By Venkata Seshu Gulibhi, Vice President, Data & Analytics and AI, Infosys
Data is a crucial business asset, especially in digital businesses, where its quality directly impact transformation success. Unfortunately, most organisations struggle to leverage their data for targeted outcomes, hindering their ability to exploit Artificial Intelligence (AI) and GenAI advancements, which rely heavily on data quality. In a recent survey of nearly 500 chief data and analytics officers worldwide, 61 percent admitted that disruptive developments in AI were forcing them to rewrite their data and analytics (D&A) operating models.
A data-first approach should be prioritized, focusing on quality, quantity and breadth of data for successful digital transformation. It enables organisations to extract value from their data and D&A investments – by aligning it with business transformation goals. In AI implementations, a data-first approach can mean the difference between success and disaster, as high-quality training data yields great algorithmic results, while flawed data can produce undesirable consequences.
Steps to data-first
An organisation may need to implement some or all the following to become data-first:
Sift through data to keep what’s important: Enterprises should identify the data that is most relevant to their transformation goals. Breaking down departmental silos is essential to cleanse, deduplicate, standardize, and integrate data across the organisation. Acquiring advanced tools to prepare and tailor data for specific purposes is vital before beginning the transformation.
Select the right tools and technology for your needs: Before deploying new tools, it is necessary to map the existing data infrastructure to grasp the changes required. Organisations must carefully pick the right tools from the data management solutions available in the market, based on purpose and user personas and roles.
Cultivate a data-first mindset and culture: Everyone in the organisation, across ranks, should embrace a data-first mindset, understanding the importance of using data for making decisions. It is about ensuring the organizational data is complete, accurate, and of good quality since AI models that use this data will provide better insights or recommendations and hence improve the trust in AI. This requires continuous training of employees in data-first principles and usage practices. A team of “data-first champions” must be tasked with overseeing the various programs.
From data-first to data-ready
A data-first approach takes enterprises a step closer to becoming data-ready for AI. The combination of high-quality data and AI puts them at an advantage to achieve desired business transformation outcomes, such as:
Accurate forecasts, insightful decisions: With increasing amounts of clean data, machine learning models enhance their analytical capabilities, detecting finer patterns and correlations. This results in highly accurate analyses and granular insights, enabling diverse use cases such as demand forecasting, supply chain management, medical diagnosis, and fraud detection. Over time, the models become more stable and reliable, making them suitable for high-risk environments, such as autonomous vehicles and industrial automation.
Streamlined, optimised, operations: Since data is already clean, consistent, and accurate – the AI models can get to work immediately, and deliver outcomes sooner; there is also collateral advantage by way of lower energy consumption and operating cost. Further, better data leads to better outcomes first-time round, meaning fewer errors, less rework, and saving of time and money that can be deployed for other purposes.
Responsible AI: Data-related issues rank right up there in the list of AI adoption challenges. To achieve Responsible AI, especially accurate and unbiased outcomes, enterprises must put together datasets with those attributes to train algorithms. A data-first approach weeds out data flaws right before they enter the system to prevent risks and reputational damage, and over time, earn the trust of users and decision-makers; it also facilitates compliance with global data regulations and standards.
Personalised customer experience and engagement: Using clean, comprehensive, customer data for training AI improves the algorithms’ understanding of customer needs, preferences, attitudes and behaviours that can be deployed to personalise offerings and experiences to individual needs, perhaps even in real-time, to drive engagement and loyalty.
Data is the foundation of every enterprise today. But merely having data is not enough to transform business. A data-first approach ensures the right data is available for every need, AI use-cases in particular, to drive the right outcomes. Supported by a data-first approach, AI technologies can deliver immense advantage to organisations on the journey to business transformation.