Council post: Things to keep in mind when building modern data estates
From financial optimisation to creating a better customer experience, artificial intelligence (AI) has emerged as a force in helping organisations meet their operational and strategic goals. As per PwC, 54 percent of executives said AI solutions have turbocharged the business productivity. In another study, 61 percent of executives said AI helped their businesses identify opportunities in data which would otherwise be missed. Though businesses are bullish on AI adoption, many lack proper support infrastructure, and struggle while building the tech stack. Nearly 84 percent of the world’s leading enterprises don’t have accurate data and analytics strategies, and lack an understanding of the foundational processes, systems, and tools to become a truly data-powered company. As a result, companies with a competitive advantage in AI achieve 22 percent higher profitability on average compared to firms that don’t. To unlock the potential of AI, companies need to have a complementary stack of tools and applications. This includes tools that can help with feature extraction, analysis, process management, and machine resource management. Unfortunately, many companies either do not have a robust data infrastructure, or is too outdated to leverage the AI/ML code in any meaningful way. This is where modern data estates come into play. Simply put, data estate is an infrastructure that helps companies systematically manage all of their data. This infrastructure or data estate can be built on-premises, in cloud, or combination of both (hybrid). On-premise to cloud migration Though companies across the globe were migrating fully – or partially – to the cloud even before COVID-19, the pandemic turned out to be a real accelerant. As per Gartner, the worldwide spending on public cloud services is expected to grow 20.4 percent in 2022 to total USD 494.7 billion, up from USD 410.9 billion in 2021. Thus, companies have to cope with the teething troubles of switching to the cloud, but also have to make a choice in deciding which technology is best suited to power their business functions. Companies need to quickly get a handle on the suitable cloud delivery models, cloud technologies, draft in specialised talent, and more, all while trying to integrate AI. Unfortunately, this is often a recipe for disaster and may result in even further disruption in terms of cloud migration and AI integration. As a result, businesses need to plan their cloud migrations to lay the groundwork for future AI adoption. Switch to self-service BI and analytics As businesses continue to accelerate their AI initiatives, they have grown increasingly frustrated with bottlenecks that prevent them from monitoring the decisions,  and activating new decisions based on real-time data signals. This has subsequently given rise to ‘self-service’ analytics. Therefore, companies have increasingly looked for ways to make their data easily accessible without involving analysts. But, to make this happen, companies have to adopt new components – decision augmentation and automation systems that integrate BI/AI insights and real-time data signals with decision engines for monitoring, simulations, and activations– an uncharted territory for many businesses–leading to delays in delivering on broader tech and BI priorities. Data governance As per McKinsey, 30 percent of employees spend time on non-value-added tasks because of poor data quality and availability. As data becomes more central to business operations, there is a need for more governance. In other words, companies need a comprehensive suite of tools for oversight and reporting across business, technical, and operational metadata seamlessly. Presently, many companies have some data governance-related suite in place. But, because of the rise in awareness of explainability in governing AI, existing tools cannot deliver the insights needed to comply with modern governance requirements. As AI and data science teams grow, businesses need scalability and agility from their data estates. Thus, they need to embrace more modern tools to help them stay ahead of the curve. Wrapping up With Modern data estate, you can not only take data and transform it the way you like, but also find where the data was transformed, and assess how it was changed, and why it was changed. Modern data operations are both incredibly complex and exciting. However, to ensure that companies can hit all the marks in their data science and AI journeys, they need to have a concrete internal framework to support it. By keeping these trends and potential stumbling blocks in mind, companies can build the data estates they need to succeed. This article is written by a member of the AIM Leaders Council. AIM Leaders Council is an invitation-only forum of senior executives in the Data Science and Analytics industry. To check if you are eligible for a membership, please fill out the form here.
To unlock the potential of AI, companies need to have a complementary stack of tools and applications. This includes tools that can help with feature extraction, analysis, process management, and machine resource management.
To unlock the potential of AI, companies need to have a complementary stack of tools and applications. This includes tools that can help with feature extraction, analysis, process management, and machine resource management.
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["data science", "Go", "artificial intelligence", "AI", "ML", "RAG", "Aim", "analytics", "Rust", "R"]
["AI", "artificial intelligence", "ML", "data science", "analytics", "Aim", "RAG", "R", "Go", "Rust"]
Natwar is a member of the executive team and the Chief Technology Officer at Fractal. He leads a global engineering team to operationalise clients' data-to-decisions platforms and partner them in their digital transformation. Natwar is also a founder at Crux Intelligence.