Without high-quality, reliable data, AI and advanced analytics initiatives are doomed to fail. Being able to effectively manage data, then, is crucial in ensuring AI success.
Bharath Vasudevan, VP of product, Quest Software, led the Data Summit session, “Getting Your Data AI-Ready,” to explore the ways in which organizations can build resilient data strategies that improve data quality, visibility, and trust while reducing development time, enhancing decision making, and fostering collaboration.
The annual Data Summit conference returned to Boston, May 14-15, 2025, with pre-conference workshops on May 13.
“Things that used to be data initiatives are now AI initiatives,” said Vasudevan. With this in mind, building a successful AI strategy requires you to:
- Know what your leadership is facing.
- Understand the data and AI lifecycle.
- Find the right people who can help.
Diving deeper into these concepts, Vasudevan explained that leaders are plagued with concerns regarding the disparate systems holding valuable data, whether the data is trustworthy, the ever-present threat landscape, operationalizing a meaningful AI strategy, and the broad pressure to demonstrate AI success.
Yet, on the developer side, “I know if a project is not making money, taking out cost, or reducing risk, it’s not getting funded,” said Vasudevan.
Operationally, some necessary tasks for AI success include:
- Driving insights by stitching ERP and CRM data with third-party data
- Establishing trust by showing how all the data is transformed, from origin to reports
- Maintaining compliance while protecting your most critical resources
- Bringing innovative and impactful AI projects to production
- Realizing the savings and the promise of AI
Fundamentally, in understanding the data and AI lifecycle, “you need to know where you are and where you want to get,” said Vasudevan.
Generally, the data and AI lifecycle is as follows:
- Model: Understand the relationships that exist between the data, creating a standardized framework that makes the data more digestible.
- Build: Create applications that become a new source of data.
- Move: Migrate data from disparate application silos into areas where they can be aggregated and transformed.
- Govern: Ensure data trust and reliable access while achieving compliance.
- Use: Get information to the right people at the right time to unlock value from data.
Knowing who to look for in a partner and vendor also plays a key role in realizing data and AI success. Since complex business problems are rarely solved by a single product, the best solution will likely span several elements of the data and AI lifecycle, according to Vasudevan.
Many Data Summit 2025 presentations are available for review at https://www.dbta.com/datasummit/2025/presentations.aspx.