The Data Disconnect: Why Digital Transformation Needs a Data Strategy

This article is the second in a series exploring data strategy, beginning with its crucial relationship to digital transformation. Subsequent articles will delve deeper into the world of data strategy, offering comprehensive insights and practical guidance.

During one of my data strategy masterclasses, an attendee made a striking remark: “Our Digital Transformation Program is hindering us from implementing a decent Data Strategy. Every time the digital landscape modernizes, our data-driven efforts become more complicated.” While this might sound counterintuitive, it sheds light on the often strained relationship between the “data world” and the “IT world.” In many organizations, these two worlds seem to exist in separate universes, each with its own priorities and challenges.

Paradoxically, while digital transformation is expected to lead to a data-driven organization, a disconnect often exists between these two realms, hindering growth and preventing companies from fully realizing the benefits of their digital investments (Image: Author).

This situation is paradoxical. On one hand, organizations pour resources into vaguely defined digital transformation programs, aiming to revolutionize business operations, streamline processes, and unlock new growth opportunities. On the other hand, despite their “digital” label, many companies haven’t truly become data-driven. Instead, we often see a familiar divide — a “hyper-digitized front-end” showcasing sleek interfaces and user-friendly experiences, while the “back-end” struggles to manage the data deluge. This disconnect hinders innovation, stifles growth, and prevents companies from realizing the full potential of their digital investments.

This article explores this paradox, examining why digital transformation and a data-centric culture are not only intertwined but essential for long-term success. We delve into the common pitfalls that create this front-end/back-end chasm and propose strategies to bridge the gap, fostering a holistic approach that aligns digital tools with data-driven decision-making. By embracing a comprehensive data strategy, organizations can unlock the true power of digital transformation.

Digital Transformation

Digital transformation is often hailed as a magic bullet for business growth and efficiency. Defined as the integration of digital technologies into all areas of a business, fundamentally changing how it operates, this process promises to reshape customer interactions, streamline operations, and unlock new avenues for innovation. In fact, research from the MIT Center for Digital Business (as referred to by Kalra et al.) suggests that companies embracing digital transformation are 26% more profitable and enjoy a 12% higher market valuation than their industry peers.

Digital transformations changes the way a business operates by reshaping customer interactions and streamlining operations (Image: Author).

According to ‘The Digital Transformation Handbook’, the ultimate goal of digital transformation is to create digital enterprises — organizations that leverage technology to continually evolve their business models, offerings, customer relationships, and internal operations. This evolution is ongoing, mirroring the rapid pace of technological advancement. For businesses, the question is no longer whether to transform but how to do so effectively.

As described in ‘A Field Guide for Digital Transformation’, a transformation encompasses two key dimensions:

1. External Transformation: Revolutionizing how a business interacts with the outside world, particularly in terms of customer perception and engagement. This can include enhancing online presence, personalizing customer experiences, and leveraging social media for marketing and communication. It can lead to increased customer satisfaction, expanded market reach, and ultimately, higher revenue.
2. Internal Transformation: Reimagining internal operations, often requiring increased collaboration between departments, as well as between humans and machines. This might involve implementing automation to reduce manual tasks, utilizing data analytics for data-driven decision-making, and adopting agile methodologies to foster innovation. By optimizing internal processes, companies can improve efficiency, reduce costs, and enhance their overall agility.

Digital transformation, when approached strategically, can be a powerful catalyst for growth and innovation. By embracing digital technologies and adapting their business models, organizations can position themselves for success in an increasingly digital world.

Igniting the Data-Driven Revolution

Digital transformation acts as a catalyst for data-driven initiatives by shifting organizations towards automated and digitized processes. By replacing inefficient manual workflows (often driven by spreadsheets and shadow IT solutions) with streamlined digital systems, companies naturally begin to capture and store vast amounts of structured data. This wealth of information fuels a range of data-driven initiatives, unlocking new opportunities for growth and optimization:

