In the fast-evolving landscape of data and artificial intelligence (AI), staying ahead of the curve requires more than just tools—it demands strategic insights and actionable intelligence. That’s why we’ve launched the 2024 CXO Insights Guide on Data & AI, a comprehensive resource packed with the latest research, industry trends, and real-world solutions to today’s most pressing data challenges. Whether you’re managing a data warehouse, exploring cloud analytics, or leveraging a data lakehouse, this guide will provide the knowledge you need to elevate your data strategy.

Unpacking the Big Picture: Key Findings from Our Analytics Research

The guide draws on exclusive insights from a survey of 237 senior decision-makers in IT, data, and product development roles across industries, including financial services, healthcare, and manufacturing. This research reveals that while organizations are eager to harness the power of AI, significant hurdles stand in the way—such as data silos, unstructured data, real-time analytics delays, and security concerns. Here’s a sneak peek into some of the most compelling findings:

Data Silos and Unstructured Data Are Holding Businesses Back

Data integration remains one of the most persistent challenges, with 42% of respondents struggling to combine data from various systems. This fragmentation not only hampers the ability to generate comprehensive insights but also leads to inefficiencies and delays in decision-making processes. Meanwhile, 65% of organizations admit they have no clear process for extracting insights from unstructured data, such as emails, multimedia, and text documents. In a world increasingly driven by AI and machine learning, this is a critical gap that must be addressed.

Real-time Analytics Are Essential, But Performance and Scalability Are Key

As businesses seek to make timely, data-driven decisions, real-time analytics capabilities have become non-negotiable. Yet, 26% of executives report frequent delays in accessing necessary data due to technical limitations and resource constraints. The ability to rapidly process large data volumes hinges on the performance and scalability of your data infrastructure, whether that’s a data warehouse, cloud analytics platform, or data lakehouse. Investing in high-performance technologies that minimize latency and can scale on demand is crucial to maintaining operational agility.

Data Security Concerns Limit AI Potential

Security and compliance continue to be top concerns, with 32% of respondents indicating that security restrictions limit access to their data. This is particularly troubling as businesses look to AI to unlock deeper insights and drive decision-making. Data breaches and compliance violations are not just costly—they can derail entire AI initiatives. Robust security measures, including regular audits and alignment with regulatory standards, are essential to protecting sensitive information and ensuring that AI investments deliver on their promise.

Data Quality Is the Foundation of Reliable Analytics and Reporting

Data quality is a critical concern, with nearly 72% of respondents highlighting challenges such as data inconsistencies, errors, and duplication. These issues undermine the reliability of analytics and AI models, leading to misinformed decisions and lost opportunities. Establishing comprehensive data governance frameworks, including automated data profiling and validation processes, can significantly enhance data quality and support more accurate analytics.

The Rise of Advanced Analytics, Visualization, and BI & Reporting

Organizations are increasingly leveraging advanced analytics, visualization, and BI & Reporting tools to transform raw data into actionable insights. According to our research, 80% of organizations use packaged BI tools, while others are diving into technologies like Python, JavaScript, and R for more customized solutions. However, integration challenges remain a key obstacle, with nearly half of decision-makers reporting difficulties in combining data from disparate sources. Scalable and user-friendly analytics tools that support real-time processing can help overcome these barriers and empower business users to interact with data independently.

Activate Your AI with the Power of Cloud Analytics and Data Lakehouse Solutions

The 2024 CXO Insights Guide on Data & AI doesn’t just highlight the challenges—it also provides actionable solutions that can help organizations unlock the full potential of their data. From leveraging cloud analytics platforms to the adoption of GenAI for data access democratization, here are some key strategies to consider:

Harness the Flexibility of Cloud Analytics

Cloud analytics platforms offer unmatched flexibility, allowing organizations to scale resources up or down as needed and access the latest analytics capabilities without the burden of managing on-premises infrastructure. By bringing together data lakehouse, visualization, discovery, advanced analytics, and machine learning capabilities into a single analytics solution, you can improve performance, enhance security, and streamline data integration across the business. OpenText Analytics Cloud, for example, provides a high-performance environment for real-time analytics, predictive modeling, and AI-driven insights—all backed by industry-leading security features.

