The exponential growth of data:? develop large-scale, secure, privacy-preserving, shared infrastructures—part 5/5

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The exponential growth of data is a 5 article series: our roadmap for your strategic postures and moves:

?? 1. The exponential growth of data: an overview—part 1/5 (please read it here);

?? 2. Build strengthened collaborations—part 2/5 (please read it here);

?? 3. Bolster multidisciplinary research —part 3/5 (please read it here);

?? 4. AI and ethics: legal aspects, and social implications—part 4/5 (please read it here);

?? 5. Develop large-scale, secure, privacy-preserving, shared infrastructures—part 5/5 (current).

This article is part of the “The Road to Sustainability?” weekly review on Linkedin. It is now followed by 12,800+ subscribers and counting, including Fortune Global 500 companies, from all industries and sectors, governmental and non-governmental agencies, VCs, fast-growing startups, and entrepreneurs from all around the globe.

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Table of content

1. Introduction
2. Leading towards technically complex and shared infrastructures
3. Emphasize the use of standards and frameworks to support the organization’s growth
4. Visual of the week
5. Endnote

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Article summary

Whether an organization needs to reposition or extend its core business, data management is essential to leverage the mission to integrate large-scale, privacy-preserving infrastructures and create a roadmap accordingly around the organization's strengths. 

Regulatory oversight is evolving beyond market concentration issues, including consumer data and privacy, global interest and security, and the future competitive landscape. 

This would allow data to be defined and linked to more effective automation, integration, and reuse across various applications. 

This would also lead to the development of standards and frameworks for effective information exchanges and support diversification, multidisciplinary collaboration, and shared infrastructure management.

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Introduction

The most innovative products and services have been influenced by data. To build long-term growth and productivity, organizations need an in-depth plan to capture data and make it part of the product lifecycle. But the journey to becoming a data-driven organization fit for the emerging AI economy is technically complex, and AI ethics-based frameworks aren’t enough.

To thrive, enterprises, causes, and initiatives will need to be backed by large-scale, secured, and trust-based infrastructures to address uncertainty. It will require reexamining initial core values, as barriers and biases can unintentionally be increased if the original resources capturing data are not well managed.

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Leading towards technically complex and shared infrastructures

Leading technology organizations have managed to stay ahead of the curve by creating and maintaining value.

The impact of the pandemic has been a fundamental shift in IT strategy. Actually, corporate infrastructure is continuing to implement data-based deployment technologies and to move forward with the flow.

Most successes invariably involve one of two roadmaps for building growth and accelerating productivity: extending their infrastructure’s capabilities into new domains or repositioning their core services during a paradigm shift. The challenge is the lack of cohesion and standardization at the infrastructure level. And as organizations become more invested in the digital transformation and their programmatic and operational work, the need to continually and actively consider distribution, trust and privacy are unquestionably more important than ever.  

However, the principal paradox is that innovation systems are not homogeneous. There is nearly always variety within them. The key idea is that different innovation systems interact at the level of their infrastructure through holistic evolution processes. And we can highlight another level of complexity as the digital transformation fundamentally impacts any organization's economic structure, affecting the level and nature of its processes, environmental and sustainability concerns, responsibilities, and initiatives. 

As mentioned before, “the urgency with which organizations need to adopt sustainability during a paradigm shift cannot be downplayed. The toughest challenge for all industries, decision-makers, and policymakers from public and private sectors remains customer-centricity and the consideration of a completely sustainable life cycle assessment plan. The digitalization of all parts of the business models plays a vital role in achieving sustainability, at scale, over a long period of time.”—[Read more about "Roadmap and perspectives" from The Road to Sustainability review series.]

Here’s the pivotal point: choosing the proper roadmap depends on where the organization falls in its life cycle: disruptor or incumbent. Most of the time, tech-based organizations encounter high growth phases as they extend into new domains. The opportunity lies in reinforcing value creation based on a holistic lifecycle, per its evolving environment, or more specifically, its ecosystem. I'm adding here the necessity to add a risk assessment plan—[Read more about "?? The risk matrix: value, activities, and application".]

Some of the most exciting disruption opportunities are markets held back from their true size by a high margin, slow incumbents. Usually, legacy systems have limited extensions of the real potential size of their ecosystem, and data based shared solutions are the big unlock.

This approach—which could be counterintuitive—is particularly potent if the market has concluded such repositioning is unlikely. 

Opinion: the tech industry only evolves as the chipmakers evolve.

Small and large companies, high and low tech alike, can benefit from time pacing, especially in markets that won't standstill. The move to cloud-based infrastructures creates a great need for more collaboration: corporate behavior, which produces both positive and negative internalities and externalities, reaches beyond financial markets—and the drivers are a healthy combination of opportunity and threat.

