Enterprise Digital Transformation

Technology, Tools, and Use Cases

Peter Augustine, Pethuru Raj, Sathyan Munirathinam, Sathyan Munirathinam, Peter Augustine, Pethuru Raj, Sathyan Munirathinam, Peter Augustine, Pethuru Raj

• Publisher: Auerbach Publications
• Year: 2022
• eBook ISBN: 9781000540543
• Edition: 1
• Topic: Computer Science
• Subtopic: Computer Networking
• Index: Computer Science

Digital transformation (DT) has become a buzzword. Every industry segment across the globe is consciously jumping toward digital innovation and disruption to get ahead of their competitors. In other words, every aspect of running a business is being digitally empowered to reap all the benefits of the digital paradigm. All kinds of digitally enabled businesses across the globe are intrinsically capable of achieving bigger and better things for their constituents. Their consumers, clients, and customers will realize immense benefits with real digital transformation initiatives and implementations. The much-awaited business transformation can be easily and elegantly accomplished with a workable and winnable digital transformation strategy, plan, and execution.

There are several enablers and accelerators for realizing the much-discussed digital transformation. There are a lot of digitization and digitalization technologies available to streamline and speed up the process of the required transformation. Industrial Internet of Things (IIoT) technologies in close association with decisive advancements in the artificial intelligence (AI) space can bring forth the desired transitions. The other prominent and dominant technologies toward forming digital organizations include cloud IT, edge/fog computing, real-time data analytics platforms, blockchain technology, digital twin paradigm, virtual and augmented reality (VR/AR) techniques, enterprise mobility, and 5G communication. These technological innovations are intrinsically competent and versatile enough to fulfill the varying requirements for establishing and sustaining digital enterprises.

Enterprise Digital Transformation: Technology, Tools, and Use Cases features chapters on the evolving aspects of digital transformation and intelligence. It covers the unique competencies of digitally transformed enterprises, IIoT use cases, and applications. It explains promising technological solutions widely associated with digital innovation and disruption. The book focuses on setting up and sustaining smart factories that are fulfilling the Industry 4.0 vision that is realized through the IIoT and allied technologies.

Chapter 1 Get Technology to Contribute to Business Strategy

Pradeep Henry

GoodScore Labs, Chennai, India

DOI: 10.1201/9781003119784-1

Contents

1. Transformation Is a Strategic Initiative
2. To Transform an Enterprise, You Need More Than Tech
   • Tech-Only Is a Risk
   • Tech Chosen without a “Choosing” Process Is a Risk
   • Tech Strategy Is a Risk
   • Operational and Outcome Risks
     • Broken Process
3. Strategy-Driven Discovery-and-Design Process
   • Corporate Strategy
     • Step 1: Discover Domain
     • Step 2: Transform Domain
     • Step 3: Design Assets
   • Predicted Outcomes
4. How to Discover the Right Tech
   • Discover Tech in the Business Context
   • Discover While Exploring Four Things
5. How to Design It Right
   • Design Tech in the Business Context
   • Design Approach
   • Designing the Encapsulated Processes
   • Designing the User Interface
6. Getting Your Team to Make a Strategic Contribution
   • Individual Contribution Is Important
   • Potentially Chaotic Team
   • How to Ensure Collaboration
7. Managing Transformation Outcomes
8. References

Transformation Is a Strategic Initiative

Transformation is key to an organization’s ability to grow and prosper. The C-suite would consider a transformation initiative to be a success if the initiative eventually generates strategic outcomes. We’ll soon describe strategic outcomes, but in the meantime, here’s a quick definition: customer value and financial performance that align with an organization’s corporate strategy. Given the organization’s situation and targeted outcomes, transformation initiatives are strategic initiatives that should contribute – often in direct and measurable ways – to corporate strategy.

A disturbing percentage of transformation initiatives fail to meet the C-suite expectation. Depending on the data source, between 70% and 85% of transformation initiatives fail. Failure could be due to one or multiple reasons. Most reasons covered in the literature are what we might call “soft” reasons such as lack of employee buy-in. However, even if all of the soft issues were addressed, the more fundamental “hard” issues, such as implementing a wrong tech asset, will still fail the initiatives. Before investing in implementation, the organization needs to be sure it is transforming using the right blend of business and tech innovations.

Transformation is not something new. Organizations have always transformed at some point in their existence. Traditionally, transformations were viewed and executed as business process redesign (BPR). Although a process view is obviously required while transforming processes, BPR is not the best approach for transforming an organization, given that today’s technologies have the capability to play key roles. On the other extreme end, the power of technology has pushed organizations to think that they can transform by merely using a tech-centric approach and still get strategic business outcomes.