Transform Your Data Strategy with a Data Lakehouse

The data lakehouse concept combines the best features of data warehouses and data lakes, offering a unified platform that supports structured, semi-structured, and unstructured data. This approach simplifies data management, enhances analytics capabilities, and provides a more cost-effective solution for organizations dealing with large data volumes. By breaking down data silos and enabling seamless integration, a data lakehouse can significantly accelerate your journey toward AI activation.

Optimize BI & Reporting for Better Decision-Making

Modern BI & Reporting tools are designed to empower business users with self-service capabilities, allowing them to create and share interactive dashboards, generate custom reports, and drill down into data without relying on IT support. OpenText Magellan BI & Reporting stands out with its scalable, cloud-based solution that integrates seamlessly with existing applications. This level of accessibility and interactivity fosters a data-driven culture and improves decision-making across the organization.

Prioritize Data Security to Safeguard AI Investments

With data privacy and security concerns at an all-time high, organizations must prioritize robust security measures to protect sensitive information and comply with regulatory requirements. Regular audits, advanced encryption, and centralized policy management are just some of the ways businesses can safeguard their data and maintain trust in their AI initiatives.

Why You Need to Read the 2024 CXO Insights Data & AI Guide

The 2024 CXO Insights Guide on Data & AI is a strategic playbook designed to help you navigate the complexities of today’s data landscape. By understanding the key trends and challenges identified in our analytics research, you can position your organization to overcome obstacles and capitalize on new opportunities. Whether you’re managing a data warehouse, exploring cloud analytics, or implementing a data lakehouse, this guide provides the insights and tools you need to activate AI and drive transformative business outcomes.

Ready to supercharge your data strategy? Download the 2024 CXO Insights Guide on Data & AI today and discover how to turn your data challenges into competitive advantages.

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In my previous blog, I looked at the explosive growth of digital twins. Estimates suggest the number of organizations using digital twins will triple by 2022. In this blog, I’m going to cover what you need to consider when creating a digital twin and the essential role of identity and access management (IAM) in addition to the core functions of an IoT platform to ensure successful delivery.

The concept of the digital twin is made possible by the advances in Internet of Things (IoT) technology. It is now straightforward to attach sensors and actuators to a physical object– such as a part within an asset or the entire asset itself – to capture contextual and operational data and control the object from its digital twin.

This is often characterized as a bi-directional flow of data from the physical to the digital and back. But, that’s telling half the story. Every digital twin will involve multiple threads of information. This is not simply between the physical object and its twin, but also between the twin and the enterprise systems – such as CAD, ERP, MES, PLM– that help create the initial model and supply supporting data, in real time or near real time, to build a complete picture of the object or asset.

In addition, data flows between the asset, its digital twin and everyone that needs access to the twin to view, manage or manipulate the data it holds. The more advanced analytics and simulations you perform through the digital twin, the more people will be involved in the process.

Why identity comes first when creating a digital twin

My colleague John Notman has written about the Identity of Things (IDoT) and how IAM is essential for creating digital twins, but I’d like to take this a step further. This disruptive technology calls for an identity-driven IoT platform as the foundation for the digital twin to facilitate and secure all the connections between people, systems and things that exist in the digital twins’ ecosystem.

This identity-centric IoT Platform must be able to do this at scale and enable the seamless integration between different digital twins – especially where large-scale deployments can quickly run into thousands of separate, but often inter-related, twins.

This identity-first approach to developing a digital twin is important because the twin represents a single point of vulnerability for both your physical and digital assets. You are, in effect, replicating everything in software and transferring that data over the cloud. The twin, and the IoT devices it's connected to, are integrated to other enterprise systems so your sensitive data and intellectual property are at risk of security breaches.