• Data-Driven Management: Implementing key performance indicators (KPIs) based on real-time data empowers managers to make informed decisions rather than relying on intuition. This data-driven approach allows organizations to track progress towards strategic goals, identify areas for improvement, and proactively address challenges.
• Operational Reporting: By monitoring processes through tactical and operational reports, organizations can assess parameters like process efficiency, effectiveness, and product quality. This data-driven feedback loop enables continuous improvement, ensuring that processes are optimized for maximum output and value.
• Artificial Intelligence (AI): AI algorithms thrive on data. With access to large, structured and unstructured datasets, AI can uncover hidden patterns, automate repetitive tasks, and even generate creative content through generative AI. Digital transformation provides the foundation for AI adoption, enabling organizations to harness the power of machine learning for various applications.
• Digital Twins: Real-time data collection, made possible by digital transformation, allows for the creation of digital twins — virtual replicas of physical systems or processes. These digital representations can be used to monitor, analyze, simulate, and optimize operations in a safe and controlled environment, leading to increased efficiency and reduced risk.

Digital transformation empowers organizations to generate vast amounts of data, unlocking new opportunities for growth and optimization through initiatives like AI models, advanced reporting and dashboards, and even digital twins (Image: Author).

In essence, digital transformation and data-driven initiatives are two sides of the same coin. A successful digital transformation not only generates valuable data but also fosters a data-centric culture within the organization. This culture prioritizes data-driven decision-making, empowering employees to use data to solve problems, identify opportunities, and drive innovation.

Conversely, data is the lifeblood of digital transformation. Without a clear understanding of how to collect, analyze, and utilize data, organizations will struggle to achieve the full benefits of their digital investments. A data-driven approach is essential for identifying bottlenecks, measuring the impact of digital initiatives, and ensuring that technology is being used to its fullest potential.

Theory vs Practice

Despite the hype, the reality of digital transformation often falls short of expectations. The term itself is vague, encompassing various initiatives and lacking clear focus. Many organizations mistakenly equate “digital transformation” with simply rolling out new IT systems, neglecting the crucial aspects of people, processes, and change management. This misconception often also leads to prioritizing flashy front-end IT solutions over robust data management, further widening the gap between the promised revolution and the actual outcomes.

Party in the Front, Firefighting in the Back

Many digital transformations focus on creating a “hyper-digitized front-end” showcasing sleek interfaces and user-friendly experiences. In this front-end world, it appears to be a celebration of innovation. Cutting-edge tools are implemented, processes are automated, and users revel in newfound efficiency. Each department proudly showcases its own state-of-the-art technology, seemingly operating in perfect harmony. But beneath this veneer of success, a crisis simmers.

This front-end world contrasts with the raw “back-end” reality where it is often struggling to manage the data deluge. Integration issues plague the back-end, making it difficult to consolidate data and generate meaningful reports. Critical information required for data-driven decision-making is either overlooked or captured haphazardly. Ambitious AI projects stumble due to insufficient data quality or availability. Frustration mounts as the data team bears the brunt of the blame, facing unrealistic demands to “fix” problems that stem from systemic issues.

The result of a vaguely defined digital transformation is often “Party in the Front, Firefighting in the Back” (Image: Author).

This isn’t merely a tale of two realities; it’s a deeply interconnected crisis. The flashy front-end is built on a fragile foundation of neglected data management. The back-end struggles are a direct consequence of this oversight, and if left unaddressed, the entire digital transformation initiative can crumble under the weight of its own contradictions.

The Wheel of Chaos

The ‘illusion of a digital transformation’ can quickly spiral into a vicious cycle, often referred to as the “Wheel of Chaos” (see ‘10x Generation: Not Another Digital Transformation’). Desperate to resolve the growing disconnect, organizations may seek help from new technology leaders or external consultants. Yet, these interventions often result in more of the same — another round of IT-centric projects that fail to address the fundamental issues. The cycle repeats itself, with CIOs replaced, consultants hired, and the root causes of the problem remaining untouched.

The wheel of chaos (Image: Author, inspired by ’10x Generation: Not Another Digital Transformation’)

In my experience, this Wheel of Chaos can have a particularly devastating effect on the organization’s workforce. As projects flounder and frustration mounts, recurring staff, disillusioned by the lack of progress and the revolving door of leadership, often choose to leave. This exodus of talent leaves a vacuum filled by consultants, who, while providing temporary relief, lack the institutional knowledge and long-term commitment needed to drive meaningful change. The result is a dependency on external resources, further exacerbating the organization’s challenges and perpetuating the cycle of dysfunction.