This chapter points out the issues with a tech-centric approach and then provides a process (method) to discover and design the right blend of business and tech assets before organizations invest in implementation.

To Transform an Enterprise, You Need More Than Tech

Transformation initiatives fail for many reasons. Here are three common reasons:

• Implementing an isolated tech asset
• Choosing a tech asset without using a “choosing” process
• Using a tech strategy to drive the initiative

These reasons are common due to a tech-centric approach to transformation. Let’s look at how they cause transformations to fail.

Tech-Only Is a Risk

Implementing an isolated tech asset may not transform the organization and generate strategic outcomes. Talking about IT and business change, Joe Peppard [1] explains,

What also seems to have been forgotten are the lessons from these earlier attempts to leverage IT. Unfortunately, the history of IT investments in most organizations is far from stellar: Research over the years suggests that the overall failure rate of IT projects is around 70%. We know that when IT projects fail, it is usually not because the technology didn’t work (although this can sometimes be the case), but because the changes required at an organizational and employee level weren’t managed effectively. Quite simply, adding technology does not automatically confer expected benefits; these benefits have to be unlocked and this can only happen through achieving organizational changes.

Technology plays an increasingly bigger role at organizations in many industries. But, even in an organization that wants to create and deliver customer value mostly through technology, a blend of business and tech assets will be required. Almost always, it is a blend that makes a strategic contribution. Unfortunately, what we see more often is organizations executing siloed technology projects.

Tech Chosen without a “Choosing” Process Is a Risk

Take the software development world for example. Collecting requirements is the task that is considered the discovery task. Even a software practice guideline, such as “Develop the right software,” suggests getting the right set of requirements. Having the right requirements is of course important, but discovery should be firstly and largely about discovering the right software. Unfortunately, the literature doesn’t offer any robust method for discovering the right tech asset. Also, with the large number of shiny new objects in today’s digital space, there is an increased probability that we choose the wrong tech assets. Organizations often end up selecting tech assets that can’t contribute to corporate goals. With the wrong tech assets, they launch the wrong initiatives.

Tech assets are often suggested by a vendor, consultant, or employee. Suggestions are often triggered by one of the following.

• The technology is hot: What’s trending need not be the one that the organization needs. The problem with hot technologies is that there could be hype and poor understanding about situations where they really help.
• The competition has that technology: Some organizations are competition-obsessed. The problem is: what matters to competition need not matter to the organization’s situation at the time. Competition obsession could also stifle purposeful innovation.
• The technology solves reported problems: The suggestion for a tech asset may come from employees. This is good. However, the suggestion could be siloed. A suggestion from a business unit may meet the functional goals of that unit, but fail to execute the corporate strategy.
• The technology solves standard problems: Often, organizations make a decision to invest in a tech asset based on standard expectations from the functional category to which it belongs or even merely based on standard benefits expected from “automation.”
• The technology is in the portfolio: The organization might pick a tech asset from a so-called “Strategic IT Portfolio.” This approach sounds impressive, but picking from a pre-prepared list may not deliver strategic outcomes.

So, selecting tech assets has been a mere decision-making activity rather than a methodical activity. “Organizations need to ensure that problems and opportunities that have not been properly diagnosed do not become projects – consuming time, money, and other resources – without reasonable confidence that they are valid means of achieving strategic objectives.” – Andrew MacLennan [2]. Tech assets should be discovered (or validated if a suggestion exists) using a discovery method that considers factors such as the organization’s current corporate strategy.

Tech Strategy Is a Risk

Nigel Fenwick [3] says “The truth is that your company doesn’t need a digital strategy.” Fenwick goes on to say “You could spend the next five years digitizing your entire business. Many tech vendors will fall over themselves helping you to do just that if you let them. Don’t!” Why shouldn’t a digital strategy or IT strategy drive your transformation initiative? The reason is that they’re potentially siloed and not aligned with the corporate strategy. The tech strategy will likely have the same silo issue that we see in departments within organizations. “I’ve done some work with a few large corporates in which I compared the corporate strategy with their IT function’s strategy – and the match was woeful.” – Terry White [4], CXO Advisor. Transformation initiatives that are driven by a “siloed” strategy hurt the organization.

