AI has become an enabler of significant competitive advantage and a way to disrupt markets. A recent Accenture survey revealed that AI achievers–those companies that advance AI maturity enough to achieve superior growth and business transformation–attribute nearly 30 percent of their total revenue to AI and outperform in areas that include customer experience and sustainability. For organizations across all industries, the opportunities that AI brings are impactful in substantive ways: Augmenting human activities in medicine to provide easier detection and better outcomes; mitigating financial risk by detecting fraud and security threats; improving industrial quality control with automation; and innovating everything from vehicles to our food system are just a few ways that AI is transforming our world. Whether you’re a C-level executive or a data scientist, AI is acknowledged as a true business game changer.
Driven by greater access to, and the availability of, data, combined with high performance computing systems augmented with powerful GPUs, AI has become an organizational priority–and mainstream technology. In fact, a Harris Poll found that 55 percent of companies reported accelerating their AI strategy due to COVID, and 67 percent expect to accelerate their AI strategy moving forward.
But just because AI adoption is increasing doesn’t mean it isn’t without its challenges. Many organizations’ AI initiatives fail, not only due to lack of proper planning, but also due to an inadequate IT infrastructure to move beyond a POC in a cost effective and scalable way. By one estimate, around 80 percent of AI projects never make it into production. Why?
Data Storage: Closing the Gap Between Data Science and the Business Solution
The gap between the data science solution and the business solution comes down to the lack of an integrated and optimized IT infrastructure. It needs to be built correctly from the start, and also keep data in place as POC moves to production, allowing for both the management of data quality and the expansion to various cloud services to enhance data science productivity.
The challenges with data storage and management, in particular, require planning for data growth so you can extract the value of data as you move forward, especially as you begin more advanced use cases such as Deep Learning and Neural Networks, which require more compute and storage power, performance, and scale.
The requirements for AI drive the demand for higher processing power and throughput, and Machine Learning only increases these requirements. When your infrastructure doesn’t allow you to scale compute and storage independently, the problems start.
- Time to market is dramatically impacted when data is stored on smaller systems that don’t scale out. With limited space you must move data as part of the normalization process, and smaller systems require data to be separated and deleted, which affects the…