The failure to properly identify IoT devices creates unacceptable risk in three areas: a lack of visibility into the device’s activity or having secure access to the data it is creating, giving too much access to the device that, at best, creates noise on the network and, at worst, provides an opportunity for bad actors, and maintaining access to the device beyond its allotted lifecycle, again offering a backdoor for hackers or disgruntled employees.

You need to think ‘identity-by-design’ when starting to create your digital twins.

Three steps to creating your digital twin

Beginning to create a digital twin can appear daunting, but can be broken down into three stages:

• Design
  • There are two main elements to the design of a digital twin: First, you need to select the enabling technology you need to integrate the physical asset within its digital twin to enable the real-time flow of data from the IoT devices and integration with operational and transactional information from other enterprise systems.You need to be clear about the type of device you require, the modeling software needed to create the 3D representation of the asset and who is going to have access to the information within the Digital Twin or gain control of the physical asset through it. Secure IoT device management is crucial for overcoming the risks associated with identifying the devices on your network. It provides the capabilities to authenticate, provision, configure, monitor and manage each device. An identity-driven IoT platform allows you to do this quickly and securely at scale.
  • This leads to the second element in design. You must understand the type of information required across the life cycle of the asset, where that information is stored and how it can be accessed and used. It’s important that information is structured in a reusable way that can be quickly and effectively exchanged between systems. An identity-driven IoT platform can manage the identity of every element involved in the digital twin and provide  messaging services to automate the secure communications between these people, systems and things.
•  Operation
  • You must decide the function of your digital twin. Is it simply for monitoring the asset? Do you want the twin to control and alter the asset? Do you want to make data from the asset available for advanced analytics to assist with predictive maintenance? Or, do you want to use the data and models within the twin to perform simulations to help with operational performance and product development?
  • The answer to these questions will determine the types of devices you attach to the asset and whether you use more sophisticated devices that allow information processing to move to the edge. It will also determine your integration and data preparation, and will identify management requirements. The more sophisticated the application for the digital twin, the more comprehensive these capabilities. For example, most twins will look to exploit analytics to improve operational performance and decision-making. Controlling how data is ingested, stored, prepared and presented is essential to enable you to apply advanced analytics.To achieve high quality results, you have to guarantee the quality of data coming from your IoT devices. Each IoT device, including its rights to transfer and accept data, is verified. Taking an identity-by-design approach builds these capabilities into your digital twin from the outset.
•  Augmentation
  • Most digital twin implementations start small, such as monitoring the performance of a single part within an asset, but expand over time. This happens in two ways. First,  organization brings a number of smaller digital twins together to give a complete picture of an entire machine, asset or business process. Second, organizations add more sophisticated capabilities – such as simulations – into an existing digital twin.
  • In either case, you don’t want to rip and replace but to layer up the functionality within the digital twin to meet these evolving requirements. You need to be able to securely add functionality to scale while maintaining performance to meet the extra data that needs to be gathered and managed.

An identity-driven IoT platform enables you to quickly and securely extend the capabilities of your digital twin though extensive integration and open APIs that allow new devices and applications to connect and interact with the twin.

In the next blog in the series, I’ll look at how organizations can move from traditional modeling and monitoring of assets into the world of digital twins. If you’d like to know more about how OpenText™ can help you get the most from deploying digital twins, please contact us.

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At our recent OpenText™ Enterprise World 2018, there was a great deal of talk about two things: how companies have digitally transformed their business and how Enterprise Information Management (EIM) is changing to help digital business succeed both today and in the future by being intelligent and connected businesses. Connected is synonymous with the car industry, and EIM in the automotive sector is uniquely suited to deliver the central information platform that automotive companies need to realize the full value of their digital transformation programs.

From connected cars and electric vehicles to personalized production and smart factories, there are many trends driving the need for digital transformation initiatives in the automotive sector. It is also the sector where these initiatives can create the most value. According to the World Economic Forum, there is $0.67 trillion of value available to automotive players and a further $3.1 trillion worth of societal benefits as a result of digital transformation up until 2025.