SAS VP Jill Dyché [5]:

The reality of business siloes means business units have launched their own digital projects. Marketing might be experimenting with real-time product recommendations pushed to customers’ smart devices, while manufacturing might be introducing sensors into the supply chain. Each business unit considers its challenges unique. Functional executives pay little heed to the connection, data integration, and analytics technologies that will ultimately be necessary to optimize these efforts.

The organization will therefore have poorly integrated processes and offer poor customer experience. Sure enough, sooner than later, the organization will have to start another project that gets the pieces to work together.

Tech strategy may not be an authentic input for a transformation initiative for the following reasons:

• Tech strategy may be outdated: The Tech strategy may not accurately reflect the organization’s current corporate strategy. This is highly likely because many organizations today use a “transient strategy” approach, where strategies are more dynamic (The End of Competitive Advantage: How to Keep Your Strategy Moving as Fast as Your Business, Rita McGrath [6]).
• Tech strategy cannot suggest the right portfolio: The so-called strategic IT portfolios are often premature in prescribing a list of tech assets. Such prescribed tech assets are unlikely to be integrated with business.
• Tech strategy may not offer directions for business innovation and change: Forrester Research founder George Colony used the phrase “naked technology” to characterize technology with no business innovation.
• Tech strategy may be self-serving: “Self-serving” need not always be bad; it could deliver tech department level benefits. However, such benefits are more likely functional or standard than strategic.

Transformation requires the most authentic inputs – your organization’s corporate strategy.

Operational and Outcome Risks

• Poor adoption: The design of tech assets used to be notoriously poor from a human use perspective, but thankfully human factors are increasingly used in the design. Business factors though are still not systematically used in the design. Where there are poorly designed tech assets, users may find some workarounds or even refuse to use them.
• Recurrence: Recurrence is the situation where one or more old business problems exist even after transformation. Recurrence may be due to a wrong tech asset, that is, the absence of a solution to the problem. Recurrence may be due to simply “automating” a process that already had problems. The accounting process at a restaurant chain remains the same-old after spending money on an accounting software; the existing accounting process is simply embedded in the new software and so if there were problems, those problems remain. A great opportunity to improve the organization was available, but it was squandered.
• Degradation: While deploying new tech assets, changes may be required to one or more connected assets to get everything to work together as one. Such changes are often not foreseen or addressed due to a tech-centric approach that lacks a holistic view. Result: Things such as customer experience may actually get worse instead of better.
  • Here are a few symptoms of degradation: Different employees give different answers to the same question; different business processes complete the same work, but with different software; decisions by different departments are not coordinated; data are everywhere in the organization, but the information required to make decisions is not easily available.
  • Implementing and using a silo tech asset or a point solution is often the reason for degradation. Writing about software applications, Ross, Weill, and Robertson [7] describe the silo problem: “Individually, the applications work fine. Together, they hinde...

Table of contents

• Cover
• Half Title
• Title Page
• Copyright Page
• Contents
• Editors
• Contributors
• Chapter 1 Get Technology to Contribute to Business Strategy
• Chapter 2 Introduction to Computer Vision
• Chapter 3 Essentials of the Internet of Things (IoT)
• Chapter 4 The Internet of Things Architectures and Use Cases
• Chapter 5 Challenges of Introducing Artificial Intelligence (AI) in Industrial Settings
• Chapter 6 Blockchain-based Circular-Secure Encryption
• Chapter 7 Security Challenges and Attacks in MANET-IoT Systems
• Chapter 8 Machine and Deep Learning (ML/DL) Algorithms for Next-Generation Healthcare Applications
• Chapter 9 A Review of Neuromorphic Computing A Promising Approach for the IoT-Based Smart Manufacturing
• Chapter 10 Text Summarization for Automatic Grading of Descriptive Assignments A Hybrid Approach
• Chapter 11 Building Autonomous IIoT Networks Using Energy Harvesters
• Chapter 12 An Interactive TUDIG Application for Tumor Detection in MRI Brain Images Using Cascaded CNN with LBP Features
• Chapter 13 Virtual Reality in Medical Training, Patient Rehabilitation and Psychotherapy Applications and Future Trends
• Chapter 14 Complexity Measures of Machine Learning Algorithms for Anticipating the Success Rate of IVF Process
• Chapter 15 Commuter Traffic Congestion Control Evasion in IoT-Based VANET Environment
• Chapter 16 Dyad Deep Learning-Based Geometry and Color Attribute Codecs for 3D Airborne LiDAR Point Clouds
• Chapter 17 Digital Enterprise Software Productivity Metrics and Enhancing Their Business Impacts Using Machine Learning
• Index