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February 26, 2024

Hakkoda Study Reveals the Need to Modernize Data Stack in 2024


We know the potential of GenAI is undeniable, however, to fully harness the power of GenAI, organizations must modernize their data stack. This means they will need to overcome challenges such as lack of organizational data literacy, internal resources gaps, and dependence on inefficient legacy systems. 

According to the recently released report by Hakkoda, 94 percent of organizations need to modernize their data stack this year. The survey of over 500 data leaders across industries shows that 2024 is the year of data modernization, at least for organizations that know how to get there. 

Hakkoda is a cloud data consulting firm that specializes in Snowflake. The company has deep domain expertise in financial services, healthcare, and the public sector. Hakkoda guides its clients to a data-driven future through a wide range of services including data modernization services. 

The 2024 State of Data report by Hakkoda indicates that new AI capabilities are being quickly integrated into business IT, with 50 percent of organizations actively implementing AI. Every organization surveyed believed that GenAI is important and nearly two-thirds shared that they believe GenAI will be very or critically important to their organization’s success by 2027. 

The report categorizes organizations into four stages of data maturity: Innovation, Insight, Order, and Chaos. Organizations in the Chaos category are behind in the data maturity cycle and are yet to identify the strategies, tools, or services that they need to optimize their data stack. 

On the other end, organizations that are in the Innovation phase are well set in their data maturity journey and have moved past standardizing and centralizing data to more advanced capabilities such as deploying AI for automation and monetizing data. Unfortunately, the Chaos organizations don’t know they are in chaos. 

Data-mature organizations seem to understand their needs better by acknowledging that they need external support to modernize their data stack. In contrast, the majority of the least data-mature organizations in the study believe they don’t need external support. 

While organizations that are less mature may have a high self-conception of success, the outcomes tell a different story. Organizations that shared their data strategies were moderately or extremely effective in 2023 but fell short of their objectives, with only only 56 percent achieving their strategy goals.


Several studies, including a recently released IBM report, have already pointed to the lack of AI skills as a major hurdle to GenAI growth within the organization. 

The Hakkoda report also highlights these challenges and reveals that organizations are considering getting external support to help make maximum use of their data stack. Seventy-nine percent of respondents believed they would need external support to achieve their data modernization goals. Organizations in the Innovation category are also getting better ROI (126 percent) for their investment in data tech, compared to Chaos organization (73 percent). 

 According to the Hakkoda report, 42 percent of organizations made use of GenAI tools in 2023. This number is expected to rise as organizations become more data-mature. The top moves to achieve data modernization in 2024 include deploying GenAI tools (85 percent), deploying a central cloud platform (74 percent), and monetizing data (64 percent). 

While all roads lead to GenAI, organizations will have to pass through data quality and governance hurdles. One of the key factors in an organization’s ability to overcome these challenges is leadership alignment. The report reveals a gap between strategy setters and executors, and this gap could be a deciding factor in the data modernization success of these organizations. 

Innovation organizations are leading the charge and are poised to reap the benefits of their investments in data modernization. The window of opportunity is narrowing quickly for the rest, and their success largely depends on their ability to identify the gaps in their data strategies and goals and make smart investments to plug these gaps. 

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