Data and intelligence: The core of digital transformation

Modern drivers are increasingly connected, urbanized and environmentally-conscious. They are demanding a completely different customer experience based around new technology features and ownership models. To respond, automotive companies need to bring the customer experience to the front and center of their thinking.

This entails a profound business transformation driven by digital technologies. Organizations are beginning to leverage digital competencies to innovate new business models, products, and services. EIM in the automotive sector enables the seamless blending of digital and physical business processes and customer experiences while improving operational efficiencies and organizational performance. It provides an intelligent platform that allows information to flow end-to-end across the extended value chains of the modern automotive company.

IDC estimates that by 2021, 20% of G2000 manufacturers will depend on a secure backbone of embedded intelligence to automate large-scale process and speed execution times. I’d say that this figure will be substantially higher for automotive companies as they understand the need for digital technologies to drive innovation and agility.

As Seval Ox, CEO at Continental, puts it: “Companies in the automotive sector must innovate fast – or die.” The question becomes: which platform can you adopt to facilitate your digital business?

The case for a transformed EIM in the automotive sector

Taking an integrated information management approach is no longer just an option for automotive companies, it has become a ‘must have’. In the digital age, information has to flow without any friction in many directions: top-down, bottom-up, from inside out and outside in, between employees, business departments and across the business network. To enable that, companies must integrate data analytics, transactional content and content-related activities.

Sadly, this is often easier said than done. The core business applications—such a ERP, PLM or MES—have not been developed to meet these requirements. As such, most of the time business applications lack the capability to seamlessly transfer data and information to other business processes and applications. In order to effectively cover intra-enterprise processes, companies often need to rely on expensive and cumbersome custom applications or error-prone manual processes.

EIM in the automotive sector acts as a central platform that helps automate processes and workflows across the organization. It is a digital backbone that lets automotive companies improve the way their products and services are designed, produced, and delivered in the digital space. This is achieved through information flows that are highly managed, highly secure and highly available.

[caption id="attachment_33748" align="aligncenter" width="600"] Figure 1: EIM in the automotive sector: Central to digital processes (Source: IDC Manufacturing Insights 2017)[/caption]

Benefiting from the new capabilities of EIM

EIM has been around for some time but it is a very different technology now from when it first appeared. From being a central repository for the management of structured enterprise content, EIM in the automotive sector now delivers comprehensive capabilities that address the key business processes for automotive companies, including:

• Content Services to manage all structured and unstructured data within the organization
• Process Automation to increase the efficiency and productivity of key workflows and processes in the automotive value chain
• Customer Experience to create an end-to-end contiguous journey for customer loyalty
• Advanced Analytics to process the vast amount of internal and external dataset and deliver actionable insight to improve decision-making
• Discovery that makes all data auditable and compliant
• Business Networks that automate the communication and information exchange across supply chains and trading partner communities

The modern EIM solution brings all these components together on a single, central intelligent information platform. This is an approach that is increasingly popular with all manufacturers. According to IDC Manufacturing Insights, by 2020 over 60% of companies will rely on digital platforms "that enhance their investments in ecosystems and experiences and support as much as 30% of their overall revenue".

The automotive sector, with its focus on innovation and speed through agile production processes and integrated supply chains, will remain at the forefront of the adoption of intelligent digital platforms. Through this, companies can achieve the three most important objectives for digital transformation programs:

• To develop and market digitally enriched or enabled products and services
• To digitalize, harmonize and improve business operations
• To engage with digital customers

Modern EIM is the intelligent platform that automotive companies need at the heart of their digital systems. It can deliver the competitive advantage as companies explore the potential in new digital-driven products and services.

As Bill Ford Junior, Chairman of the Ford Group states, “If we do this correctly, we can have a very different-looking company 10 years from now. If we don’t do it correctly, we will be at best a low-margin assembler of other people’s technologies and that’s not where we want to be.”

I will be exploring how many of our Automotive customers are using EIM in their business when we meet for our next Automotive & Manufacturing User Group in Detroit, October 11th. Save the date now!

To find out more about the User Group and how EIM in the automotive sector is helping in automotive transformation, please contact us here.

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