Data Challenges Loom, but a Path Forward Exists
Data is the crown jewel of modern businesses. But several speed bumps get in the way of driving business success with data. The good news? Data streaming platforms can help combat these challenges in most and many situations.
Mind the Gap: Top Hurdles
Having access to the right data at the right time is crucial to gaining timely insights into market trends and customer needs—insights that ultimately drive the business agility that’s key to long-term, strategic success.
However, most organizations still face multiple data hurdles that get in the way of timely decision-making.
AI Rush Reveals Hidden Hurdles
Getting access to the right data has become even more critical with the onslaught of AI, especially generative AI. But as more companies ramp up adoption, additional data challenges are cropping up—including those driven by a lack of in-house AI expertise.
While the specific hurdles may vary, the underlying data issues mirror those faced in other data-driven initiatives. Tackling these challenges is crucial for organizations seeking to scale and maximize the value of their AI investments
Paving the Path to Success
Tackling data challenges head-on is crucial for maximizing business success in a data-driven world.
Our findings confirm, data streaming platforms help organizations address many of these pervasive challenges that hinder real-time, data-driven operations. DSPs ensure data is available in real-time, everywhere it’s needed—so businesses can focus on accelerating innovation instead of navigating the maze of point-to-point connections.
Extent of data related challenges experienced by the business
Rise Above Data Hurdles
Ensure you are investing in data tools that solve business problems. Niche tools can appear interesting but integrating them remains a challenge. We suggest the following:
- Enforce governance at data production. Reestablishing trustworthiness later is almost always harder.
- Ensure data producers create data structures with consumption patterns in mind.
- Make data discoverable to encourage reuse rather than recreation.
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