Three Impediments to AI Success

There is a sports equipment company marketing an AI-enhanced baseball bat. Could this be peak AI yet? Every company is rebranding their products and solutions as artificially intelligent. I prefer to plant our flag elsewhere. Nasuni is not an AI company. We are your partner in AI enablement and getting fit for AI .

We could just follow the wave and brand ourselves as an AI company, since we already leverage machine learning to improve the capabilities of our system behind the scenes. Our File Accelerator technology, for example, is designed to monitor data usage across large global organizations, identify patterns, and then prioritize and optimize the synchronization of what the technology deems important files. This is a classic application of machine learning. The system senses, recognizes patterns, and acts with some degree of autonomy. But that’s not how our customers can leverage Nasuni to enable AI inside their own environments. By making our customers fit for AI, we have a critical role to play.

If AI is the lightbulb, and your own data is the electricity, Nasuni is the pipeline that feeds power to that appliance and illuminates the room. If you hope to effectively leverage AI as a large organization, and you have growing stores of data distributed across multiple departments, user groups, and locations, Nasuni allows for the consolidation of all of that data into one logical entity that can be consumed by AI. Machine learning and AI tools feed on data, and Nasuni makes your data available to these advanced solutions through a secure, reliable and scalable platform.

Our technology directly addresses three major impediments to AI success.

1. Consolidation

You cannot use AI to extract value from your organization’s data if that data is siloed in different locations or distributed across disparate, disconnected systems. Nasuni consolidates all of your enterprise data, from every location across the globe and all departments and users, into a single global namespace in the cloud.

When all your data is securely stored and continuously updated in the object store, you have an infrastructure that is optimized for AI because you can tap into any one of a number of cloud-centric AI services, and provide those tools with a broad dataset that truly represents your organization. You do not necessarily want to train on that broad dataset, for reasons I will explain below, but consolidation is the first obstacle.

2. Access Control

The second common impediment to AI success is access control. You cannot turn to AI if your corporate access controls are not set up correctly, as you need to know which data can be made available to your chosen tool, and which data should not be made available. You must avoid feeding the wrong data to the wrong people or solutions. The last thing you want to do is fall out of compliance as you deploy AI.

This is where the Nasuni File Data Platform is extremely useful. The technology consolidates all your unstructured data while maintaining your established corporate access controls. Compliance concerns are off the table.

3. Intelligence

Once your data is consolidated, and the proper access controls are preserved, you need a way to understand the interactions between users and their data at a deeper level. Training an AI on a broad dataset covering your whole organization will not necessarily generate insights relevant to specific users or teams.

With Nasuni, you can clarify who is using specific datasets, how they are using them, and who they are collaborating with. You can extract and understand the interactions and mappings between users and data inside your organization. This gives you a very specific and beneficial level of data intelligence, and allows your chosen AI tool(s) to uncover truly meaningful patterns and insights. For instance, one can feed specific document repositories as the training sets for in-house AIs in order to provide easier access to reference material.

Quality In, Quality Out

AI’s ability to help you is only going to be as good as the data you’re feeding it. As my electronics professor used to say, garbage in, garbage out. If you don’t have a sense of who is using data and who they are sharing it with, then the AI will only be a general-purpose tool. That is not what you want out of this technology. You want to feed it the best possible data that is very specific to your organization and the groups and users within your organization – quality in, quality out – and this is where Nasuni is going to shine in the years ahead.

When the AI marketing craze settles, and baseball bats return to being baseball bats, our platform will be quietly operating in the background, intelligently managing the data that will help AI fulfill its true potential with customers truly fit for AI.