Building a Digital Transformation Roadmap

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Many corporations recognize their future depends on digital transformation. Earley Information Science outlines the 4 step process for building a roadmap to transformation.

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Building a Digital Transformation Roadmap

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Presented at Acquia Engage APAC by Brittany Fox, Marketing Campaign Strategist, Deloitte. Every organisation undergoing a marketing transformation has a starting point, with the difference only being the product of internal capability and maturity. At Deloitte, we take our clients from their starting point to being ready for whatever the next innovation is. This is the only real mechanism enterprises can implement for the future.

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Learn more: https://insights.hpe.com Enterprises can survive digital disruption as well as grow revenue, improve profitability and increase market valuation — if they start rethinking what they do. Digital transformation. It’s the use of technology to create a better customer experience, improve products and services, and increase the effectiveness of business operations. But it really means what your enterprise must do to adapt and thrive. Today’s smaller, emerging companies are born digital. They can — and do — change quickly to answer consumer demand or a competitive offering. Larger, mature enterprises must start with a shift in strategy, because all industries will be changed or already have been changed by digital transformation. Many, like news media and publishers, music, video and retail have been or significantly disrupted. Up next: financial services, healthcare manufacturing, insurance, legal, education, utilities and energy. The good news? No industry has been or will be completely upended. The first step is to recognize that disruption does not have to be a mass-extinction event. Enterprises can survive as well as grow revenue, improve profitability and increase market valuation. Here’s how to start rethinking what you do.

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Your Challenge Organizations have to adapt to a growing number of trends, putting increased pressure on IT to move at the same speed as the business. The business, seeing that IT is slower to react, looks to external solutions to address its challenges and capitalize on opportunities. IT and business leaders don’t have a clear and unified understanding or definition of an operating model. Our Advice Critical Insight The IT operating model is not a static entity and should evolve according to changing business needs. However, business needs are diverse, and the IT organization must recognize that the business includes groups that consume technology in different patterns. The IT operating model needs to support and enable multiple groups, while continuously adapting to changing business conditions. Impact and Result Determine how each technology consumer group interacts with IT. Use consumer experience maps to determine what kind of services consumer groups use and if there are opportunities to improve the delivery of those services. Identify how changing business conditions will affect the consumption of technology services. Classify your consumers based on business uncertainty and reliance on IT to plan for the future delivery of services. Optimize the IT operating model. Create a target IT operating model based on the gathered information about technology service consumers. Select different implementations of common operating model elements: governance, sourcing, process, and structure.

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Digital strategy roadmap is a blueprint for aligning business goals with your digital strategy. It shows how the company plans to embrace digital technologies to grow business and create better customer experience in the long term. Creating a digital strategy roadmap is not simply listing out key digital initiatives and giving them a tentative timeline. It follows an extensive research process which starts with Digital Review. It starts with uncovering consumer insights and market insights. One then develops Digital Strategy keeping in mind the digital goals and key initiatives in the short and long term. The third step in this process is Digital Planning which involves setting realistic time frame for implementing digital initiatives. The final steps involve setting KPIs for measuring the success of digital strategy. Taking the healthcare sector as a case study, our researchers have outlined the digital landscape of the industry and showed key digital strategies a healthcare provider wishes to implement for digital transformation. https://bit.ly/3bL7TlW

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Learn more: https://insights.hpe.com Your enterprise can digitally transform by gaining insights from your data to improve the experience for your customers. Enterprises need to make over all aspects of their business, because today’s customers expect frictionless experiences — and because new competitors launched with the latest technologies can change and respond to customers faster than mature companies. Start with the fact that your enterprise has valuable assets that start-ups don’t — your customers. Fostering loyalty among these customers requires improving their interaction with not only your products and services, but also sales, billing, support and shipping operations. Successful companies count on digital technologies to transform the total customer experience. As consumers, we’ve come to expect digitally enabled products as the new normal. But what’s the next step for your enterprise? Find ways to translate into their business lives what people love and expect as consumers. Enterprises can learn from the digital leaders who look for ways that apps and data can be added to products to create new value over time. Digital leaders use what they learn from the data to reshape core operations to drive the enterprise forward. What’s considered a core operation varies from industry to industry, but the common characteristic is that core operations make up a sizable portion of the enterprise budget. Gaining even a modest amount of efficiency through digital transformation can significantly impact the bottom line. Data also can be used to predict mechanical failure and to schedule preventive maintenance to avoid business disruptions. Digital transformation begins with data. So how can your enterprise gain insights from your data to improve the experience for your customers?

