Enterprise Data Storage: The Key to Successful AI Initiatives
Dominik Krimpmann, PhD
Business & Technology Futurist at Accenture | Helping Companies Reimagine via Disruptive Technology
In view of the vast potential of artificial intelligence (AI), many enterprises now regard the tech as a must. In recent Accenture research, 95% of those polled saw value in working with the tech. But the steadily growing number of AI implementations poses challenges – particularly in terms of the ever-rising data volumes involved. That’s why choosing the right data-storage architecture is key.
AI: Reap the Benefits – Tackle the Challenges
AI offers many attractive opportunities, but it also inevitably brings increasing data volumes. It’s hardly surprising, then, that data storage is pivotal in AI implementations. In fact, it’s so important, and so often overlooked, that it has been called the “unsung hero of AI deployments”. And IBM considers data storage and data architecture – that is, how data is managed from A through Z – to be the underlying foundation for AI applications.
Bridging the Data Value Gap
However, companies often have to contend with a “data value gap” – where the value of their data lags far behind its potential. Internal research by Accenture shows that some 84% of C-level execs currently lack the data they need to exploit the full potential of AI solutions.
So, how can businesses bridge this gap and pave the way for successful AI initiatives? The answer is by deploying an effective data-storage architecture.
From Performance to Cost-Efficiency: Covering All the Bases
First up, companies must consider performance. Data volumes vary depending on the nature of the particular AI project. So, it’s important to ensure that your data-storage architecture meets your requirements and can handle expected throughput.
A further key aspect is security, especially the encryption and access control needed to safeguard sensitive information. Another, closely related, point concerns regulatory requirements and compliance. Not only must your data-storage architecture comply with applicable data protection regulations; it must also include mechanisms for tracking data access and modifications.
Manageability is another central factor. Can you operate you solution with minimal administrative overhead? Is it compatible with your existing enterprise infrastructure? If the answer to either of these questions is “no”, you could face potentially costly challenges.
And speaking of cost, when considering data-storage architectures, don’t just look at upfront costs. Long-term operational spend, including maintenance and upgrades, is also key – and offers considerable savings potential. An Accenture case study found that an optimized data-storage architecture reduced the cost per petabyte of storage by more than 70% and the cost per compute by more than 50%.
Every Architecture Needs an Architect
Experienced data architects are vital for the success of any data-storage architecture. Thanks to their profound knowledge of data management, storage solutions, and system integration, these expert play an important part in choosing the right storage architecture. Here, the architects’ tasks include the following:
Data-Storage Solution Types At-a-Glance
There are three main types of data-storage solutions, each with its own characteristics, as shown in the following table:
?Pure On-Premises and Cloud Solutions: Benefits and Drawbacks
Each of these approaches has its own distinct advantages and disadvantages. On-premises storage is ideal for organizations with strict data security and compliance requirements. What’s more, it boasts lower latency than cloud solutions, a vital consideration where real-time data access is required – for example, with Edge AI applications.
While initial investments in hardware, software, and infrastructure investments tend to be high for solutions of this kind, they’re also predictable.
The major benefit of cloud storage solutions is accessibility. They allow data to be used from any location, facilitating remote work and collaboration. However, a major drawback is that these solutions aren’t suited for growing volumes of data. As a result, cloud-first data-storage solutions are considered ineffective for AI implementations at scale.?
Hybrid Storage: The Best of Both Worlds
By combining on-premises and cloud approaches, hybrid solutions overcome many of these issues. Hybrid storage enables critical and frequently accessed data to be stored on-premises for faster access. At the same time, however, it makes the most of the cloud for backup and archiving tasks.
By blending the benefits of on-premises and cloud storage, hybrid delivers cost efficiency, scalability, and enhanced data security – enabling organizations to create an AI-friendly environment. Gartner’s 2024 Hype Cycle for storage technologies (login required) predicts that hybrid storage will have market penetration of as much of 50% of the target audience.
But these benefits notwithstanding, it’s important to be aware of the challenges hybrid solutions can present. For example, they can be tricky to manage because they require specialized knowledge, increasing administrative overhead. Additionally, latency issues can arise due to the cloud data involved. Cost management is another potential pitfall. If cloud resources aren’t managed carefully, there may be unexpected expenses.?
Key Takeaways
To get the most out of your AI initiatives, it’s essential to address the increased data volumes that they’ll inevitably entail. But by carefully considering the various aspects of data-storage architecture discussed above and by working hand in hand with seasoned data architects, you can create an AI-friendly data-storage environment.
In addition, you should establish effective vendor risk management so that you can identify any potential risks in good time. And to ensure responsible use and storage of data, you should also embed data ethics firmly in your data compliance activities.
Questions, Comments??
Want to find out more about data-storage architecture? Then feel free to reach out to me. What is your experience with data storage in the context of AI implementations? Let us know in the comments below.