In a recent Cube conversation, I delved into Alation’s latest research report on the State of Data Culture Maturity. I was joined by Julie Smith, Director of Data and Analytics at Alation, to discuss the pivotal findings and their implications for modern organizations. This research is coming at a time when organizations are trying to understand what investments are needed in technology, processes, and people. Critical to investments that are being made in building data products and which, in many cases, now utilize AI. The old saying “Garbage In, Garbage Out” rings so true in the age of analytics and AI. But this is just one of the major challenges.
Source: Alation The State of Data Culture Maturity: Research Report, December 2023
Overview of Alation and Its Research
Julie Smith described Alation as a company that aids organizations in finding, understanding, and trusting data through their data catalog and intelligence platform. She highlighted her unique perspective, transitioning from a customer to an internal role at Alation. Smith underscored Alation’s recognition of the vital role of people and data culture in an organization’s success, especially for those aiming to be data-driven. The findings on who has the knowledge of where data lives and what is the right data, organizationally, were enlightening and map back to discussions we are having with data teams.
Key Findings of the Research
The research, involving nearly 300 industry professionals, not limited to Alation customers, revealed insights into the trends and perceptions of data culture. Smith emphasized the importance of networking and professional insights in understanding the broader challenges and advancements in the field. Four things became clear from the research: 1/ Data governance is the exception versus the norm, 2/ Search is a critical foundation for any data team, 3/ Organizations are focused on using data to drive business value, and 4/ Data leadership drives revenue returns.
You can find the full report here
The Importance of Data Leadership and Governance
We discussed the evolving role of data catalogs and their increasing importance in organizational data strategies. The report highlighted the correlation between strong data leadership and organizational success, with 89% of respondents having robust data leadership meeting or exceeding revenue goals.
Challenges in Data Governance
One of the primary challenges in implementing effective data governance, according to Smith, is people and the existing data culture. Technical challenges exist, but the main hurdle is aligning people’s understanding and approach to governance.
Emerging Trends in Data Governance
Smith pointed out that only a quarter of organizations feel they have achieved widespread data governance, signaling significant room for improvement. The trend is shifting from defensive (compliance-focused) to offensive (business value-focused) data governance. She also noted the increasing adoption of federated governance models, allowing more adaptability within business units.
AI and Data Governance
The conversation also touched upon the role of AI in emphasizing the need for quality data. Smith believes that the growing focus on AI will concurrently drive improvements in data governance, as trusted AI requires trusted data.
Advice for Organizations Embracing Data Culture
Smith advised organizations not to underestimate the challenge of developing a data-driven culture. She emphasized the need for strong leadership, data literacy, modernized governance, and empowering the workforce to discover and trust data. The key, according to Smith, is to measure progress in culture change and ensure that technology, people, and processes align.
Our ANGLE
The rising interest in AI and its impact on data culture is significant, with many organizations struggling to determine the right key performance indicators (KPIs) to measure. Focusing on creating a culture where data governance is the norm, tools, such as search, are embedded as the foundation for any data team to build processes around, continued focus on using data to drive business value and the right use cases that map to business value, and the largest is having organizational leadership drives a data-driven agenda based on revenue returns. All of these key learnings are related to each other in that having one, but not the others, will not yield returns from a data-driven strategy. This is why we continue to advocate for a Data Product Management strategy for teams so that use cases are mapped to data products that are built upon data features that solve the organization’s data needs.
(* Disclosure: Alation sponsored this segment of theCUBE. Neither Alation nor other sponsors have editorial control over the content on theCUBE or SiliconANGLE.)