The Root-Cause: A Lack of Data Understanding

To overcome the deceptive illusion of progress and break free from the “Wheel of Chaos,” organizations must shift their focus from merely implementing new technologies to fostering a data-centric culture and establishing a robust data strategy. This involves addressing the root causes of the disconnect between the front-end and back-end, ensuring that data is treated as a strategic asset throughout the digital transformation process.

Many challenges in digital transformation programs stem from a lack of shared understanding among stakeholders regarding the role of data (Image: Author).

Many challenges in digital transformation programs stem from a lack of understanding and communication between business, software development, data teams and many others. It is crucial to ensure that all parties involved in a digital transformation project have a clear understanding of the role of data, its importance, and the specific needs and requirements associated with working with data.

Here are some common misunderstandings that I experienced and how to address them:

• Data Accessibility: Prioritize data integration from the start when procuring or implementing new tools. Understand the technology capabilities of both the new tool and the existing data platform to ensure seamless data extraction. Open communication and collaboration between teams are key to avoiding unexpected roadblocks.
• Data Requirements: Clearly define data requirements for each use case through close collaboration between business stakeholders and data teams or stewards. Avoid vague requests for “all data” and ensure everyone understands the specific data elements required for streamlined collection and analysis.
• Data Changes: Consider data change management capabilities during tool selection and implementation. Not all front-end tools facilitate identifying data changes, which are crucial for optimizing data processing in platforms. Proactive planning ensures efficient data pipelines and avoids disruptions.
• Data Contracts: Recognize and document implicit data contracts between front-end tools and data platforms. Changes in tool structure or data formats can disrupt these contracts. Proactive communication, regular testing, and version control can maintain data integrity and prevent costly outages.

Having led data teams across various organizations and cultures, I’ve witnessed these misunderstandings firsthand. The most extreme example was a four-year ERP implementation project delivered overnight, with the expectation of rapidly connecting a data warehouse to inaccessible databases, despite lacking documentation or data model knowledge. Any meeting with stakeholders resulted in a stalemate; a lack of understanding from each other’s perspectives transformed the “quick DWH connect” into another multi-year endeavor, delaying the organization’s ability to leverage data for insights and decision-making.

A Holistic Data Strategy

A holistic data strategy is key to bridging the disconnect between the “data world” and the rest of the organization. While a comprehensive discussion of data strategy warrants a dedicated article, in the context of digital transformation, a few key elements are essential:

• A roadmap of data-driven initiatives, adaptable to evolving business needs through a combination of top-down planning and bottom-up innovation.
• A clear articulation of how each initiative on the roadmap directly contributes to the realization of the overarching business strategy.
• A framework of metrics to objectively measure the impact and progress of your data-driven strategy, ensuring accountability and continuous improvement.

By adding a data strategy to a digital transformation program, we may switch terms to a “Data Driven Digital Transformation”. As defined by Belhadi et al., a data-driven digital transformation is a fundamental process triggered by the innovative use of data analytics capabilities to radically improve organizational performance. This transformation is powered by the vast data generated within the firm’s environment, driving transformative changes facilitated by the firm’s ability to process information effectively.

Roadmap

The cornerstone of any data strategy is a multi-level roadmap. This roadmap encompasses both strategic and use-case levels, ensuring alignment between overarching data goals and specific initiatives.

• Strategic Roadmap: This outlines the fundamental initiatives required to enhance data maturity within the organization. These may include data governance initiatives such as data quality management, roles and responsibilities, data ethics and privacy, data processes, and other key areas. One such initiative might be to enhance data literacy across the organization, ensuring employees understand the value of data and can effectively utilize it to drive decision-making.
• Use-Case Roadmap: This focuses on specific data-driven use cases, such as reports, AI models, or data-driven tools. These use cases should directly contribute to achieving business goals.

A data strategy’s core is a multi-layered roadmap, seamlessly integrated with the overall business strategy (Image: Author).

