ for deployment

Both low and medium maturity companies need to ease the adoption and deployment. Try to mimic some of the techniques that vendors offer to overcome adoption barriers. For instance, create and market process packages internally for high-value, proven use cases for quick, sustainable wins. Doing so will reduce disruption and learning curves, while accelerating transformation. This will help you improve: deployment and use.

5. Know speed is the goal technology needs to support

Make decisions about technology based on speed and automate its value. You’ll be able to differentiate from your competitors based on speed of responsiveness to customer demand. This will help you improve: analytics methods, deployment and use.

Many organizations make the mistake of thinking IIoT and analytics are technological pursuits. They measure progress by the ability to discover, implement, and use some singular, ‘correct’ approach with analytics.

Instead, work backward from the customer problem to understand what the company needs to do with its resources, including technology and workers’ comfort level with it. This outside-in approach helps organizations orient around the reality that it will need to dynamically evolve technology capabilities to answer customer needs. If your maturity is low or medium here, you are likely struggling with understanding the technological landscape and/or scaling its use. ARC recommends:

Category Definition

Governance

Organizational rules that determine access to, and use of, data by people and systems.

Data management

Processes for generating, acquiring, and consuming data from internal and external sources.

Analytics methods

Adoption of techniques and tools used to produce insight from data.

Deployment & use

How software is installed and used across an organization’s operations.

RECOMMENDATIONS

Analytics, data and technology

Take the next steps to IIoT digital transformation | 5

1. Inventory your systems and associated data and infrastructure and identify knowledge gaps

Stable, sustainable change isn’t possible without this inventory. This will help you improve: distributed intelligence.

2. Explore the value and tradeoffs of OEM and vendor options for remote monitoring and field services

Take advantage of price and service options that allow investment to match scale, service level needs and data usage.

This is helpful if you score low on maturity. Packaged hardware/software/services solutions are abundant, require little-to-no heavy lifting with existing data, and can be done with little risk. This will help you improve: connected assets; distributed intelligence, security, management processes.

3. Review the ability of enterprise systems to connect to and work with IIoT devices and solutions

This is for companies that have made some forays into IIoT, but haven’t been able to scale. Consider deploying cloud and SaaS versions to reduce the cost and complexity of integrating key enterprise functions and data into IIoT and analytics. This will help you improve: connected assets and distributed intelligence.

4. Transition from building stronger cyber silos to developing broad-based IT-OT security networks

For maturing companies wishing to accelerate success, this will enable you to think about extending IIoT and analytics to processes and data outside its organization, including external systems, data, and remote devices. This will help you improve: distributed intelligence, security, management processes.

It isn’t realistic to overhaul infrastructure to make an organization ‘intelligent’. Quick wins can be achieved with packaged solutions, some that combine hardware with embedded and/or cloud-connected analytics software.

From a maturity standpoint, the challenge is mainly about scaling efforts across additional infrastructure, asset classes, fleets, etc. into a holistic strategy. Doing so requires an operational plan for how to orient infrastructure goals to external demands, particularly those of customers that will ask you to operate in more agile, distributed, and customized ways. If you are at low or medium maturity, ARC recommends:

Category Definition

Connected assetsThe capacity of assets, devices, and equipment to receive and transmit data.

Distributed intelligence

Visibility to infrastructure network and health and capacity to dynamically manage performance of distributed assets.

Security

Capacity to secure physical/cyber systems, networks, and devices down to the endpoint.

Management processes

How the asset lifecycle and its related processes, such as maintenance, are managed.

RECOMMENDATIONS

Operational infrastructure

Next stepsAnalytics and IIoT can provide a transformational pathway to success. Failure to transform is likely to undermine the future viability of the organization. Wherever you are on the maturity curve, SAS can help you operationalize analytics across your enterprise so you can realize the outcomes and ROI that will drive your business and operational performance forward.

We recommend pursuing scalable, sustainable and secure digital transformation. To be successful, you need a framework to provide direction that accounts for the state of the organization and how to grow its capacity to support analytics and IIoT. This will help you define, plan, and execute initiatives that generate specific outcomes and investment return, while also supporting the larger business goals achieved via cohesive digital transformation.

Reach out to SAS The powerful combination of AI + IoT – also known as the Artificial Intelligence of Things (AIoT) is transforming organizations globally and across industries. Read more about gaining true value from IoT data through advanced analytics and artificial intelligence (AI)at www.sas.com/aiot.

SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. ® indicates USA registration. Other brand and product names are trademarks of their respective companies. Copyright © 2019, SAS Institute Inc. All rights reserved.

www.arcweb.com Founded in 1986, ARC Advisory Group is the leading technology research and advisory firm for industry, infrastructure, and cities. ARC stands apart due to our in-depth coverage of information technologies (IT), operational technologies (OT), engineering technologies (ET), and associated business trends. Our analysts and consultants have the industry knowledge and first-hand experience to help our clients find the best answers to the complex business issues facing organizations today. We provide technology supplier clients with strategic market research and help end user clients develop appropriate adoption strategies and evaluate and select the best technology solutions for their needs.

All information in this report is proprietary to and copyrighted by ARC. No part of it may be reproduced without prior permission from ARC. This research has been sponsored in part by SAS. However, the opinions expressed by ARC in this paper are based on ARC’s independent analysis.