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Digitally mature companies out-perform their peers in innovation, agility and responsiveness to customers. “Digirati” also enjoy advantages in efficiency and effectiveness in product delivery, marketing, e-commerce, sales and customer service. More importantly, companies that achieve Digital Excellence are 26% more profitable (source: Capgemini Consulting and MIT Centre for Digital Business). However, building a Roadmap for Digital Transformation requires not only successful collaboration between the CMO and the CIO, it also demands a strong customer-focused orientation and digital culture. During this presentation, John Sinke will share insights from leading marketers and his personal experience of turning Resorts World Sentosa into a “digital business”.

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In an era where artificial intelligence (AI) stands at the forefront of business innovation, Information Architecture (IA) is at the core of functionality. See “There’s No AI Without IA” – (from 2016 but even more relevant today) Understanding and leveraging how Information Architecture (IA) supports AI synergies between knowledge engineering and prompt engineering is critical for senior leaders looking to successfully deploy AI for internal and externally facing knowledge processes. This webinar be a high-level overview of the methodologies that can elevate AI-driven knowledge processes supporting both employees and customers. Core Insights Include: Strategic Knowledge Engineering: Delve into how structuring AI's knowledge base is required to prevent hallucinations, enable contextual retrieval of accurate information. This will include discussion of gold standard libraries of use cases support testing various LLMs and structures and configurations of knowledge base. Precision in Prompt Engineering: Learn the art of crafting prompts that direct AI to deliver targeted, relevant responses, thereby optimizing customer experiences and business outcomes. Unified Approach for Enhanced AI Performance: Explore the intersection of knowledge and prompt engineering to develop AI systems that are not only more responsive but also aligned with overarching business strategies. Guiding Principles for Implementation: Equip yourself with best practices, ethical guidelines, and strategic considerations for embedding these technologies into your business ecosystem effectively. This webinar is designed to empower business and technology leaders with the knowledge to harness the full potential of AI, ensuring their organizations not only keep pace with digital transformation but lead the charge. Join us to map a roadmap to fully leverage Information Architecture (IA) and AI chart a course towards a future where AI is a key pillar of strategic innovation and business success.

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Many Organizations are struggling with the best way to govern and manage the use of Generative AI in the enterprise. There are many dimensions to this challenge ranging from ethical issues, data architecture and quality, legal and copywrite, operational and more. This is why a governance framework needs to be carefully designed and put into place so the business can make the most use of this truly revolutionary technology, reduce and mitigate risks, control costs, maintain a positive employee and customer experience and most importantly, find competitive advantage in the marketplace.

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Improving product data quality will inevitably increase your sales. However, there are other benefits (beyond improved revenue) from investing in product data to sustain your margins while lowering costs. One poorly understood benefit of having complete, accurate, consistent product data is the reduction in costs of product returns. Managing logistics and resources needed to process returns, as well as the reduction in margins based on the costs of re-packaging or disposing of returned products, are getting more attention and analysis than in previous years. This is a B2C and a B2B issue, and keeping more of your already-sold product in your customer’s hands will lower costs and increase margins at a fraction of the cost of building new market share. This webinar will discuss how EIS can assist in all aspects of product data including increasing revenue and reducing the costs of returns. We will discuss how to frame the data problems and solutions tied to product returns, and ways to implement scalable and durable changes to improve margins and increase revenue.

