Artificial intelligence (AI) has captured the imagination of businesses worldwide, promising revolutionary insights and transformative outcomes. However, amidst the AI frenzy, a critical aspect often gets overlooked: data readiness.

A recent Wavestone report reveals that 79% of executives will be increasing their investment in AI/ML in 2023. But the truth is, most companies are far from having their data AI-ready, and this can hinder their AI initiatives. 

The report also sheds light on the alarming state of data understanding among businesses. It reveals that a staggering 81% of companies lack a comprehensive understanding of the data they collect. This lack of awareness poses a significant hurdle when it comes to leveraging AI effectively. 

Transitioning to a data-driven culture is another challenge faced by organizations. According to the same report, only 24% of companies have successfully made this cultural shift. Without a data-driven mindset, organizations struggle to derive meaningful insights from their data and make informed decisions.

For most organizations, poor data quality comes at a high cost. Gartner estimates that subpar data quality costs enterprise businesses an average of $15 million annually. AI algorithms are only as effective as the quality of data they process. If the data is flawed or of low quality, even the most advanced AI models will fail to deliver reliable results. 

It is essential for companies to take a step back and assess their data landscape before jumping on the AI bandwagon. To build a strong foundation, companies need: 

1. To establish a robust data governance strategy. This should include processes for data management, privacy, and security. By implementing effective data governance practices, organizations can ensure the reliability, integrity, and accessibility of their data. 
2. To integrate disparate data sources. Many companies struggle with data silos, where data is scattered across different systems and departments. Consolidating and harmonizing these data sources is vital to create a comprehensive view that can fuel AI algorithms effectively. 
3. To prioritize data quality. This involves conducting data cleansing, addressing missing values, removing duplicates, and resolving inconsistencies. By investing in data quality, companies can improve the accuracy and reliability of their AI models and drive meaningful outcomes. 

Once companies have laid the groundwork for data readiness, they can begin to explore the exciting possibilities of AI. AI-powered automation, predictive analytics, personalized recommendations, and improved decision-making are just a few of the benefits that await organizations that have prioritized data readiness. 

The Roadmap to AI-Ready Data 

While the allure of AI is undeniable, data readiness must be the first priority for organizations. Without high-quality, well-governed, and integrated data, AI initiatives will fall short of their potential. 

To help you navigate the complexities of preparing your data for AI, OneSix has authored a comprehensive roadmap to AI-ready data. Our goal is to empower organizations with the knowledge and strategies needed to modernize their data platforms and tools, ensuring that their data is optimized for AI applications. 

Read our step-by-step guide for a deep understanding of the initiatives required to develop a modern data strategy that drives business results.