By: Tom Christensen, Head of Thought Leadership - Nordics, LTIMindtree

Yet another successful edition of the Data Innovation Summit at Sweden, has come to a close, leaving us with a sense of honor and pride to have been part of such a remarkable occasion. The event had 3000 delegates, an impressive speaker lineup, global exhibitors, and engaging presentations.

The summit was designed to equally address all the elements of data-driven and AI-ready business to ensure the right balance of data, people, processes, and technology to succeed. It was crafted to be a comprehensive experience, striking the perfect balance between business and technology, offering practical insights along with inspiration, a realistic outlook intertwined with a futuristic vision.

Here are the key takeaways from the Data Innovation Summit, which spanned two enriching days in Stockholm.

The data-to-value concept

Upon reflecting on the keynote speakers and the emphasis on transitioning from data to value, I gained a clear understanding of the importance of a data strategy, that traditional AI continues to be a growing mature business, and how generative AI is expected to deliver benefits across the following three dimensions. Firstly, it can be seen as the next wave of productivity optimization – by doing work more efficiently, correctly, and faster. Secondly, it can be used to create new revenue streams and come up with new ideas to innovate products in the research and development phase. Thirdly, it can be used to streamline costs by reducing human intervention during a well-defined work process.
Success in these endeavors ultimately depends on a business-led approach

Technology for business success

The sheer volume of technology news could be overwhelming; however, companies invest in new technology when it brings true value to the business. For this reason, the race to success is about finding the most valuable business use case and then selecting the appropriate technology and data strategy.

Finding the most valuable use cases

To ensure the maximum benefit of data analytics, AI, or the latest generative AI, a strategic approach and an ecosystem of partners are necessary to jumpstart the adoption process.  This helps capitalize on collaboration and add skilled resources to more effectively navigate the business and data landscape complexities. Hence, organizations can start quickly and stay agile, innovative, and competitive in a rapidly evolving market.

Collaborating to create impactful use cases

Consider a partner-led co-innovation workshop for your business and product teams where individuals from different departments come together to generate fresh ideas, solve problems, and drive business growth through shared creativity and expertise using the latest technology. These workshops aim to foster a culture of innovation, enhance team collaboration, and spark new insights by leveraging the diverse perspectives and skills of employees. Orchestrated and combined with what’s possible with the latest technology, it leads to a list of relevant use cases with maximum impact on the business.

Bridging theory and practice

Practical examples where use cases are made tangible include delivering an app powered by generative AI to solve a predefined business challenge. At LTIMindtree, we accomplish this through our technology-agnostic Canvas.ai platform, which guides customers in creating navigator apps in a workshop. This method involves selecting the right use case, and appropriate data sets and training the model in a pre-released app with minimum variable features. When the outcome is confirmed, you are ready to finalize the app in your environment of choice. During the event, other vendors showcased a comparable approach, featuring vendor-specific technologies such as Microsoft Copilot and Google, among others.

Unlocking success by prioritizing your data strategy

One of the critical parts of success lies in getting the basics right. We should remind ourselves that without data… machine learning, analytics, and AI, Generative AI have no value. Therefore, it is important to start with a data strategy covering all data types (structured, semi-structured, and unstructured data), data quality, data availability, and so on. We know that 80% of the data landscape is unstructured data scattered all over the organization. Accept that data is decentralized but focus on breaking down data silos by introducing a Data-Fabric, Data-Mesh, Microsoft Fabric, or similar architecture, in combination with a marketplace to enable machine learning, business intelligence, and predictive analytics.

Exploring the world of data marketplaces

Data marketplaces were promoted by industry-leading technology players such as Databricks and Snowflake, among others. It is referred to as a data exchange, which is an online platform designed to facilitate the buying, selling, or sharing of data between various entities. It serves as a transactional location where data providers and data consumers can interact securely to exchange data assets. Data marketplaces offer a user-friendly way for organizations and individuals to discover, access, and utilize the data required for projects, reports, dashboards, and applications. These platforms enable users to buy and sell various forms of data, including data sets, data streams, and data services, in a secure environment, promoting data-driven collaboration and innovation within the digital ecosystem.

When to use Gen AI

Over the last year, the influence of ChatGPT has led to a significant awareness of generative AI. This technology has rapidly emerged as the industry’s fastest-growing technology, generating over 1.7 billion visitors within the initial 12 months¹. However, it is worth noting that traditional AI continues to dominate the overall potential value of AI with a revenue of between US$11 to 17 billion, as highlighted by McKinsey & Company in the report The economic potential of generative AI². Traditional AI with advanced analytics and machine learning algorithms are highly effective at performing numerical and optimization tasks using predefined rules and patterns such as predictive modeling, and they seem to continue to find new ways in a wide range of industries. On the other hand, generative AI is pushing boundaries in creativity, optimization, and innovation as it advances and matures, offering exciting possibilities for the future.

Prioritizing business objectives over technology-led strategies

The event elegantly demonstrated today’s fast-paced tech landscape, where organizations are navigating the challenge of being business-led rather than technology-led. A strategic shift towards prioritizing business objectives over technological advancements is crucial for sustainable success. To achieve this, companies are advised to conduct workshops to identify the most valuable use cases within their operations.

Following the identification of key use cases, the next step involves crafting a robust data strategy tailored to support these specific scenarios. This strategy acts as a guiding framework to streamline decision-making processes and ensure that data is leveraged effectively to drive business outcomes. Subsequently, selecting the most suitable technology solutions that align with the established data strategy becomes paramount.

Despite these strategic maneuvers, many companies encounter a significant roadblock on their path to success —a shortage of skilled talent. The scarcity of tech-savvy professionals remains a prevalent challenge, hindering organizations from fully realizing the potential of their digital initiatives. Addressing this talent gap by investing in recruitment, upskilling, and retaining top technical talent emerges as a critical imperative for businesses aiming to thrive in the evolving digital landscape.

However, others debated that relying solely on IT specialists may not fulfill all requirements. The necessity of a Global System Integrator (GSI) with local presence and industry-specific knowledge is crucial for scalability and success in the age of data-driven operations.

In essence, while embracing a business-led approach, leveraging data strategies, and adopting cutting-edge technologies are essential components of digital transformation, overcoming the talent shortage emerges as a vital factor that can ultimately determine the success or failure of these endeavors. Organizations must navigate this challenge proactively to unlock their full potential in the dynamic tech-driven environment.

Let me conclude by saying, see you next year at the Data Innovation Summit 2025.

Sources:

¹Number of ChatGPT Users (Apr 2024), Fabio Duarte, Exploding Topics, March 27, 2024: https://explodingtopics.com/blog/chatgpt-users

²The economic potential of generative AI: The next productivity frontier: Michael Chui, Eric Hazan, Roger Roberts, Alex Singla, Kate Smaje, Alex Sukharevsky, Lareina Yee, and Rodney Zemmel, McKinsey Digital, June 14, 2023:https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier

More information:

Insights to Help You Create Your Ultimate GenAI Roadmap: Unveiling “The State of Generative AI Adoption Report”, LTIMindtree: https://www.ltimindtree.com/canvas-ai/

The state of Generative AI Adoption: The Current Landscape and Lessons from Early Adopters, LTIMindtree: https://www.ltimindtree.info/gen-ai/