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In the rapidly evolving world of ChatGPT and Large Language Models (LLMs), businesses are understandably apprehensive. Numerous potential hazards and hurdles exist such as: Unrealistic expectations of LLMs as a magic solution to managing corporate content without requisite human involvement Difficulty distinguishing between creative outputs and fabricated responses (hallucinations) Decisions around training models: balancing usefulness with the threat of exposing trade secrets or other proprietary knowledge Absence of clear audit trails and citation sources The risk of generating responses misaligned with company policies or brand image Potential financial burden of proprietary LLMs and related enterprise software platforms In this webinar, we will examine a structured approach to harvest, utilize, and protect corporate knowledge resources. We will explore how both commercial and open-source large language models can be leveraged to deliver precise conversational responses without jeopardizing intellectual property. Learn how your organization can effectively use LLM based applications for competitive advantage. Using a general LLM will provide efficiency, but through standardization. Differentiation using your corporate terminology and knowledge will allow for competitive advantage. You don’t have to deploy ChatGPT to benefit from these approaches. They will improve the information metabolism of the enterprise and pave the way for advanced AI applications.

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In this session we will be discussing the challenges the organization faced in content usability, traceability, and findability, hindering their internal training workflows and access to critical knowledge assets. We will also discuss what’s next on the content and information horizon, including the role of machine learning and why these approaches are needed for AI-Powered applications, including LLMs and ChatGPT types of information access.

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Generative AI is getting all the attention, headlines, and industry hype. Organizations are looking at how it can be used to create better employee and customer experiences by unlocking the potential stored in the vast troves of unstructured data that house knowledge assets. We will begin by providing an overview of the fundamental concepts and advances in generative AI, followed by an in-depth examination of the importance of knowledge management in developing, implementing, and improving these systems. We’ll discuss knowledge management approaches for the organization and retrieval of information, how retrieval fits in with content generation, and the challenges and opportunities it presents for the enterprise.

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The Increasing Criticality of MDM for Personalization for Customers and Employees Master data management seems to be one of those perennial, evergreen programs that organizations continue to struggle with. Every couple of years people say, “we're going to get a handle on our master data” and then spend hundreds of thousands to millions and tens of millions of dollars working toward a solution. The challenge is that many of these solutions are not really getting to the root cause of the problem. They start with technology and begin by looking at specific data elements rather than looking at the business concepts that are important to the organization. MDM programs are also difficult to anchor on a specific business value proposition such as improving the top line. Many initiatives are so deep in the weeds and so far upstream that executives lose interest and they lose faith in the business value that the project promises. Meanwhile frustrated data analysts, data architects and technology organizations feel cut off at the knees because they can't get the funding, support and attention that they need to be successful. We've seen this time after time and until senior executives recognize the value and envision where the organization can go with control over its data across domains, this will continue to happen over and over again. Executives all nod their heads and say “Yes! Data is important, really important!” But when they see the price tag they say, “Whoa hold on there, it's not that important”. Well, actually, it is that important. We can't forget that under all of the systems, processes and shiny new technologies such as artificial intelligence and machine learning lies data. And that data is more important than the algorithm. If you have bad data your AI is not going to be able to fix it. Yes there are data remediation applications and there are mechanisms to harmonize or normalize certain data elements. But looking at this holistically requires human judgment: understanding business processes, understanding data flows, understanding dependencies and understanding of the entire customer experience ecosystem and the role of upstream tools, technologies and processes that enable that customer experience. Until we take that holistic approach and connect it to business value these things are not going to get the time, attention and resources that they need. Seth Earley, Founder & CEO, Earley Information Science Dan O'Connor, Senior Product Manager at inriver