The two roadmaps are interconnected, with strategic initiatives often serving as prerequisites for successful use case implementation. For example, a use case to implement a generative AI model that summarizes and analyzes tender documents would require strategic initiatives to ensure the structured availability of tender documents and establish security and privacy policies to protect sensitive information.

Ultimately, the data strategy should be designed to support and accelerate the realization of the broader business strategy and goals.

Top-Down and Bottom-Up

The most effective data strategies are dynamic, continuously evolving to adapt to new challenges and opportunities. This dynamism is best achieved through a blend of top-down and bottom-up approaches.

• Top-Down: Leadership defines the vision, establishes the foundation for data governance, and invests in data literacy initiatives to raise awareness of data’s value.
• Bottom-Up: As data literacy grows, employees become empowered to generate innovative ideas and initiatives for leveraging data to drive business value. These ideas are then evaluated and prioritized through a portfolio management approach, ensuring that projects are aligned with strategic goals and deliver tangible results.

This dual approach, inspired by Mintzberg et al.’s “Strategy Safari,” recognizes that strategy emerges not only from deliberate planning but also from the creative energy and insights of individuals throughout the organization.

A data strategy should have top down and bottom up flows (Image: Author, inspired by Mintzberg et al.)

Strategic leadership is crucial in balancing these two approaches. By monitoring progress, identifying roadblocks, and adjusting the data roadmap accordingly, leaders ensure that the data strategy remains aligned with the evolving needs of the organization. This continuous adaptation ensures that the data strategy remains a living document that drives ongoing improvement and value creation.

Metrics and Follow-Up

To ensure the success and continuous refinement of your data strategy, it’s essential to “eat your own dog food” — using data to measure the impact of your data-driven initiatives. Key metrics to track include:

• Use Case Realization Grade: Monitor the progress and timeliness of each data-driven use case implementation to identify bottlenecks and optimize your data delivery pipeline.
• Coached Data Employees: Track the number of stakeholders trained or certified in data literacy to gauge the success of your data literacy initiatives and the growth of data skills within your organization.
• Data Quality: Implement KPIs to monitor data quality trends, ensuring that your data remains accurate, complete, and consistent for reliable decision-making.
• Delivered Value: Measure the actual ROI of each data use case to validate initial assumptions and improve your ability to estimate future ROI for subsequent projects.

By diligently tracking these metrics, you create a data-driven feedback loop that enables you to assess the effectiveness of your data strategy, identify areas for improvement, and demonstrate the tangible value of your data-driven initiatives to the entire organization.

Conclusions

The journey towards true digital transformation is fraught with challenges, often marked by a deceptive divide between flashy front-end innovations and neglected back-end data management. While digital transformation promises significant advancements in efficiency and growth, its full potential can only be realized through the integration of a robust data strategy.

Organizations must recognize that digital tools alone are not enough. The true power of digital transformation lies in fostering a data-centric culture built on seamless data integration, promoting data literacy across all levels, and implementing a dynamic data strategy that aligns with overall business objectives.

Integrating a holistic data strategy into your digital transformation program is essential. It lays the groundwork for realizing data use cases that can further innovate your business model and provide a competitive advantage. This involves prioritizing both top-down leadership and bottom-up initiatives, fostering a collaborative environment where data-driven decision-making thrives.

In crafting a data strategy, it’s crucial to strike a balance between technological solutions and foundational practices. While technology is a powerful enabler, establishing a solid groundwork is paramount. This approach helps avoid the “Wheel of Chaos” and ensures that technology investments are purposeful and effective. As your organization matures in its data journey, technology will inevitably play a more prominent role. To achieve your strategic data goals, seek expert advice and rely on skilled technological talent. Emerging trends like data catalog tools, data mesh architectures, and AI-powered data governance can help build a robust technological foundation for your data-driven organization.

Remember, technology is a means to an end, not the end itself. By prioritizing foundational practices and aligning technology with your strategic goals, you can harness the power of data to drive innovation, efficiency, and sustainable growth.

Questions? Feedback? Connect with me on LinkedIn or contact me directly at Jan@Sievax.be!

This article is proudly brought to you by Sievax, the consulting firm dedicated to guiding you towards data excellence. Interested in learning more? Visit our website! We offer a Data Strategy Masterclass that provides a deeper understanding of the world of data strategy.