Today all companies are software companies, but many haven’t yet realized or fully understood the implications. This is because today’s software is used in almost all aspects of running a company no matter what type of business, even if a company doesn’t produce software as a product. In addition, almost all companies are producing and adopting AI tools at a feverish pace to increase business productivity and efficiency and stay competitive. This trend is increasing the number and the diversity of software applications used within companies, and it does not look like it will end anytime soon.
The Emergence of ‘Agile-as-a-tool’ and Then ‘Agile-as-a-culture’
Since the beginning of the Digital Age, many industries and thousands of organizations have been disrupted by the rise of technology. The initial Agile movement (or ‘Agile-as-a-tool’) was formally launched in 2001 when 17 technologists drafted the Agile Manifesto. The Agile methodology originated in the software development industry as a new way to manage software development. Many software development projects were failing or taking too long to complete, and industry leaders realized they needed to find a new, innovative approach. Over time the realization set in that true business agility is grounded in the adoption and evolution of values, behaviors, and capabilities. This adoption enables businesses and individuals to be more adaptive, creative, and resilient when dealing with complexity, uncertainty, and change, leading to improved well-being and better outcomes.
The ‘Agile-as-a-culture’ movement emerged as companies and Agile practitioners realized that to *fully* benefit from being truly Agile companies needed to operate in a very different way, with leadership, values, and norms all reinforcing the culture. Companies that successfully embraced the ‘Agile-as-a-culture’ are guided by deep, underlying assumptions, beliefs, and ways of working that permeate all levels of an organization and are replicated and passed on to other members. ‘Agile-as-a-culture’ is about creating an environment underpinned by values, behaviors, and practices that enable organizations, teams, and individuals to be more adaptive, flexible, innovative, and resilient when dealing with complexity, uncertainty, and change. Core values of ‘Agile-as-a-culture’ are:
- Innovation & Learning over the status quo
- Inspiring Leadership over conservative management
- Collaboration and Autonomy over hierarchical control
- Collective purpose over self-interest
The ‘Disruption 3.0’ Movement
Because of the rapid growth in data and applications used by businesses, new privacy and compliance needs, and the overall rapid pace of technology development and adoption (especially AI), managing a business has become even more complex. This disruption is driving companies to evolve their business processes and rate of change just to keep up and stay competitive. This disruption has been named the ‘Disruption 3.0’ Movement and is gaining traction within companies today. Great books such as ‘Project to Product’ by Mik Kersten describe this disruption and discuss how companies can use new approaches such as the Flow Framework to successfully navigate and manage the fallout from this disruption. (It should be noted that this is why the sub-title of this book is tellingly “How to Survive and Thrive in the Age of Digital Disruption with the Flow Framework”. Also, the Flow Framework has evolved from the Value Stream Management framework which came before it).
Insights and best practices surfacing from the Disruption 3.0 movement include:
- Adopting a yearly and quarterly planning and prioritization process.
(Or even better a monthly planning and prioritization rhythm of business). - Adopting company-wide Objectives and Key Results (OKRs) and North Star Metrics (NSMs) to guide our prioritization and decision-making processes.
- Pivoting from an ’enterprise agile’ to a ‘platform operating model’, creating platforms that are groupings of like business capabilities and the systems and people that deliver and operate them end-to-end, to reduce dependencies between teams and increase the velocity and reliability of a company’s technology ecosystem.
- Adopting a product mindset in everything a company does, treating experiences, APIs, data, and platform capabilities as products.
- Investing in the evolution of digital, data, and design practices driving customer obsession, insights, and digitalization in everything a company does.
- Continuing to grow and deepen the engineering experience, tooling, platforms, and paved paths that will continue to accelerate a company’s velocity and increase the reliability of its platforms.
- Ensuring that engineering has an equal voice in the organization so that the company is building the right thing the right way, continuously paying down technical debt, automating out legacy processes, and creating market-leading experiences.
New Data Governance Frameworks Emerging in Response
- Data Mesh 2.0 (or ‘Federated Computational Governance’)
Data Mesh 2.0 is related to data governance but is not solely a governance framework. Instead, it represents a decentralized architectural approach to data management that emphasizes domain-oriented ownership and federated governance. Key Features of Data Mesh 2.0 are:
- Decentralized Ownership: Data Mesh 2.0 promotes the idea that individual domain teams should own and manage their data products.
- Data as a Product: This model treats data as a product, meaning that teams are responsible for ensuring the quality, usability, and accessibility of their data offerings.
- Federated Computational Governance: This model allows for shared responsibility between domain teams and a central governance body while domains have autonomy over their data.
- Self-Serve Data Infrastructure: This model supports self-service capabilities, enabling domain teams to manage their data without extensive technical expertise.
Data Mesh 2.0 is not merely a data governance framework; its a holistic approach to data architecture that integrates governance principles within a decentralized model, enhancing data accessibility and accountability across organizations.
WDTA AI-STR-03 (specific to LLMs)
The WTDA AI-STR-03 standard is a data governance framework specifically designed for managing artificial intelligence (AI) systems and the data they utilize.
This framework addresses the unique challenges of integrating AI technologies into existing IT ecosystems, providing guidelines for various phases including development, deployment, and maintenance.
- WDTA AI-STR-03 was developed by the World Digital Technology Academy (WDTA). Its goal is to achieve security for the lifecycle of Large Language Models (LLMs).
- It is new but is already regarded as essential because AI models may be used in products or services operated fully or partially by third parties, but not managed by them.
WDTA AI-STR-03is part of a broader trend toward establishing robust governance structures that ensure ethical, secure, and compliant use of AI technologies.
References:
- Disruption 3.0 Beyond Agile (Macquarie Engineering Blog) –Link Here
- Towards an Agile Culture (Agile Business Consortium) –Link Here
- The WDTA AI-STR-03 Data Governance Framework & Standard –Link Here
- New global standard aims to build security around large language models (WDTA) –Link Here
- Data Governance in a Data Mesh –Link Here
- Data Mesh: Federated Computational Governance –Link Here
- Data Governance Data Mesh –Link Here