Artificial intelligence technology comes with complex data needs. At the Cork Institute of Technology, artificial intelligence research is powered by a supercomputer custom-designed by Dell Technologies. The Massachusetts Institute of Technology approaches artificial intelligence as a discipline inextricably intertwined with data infrastructure. The high demand that AI places on computer architecture can be accommodated with a variety of hardware and software solutions. A robust data infrastructure market has sprung up to provide artificial intelligence technology solutions—some of which require AI themselves.
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The market
It’s impossible to disentangle AI from data infrastructure in the current market. AI places high demands on data infrastructure, but it is also essential for analyzing and managing large amounts of data. The AllianzGI Artificial Intelligence & Technologies Fund, an investment firm whose financial strategy centres on emerging AI technology, points to data (including the Internet of Things, AI, augmented and virtual reality, and automation) as the major driver of growth in computing both now and in the future.
Before we go on, it’s important to draw a distinction between artificial intelligence and cognitive technology. AI is designed to make decisions and solve problems on its own. Cognitive computing is an older field that simulates and mimics human behaviours via complex algorithms. Both forms of technology can be used for tasks like speech recognition, but AI is more appropriate for automation and robotics. According to contributor Jun Wu on Towards Data Science, AI is often the more appropriate choice for service provision in today’s market. Our discussion of data infrastructure primarily applies to AI strategies for service provision. If your company profitably uses cognitive computing techniques, it is still possible to integrate them into an overall AI strategy as you accommodate the demands of the current market.
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Architectural shifts
The right AI technology for your business may include a mixture of proven architectures and novel designs. It’s important to understand architectural shifts in the current market when redesigning your data infrastructure in preparation for implementing AI software.
According to venture capital firm Andreessen Horowitz, data infrastructure is undergoing six major shifts:
- On-premises data storage is moving to the cloud.
- Bespoke map step/reduce step infrastructure like Hadoop is being phased out in favour of data lakes with Relational Database Management System (RDBMS)–like features.
- Extract-load-transform pipelines are replacing extract-transform-load processes.
- Workflow management is shifting to dataflow automation systems.
- Rather than being delivered by analyst teams, new software makes data analysis more accessible to users with less technical know-how.
- Security is moving from endpoint protection to centralization on the data platform itself.
These trends signal an overall shift from static to dynamic, algorithmic to agile, and invite the use of emerging AI technologies for data infrastructure management.
What AI can actually do for your data infrastructure
So you need good data infrastructure for AI, and you need AI for good data infrastructure. What is artificial intelligence technology going to do for your data storage and analytics that a solid team of IT personnel can’t handle?
Your team of IT personnel will still need to supervise your data infrastructure, but artificial intelligence and information technology can streamline data mining to gain unparalleled insight into customer preferences. Neural nets trained on user data have long been used by Silicon Valley giants like Facebook and Google to create targeted ads. Traditional stockbrokers and high-speed traders alike are using new artificial intelligence technology to disrupt the financial market. The future of law practice is also being impacted by artificial intelligence technology: repetitive, tedious work like text and image redaction can be delegated to machines, while deep learning programs applied to the body of evidence can supplement the development of legal strategy for trial.
All these applications of artificial intelligence technology stem from good data-driven insights. These insights emerge from a coherent narrative and are supported by advanced visualization techniques. Artificial intelligence technology can be used to curate data, refine the narrative, and create a compelling illustration of your results.
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How to know if your data infrastructure is ready for AI
The market is shifting towards cloud computing even as security features are becoming more centralized. Data lakes with RDBMS features and extract-load-transform pipelines are now the name of the game. Can your data infrastructure support your AI projects? Or do you need to overhaul your architecture?
Start out by determining how much computing power you will need for the AI projects you have planned. Will your current servers be adequate? If not, would you be willing to move some of your operations to the cloud?
Now, take a look at where and how your company’s data is currently stored. If your AI venture is a new and largely separate project that will not affect data storage and day-to-day operations, you can approach the data infrastructure for it independently from your current architecture. Otherwise, data integration techniques may be necessary to fuse the new artificial intelligence technology with the old hardware and software.
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Key takeaways for AI and data infrastructure
Architectural shifts in data infrastructure are transforming the current market. Take the current trends into account when setting up or adapting your data infrastructure for AI. If most major companies are moving away from Hadoop-like infrastructure towards more versatile data lakes with RDBMS-like features, it could be worth looking into. However, you may wish to retain on-premises data storage rather than migrate to the cloud, despite the direction of the market at present.
Ultimately, constructing the right data infrastructure for your AI-driven service provision strategy will require knowledge and expertise from IT professionals. Whether you choose to build your own GPU servers or outsource to external providers, go with a data lake solution or use a bespoke data storage, integrating the result will still require a human touch. Learn more about our market offer for artificial intelligence technology solutions today.
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