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A knowledge graph is a type of data representation that utilizes a network of interconnected nodes to represent real-world entities and the relationships between them. This makes it an ideal tool for data discovery, compliance, and governance tasks, as it allows users to easily navigate and understand complex data sets. In this webinar, we will demystify knowledge graphs and explore their various applications in data discovery, compliance, and governance. We will begin by discussing the basics of knowledge graphs and how they differ from other data representation methods. Next, we will delve into specific use cases for knowledge graphs in data discovery, such as for exploring and understanding large and complex datasets or for identifying hidden patterns and relationships in data. We will also discuss how knowledge graphs can be used in compliance and governance tasks, such as for tracking changes to data over time or for auditing data to ensure compliance with regulations. Throughout the webinar, we will provide practical examples and case studies to illustrate the benefits of using knowledge graphs in these contexts. Finally, we will cover best practices for implementing and maintaining a knowledge graph, including tips for choosing the right technology and data sources, and strategies for ensuring the accuracy and reliability of the data within the graph. Overall, this webinar will provide an executive level overview of knowledge graphs and their applications in data discovery, compliance, and governance, and will equip attendees with the tools and knowledge they need to successfully implement and utilize knowledge graphs in their own organizations. *Thanks to ChatGPT for help writing this abstract.

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Some product information management (PIM) tools make it difficult to change core data models once they have been set up in the system. To avoid costly rework, you can utilize a “pre-PIM” design tool as a PIM accelerator. This class of software allows you to: **Iterate on designs before committing to a PIM architecture Improve data quality **Collaborate on decision-making and audit trails **Set up metrics around product data and attribute structure **Correlate performance measures with metrics – product data and hierarchy improvements are correlated with user behaviors and outcomes **Integrate governance content prior to PIM load **Decrease reliance on spreadsheets While some PIM tools include a subset of these functions, they are often lacking in flexibility, functionality, and integration capabilities, especially around product data model and hierarchy design changes. In this webinar our PIM experts introduce a pre-PIM software solution that enables fluid design changes while ensuring data integrity, reducing risk, increasing stakeholder engagement, and showing clear ROI on investments in product data.

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If you want to deliver a truly personalized product experience and strengthen customer loyalty, a Product Information Management System (PIM) is a must. PIM systems ensure clean, complete, and consistent data to enhance both the customer and employee experience. With intuitive management of complex product information, PIM unites internal teams with better visibility and reporting. In this session our experts in enterprise information architecture and PIM technology explain ways you can: --Streamline the complexity of supply chain information --Publish consistent product information across all channels --Adapt quickly to market changes and bring products to market faster --Increase the total performance and profitability your Ecommerce business Speakers: Chantal Schweizer, Director of Solution Delivery at Earley Information Science Jon C. Marsella, Founder, Chairman, and CEO at Jasper Commerce Inc.

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How Large Enterprises are Saving Millions in Operational Costs and Improving the Employee Experience. In this session, Earley Information Science, with partner PeopleReign, will show how these programs can rapidly produce measurable results in weeks rather than months and years. While large-scale knowledge problems cannot be solved overnight, by focusing on narrow AI with clearly defined processes and curated knowledge, organizations can see ROI in as little as 30 days.

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In today's world everyone, including your B2B customers, expect personalized buying experiences. Unless you have the right information architecture in place to power your digital experience tools you will not be able to scale and retain trust with your customers. In this webinar, B2B ecommerce experts Allison Brown with Earley Information Science and Jason Hein with Bloomreach walk through the reasons why you must invest in information architecture foundations in order to compete.

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In this webinar Seth Earley establishes the formula for AI success, demystifies the topic for executives and provides actionable advice for data strategists. Key Takeaways: **AI-Powered solutions begin with a focus on business goals **Successful AI requires a semantic data layer built on a solid enterprise information architecture. **Instrumenting measuring ROI should be part of every AI program

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Enterprises are increasingly recognizing the critical need for knowledge management (KM) to power cognitive AI. In fact, KM and AI are two sides of the same coin. Training a chatbot requires the same organized information that we use to train a human. When you engineer knowledge correctly, you serve the needs of people today and prepare for greater automation in the future. In fact, the long term success of the organization will depend on doing just that – especially when the competition builds high functionality bots that will produce lower costs and better customer service. Those without the capability will not be competitive. In this panel discussion, our experts discuss examples and approaches that show how KM supports AI and how to ensure the success of your KM initiative. Knowledge management and AI People and cultural considerations Business justification for long term investment

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