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Digital Business Methodology: The Pathway to Unlocking the Power of Digital Business

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Digital Business Methodology: The Pathway to Unlocking the Power of Digital Business

Digital Business Methodology: The Pathway to Unlocking the Power of Digital Business

It’s been fifteen years since the inception of DevOps, a movement to break down silos and improve collaboration tools and practices between software engineers and technology operations. The objective was clear, to expedite software releases while ensuring their quality. Over time, DevOps has become a standard operating model, improving collaboration, delivery, and fostering an agile digital culture. However, as digital business takes center stage as the primary driver of growth, a fresh mindset is required to achieve tangible outcomes. To embrace this new paradigm effectively, it is crucial to acknowledge the importance of digital business and establish a robust methodology to govern its implementation.

Differentiating between digital business and digital transformation is very important. The term “digital transformation” has become synonymous with the adoption of data and digital capabilities, but this is a misconception. CEOs and C-Suite executives should shift their mindset away from a technology-first approach associated with digital transformation and instead shift to digital business and its emphasis on solutions that deliver tangible outcomes. Digital business operates within a company’s existing ecosystem, but it functions independently of existing silos and complex technology environments, ensuring minimal disruption to the current business operations. At the heart of digital business lies the Digital Business Methodology (DBM), serving as the driving force for success.

As a holistic approach, the DBM enables companies to adopt and implement digital business, it provides a defined path that orchestrates and converges data, technology, and people, delivering an outcome-driven, incremental approach delivering results across the enterprise with speed, consistency, and scale. This is powered by a business led agile digital culture that focuses on bite-sized outcomes essential to accelerating business growth. The business takes the lead in collaboration with key stakeholders from ideation to deployment, focusing on the simplification of end-to-end workflows and the establishment of a single sources of truth (SSOT).

DBM is a guided, adaptable ideation-to-deployment ecosystem that enables seamless collaboration between business owners, engineers, analysts, scientists, and operational teams to drive innovative solutions and achieve outcomes. The establishment of strict governance ensures engineering rigor, quality, security, compliance (audibility, and traceability), and cloud services enabling companies to operate with higher productivity and predictability. A key outcome of the DBM is the establishment of strong data management and governance, bringing core and domain data into an enterprise SSOT as a precursor for AI/ML.

According to Statista, global spend on AI in 2023 the worldwide market revenue for artificial intelligence is forecast to grow significantly from 2018 to 2030. Estimates vary but they could reach over half a trillion dollars by 2024 and over $1.5 trillion U.S. dollars by 2030. While many companies are quick to jump on the AI bandwagon and readily allocate significant funding, they do so out of anxiety or fear of missing out. Unfortunately, these emotions are pushing them to get ahead of themselves without building the underlying core foundation of data that enables the effective use of tools like AI. Adverse consequences of not creating a proper data ecosystem include:

  • Incomplete data or low quality leading to biased AI models, inaccurate predictions, and poor decision-making.
  • Low-quality data lacking the necessary robustness to generate meaningful insights, resulting in unreliable and inconsistent outcomes.
  • Limited Insights and decision-making capabilities due to low-quality data lacking the depth and breadth, hindering effective decision-making processes.
  • Unreliable automation caused by models trained on poor-quality data, negatively impacting automated processes and operational efficiency.
  • All these factors can negatively impact strategic decisions, automated processes, and operational efficiency.

To correctly sequence and construct the building blocks for a comprehensive end-to-end data ecosystem, it is critical to focus investments on specific components that include:

  • Data quality and governance ensuring high-quality, clean, and reliable data.
  • Integration of data across various core and domain sources, both internal and external, to create a comprehensive and unified SSOT.
  • Scalable and robust infrastructure for handling the volume and velocity of data, as well as providing the necessary computational resources required for AI applications.
  • Strong data security measures, implementing encryption protocols and data privacy practices to maintain trust with customers.
  • Team building and upskilling employees or find a partner with skills and expertise in data science, ML, and AI.

Creating an enterprise-wide data ecosystem that can be utilized for AI/ML optimizes business strategies for better outcomes and higher growth. It is critical to build a comprehensive end-to-end data ecosystem and achieve numerous competitive benefits, greater agility, and faster responsiveness. These benefits include:

  • Informed and improved decision-making through leveraging vast amounts of data, leading to enhanced strategic execution and agility.
  • Increased productivity through AI by automating repetitive tasks, producing greater operational efficiency and resiliency.
  • Personalized customer experiences created by AI-powered algorithms that analyze customer data, tailor marketing campaigns, and improve customer service levels.
  • Uncover patterns and insights in large datasets, enabling better predictive analytics, AI algorithms, forecasting, risk mitigation.
  • Drive Innovation and new product/service development, empowering companies to create competitive offerings and generate higher margins.

DBM, in relation to digital business, can be compared to what DevOps is to software development. DevOps has revolutionized collaboration and improved the delivery of software, similarly digital business has emerged as the driving force for business growth, necessitating a mindset and methodology for achieving results. Digital business goes beyond digital transformation by focusing on business outcomes rather than a technology-first approach. At the heart of digital business lies the DBM, which brings together data, technology, and people, delivering results with speed, consistency, and scale. DBM fosters a business-led agile digital culture, streamlining workflows, and emphasizing a SSOT. It establishes governance to ensure engineering rigor, quality, security, compliance, and cloud services, enabling companies to operate with productivity and predictability.

Notably, DBM also facilitates strong data management and governance, setting the stage to utilize AI/ML advancements. To fully leverage the potential of AI, companies must prioritize building a robust data ecosystem that addresses data quality, integration, infrastructure, security, and talent. By doing so, they can harness the power of AI for informed decision-making, increased productivity, personalized customer experiences, uncovering insights, and driving innovation, ultimately achieving competitive advantages and accelerated growth. DBM is the pathway to digital business and AI; it is a game changer for growth and unlimited outcomes.

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