Therefore, organizations must understand their impact on a broader group of stakeholders, e.g., customers, employees, communities, and shareholders, for strategic management decision-making.

As stated in the previous review, how AI can augment decision-making continues to expand. New applications can cause profound and often challenging shifts in workflows, responsibilities, and culture, which leaders must carefully guide their organizations through.

Organizations that excel at integrating AI/ML should have a significant advantage in environments where humans and machines interact. Yet this means creating different tools and methods: roadmaps and frameworks—as ontologies1. In these critical phases, when collecting data by advocacy organizations, governmental agencies, research and development centers, or universities are first and foremost essential to the survival of any organization. Yet to accelerate impact value creation, data management is required to be highly qualified. 

“As concerns about scarcity and inequality have become increasingly urgent, the main requirement is to obtain appropriate data to assess the impact of the investments made: this means encouraging the management of unstructured and structured data. It also means gaining a robust understanding of the value of the data and the different outcomes.”—[Read more about the "Costs and benefits post-crisis" review.]

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Emphasize the use of standards and frameworks to support the organization’s growth

The deployment of sophisticated systems can lead organizations to higher network awareness by improving infrastructures in a collaborative environment. 

Yet, the thought of moving confidential data from an existing infrastructure to an external environment is disturbing for most organizations. There are plenty of reasons for that distrust. The lack of data; many organizations, if they ever collect, hesitate to share it because it often reflects poor or slow progress towards their announced goals. Other points are the lack of transparency and privacy issues. To ensure transparency in data governance, trusted auditing applications must be explored, evaluated, and assessed as part of the technology stack. Many explore assets from distributed ledger technology and “chains of custody” (e.g., blockchain): it is one solution among others.

A crucial element to succeeding in the implementation of infrastructures is trust. Trust is often talked about as the bedrock of an organization’s success. To build trust it is critical to diversify the AI workforce to prevent the narrow perspectives and unintended biases that can stigmatize the development and use of infrastructures.2

I can't but emphasize more that in a federated approach to data trust as cloud-native peer-to-peer applications that would achieve data interoperability, share computational resources, and provide data scientists with a shared workspace train and test AI algorithms. By leveraging the capacity to leverage AI-based systems that are becoming ubiquitous in fields ranging from finance and health care to law enforcement and the judicial system, organizations can attract and retain the workforce and build a multidisciplinary core. 

Regulations are increasingly essential variables to keep in mind when crafting an organization’s value-creation strategy.

We urgently need more AI-based models that enable ethical design. But as mentioned in our previous post: "Most AI ethical frameworks cannot be concretely implemented, as researchers have consistently demonstrated. A complex set of issues exist at the intersection of AI development and application uses. Some of the main areas include health and well-being, education, humanitarian crisis mitigation, and cross-cutting themes such as data and infrastructure, law and governance, algorithms, and design." [Learn more about "AI and ethics, legal aspects, and social implications".]

Progress proof and market extension

Whether an organization needs to reposition or extend its core business, the essential mission of data management is to deeply leverage the transition occurring in the ecosystem and create a roadmap built around the organizations' core activities. But to do so, it is essential to emphasize the use of standards and frameworks to be perfectly aligned with where the organization’s strengths match a promising new market opportunity.

It will help eliminate systemic biases causing breaches and risk tolerance within corporate practices, supported by movement capacity to amplify commitments and integrate an equity task force.

"The ones who are crazy enough to think they can change the world, are the ones that do."—Steve Jobs

You can explore our case studies and practice around our framework of values, principles, and purpose, ethical conduct on Nevelab's Vault. Please consider becoming a paying subscriber (enjoy the spring sale ending soon: 35% off!).

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Visual of the week

Get more insights and practical use cases. I invite you to learn more here.

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Endnote: 

This is the first time in history where there's a significant macro phenomenon, in this case, a health crisis, that has the digital transformation as a consequence. Social distancing led us to use digital interfaces for communication and transactions.

The next wave of infrastructure will be design-led. As more humans interact with systems to do their work, it will lead to more disruption opportunities. Yet, we will need to rethink how regulations, rules, and "decisions" are made to drive investment in infrastructures and see the impact of their efforts on their profitability. Considering that a large of the world is moving to more distributed format and remote work, we will have to move data holistically and improve infrastructure workload capacity. It will prove how important and critical are safe, privacy-led, shared infrastructures to help organizations run their business.

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Resources

1. An ontology is a consistent representation of data and data relationships within your business, a model of all the elements that go into and connect your various information systems: the products and services, solutions and processes, organizational structures, protocols, customer characteristics, manufacturing methods, knowledge, content, and data of all types.
2. AI Index Diversity Report: An Unmoving Needle by Stanford University

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