The way we gather and organize knowledge is changing rapidly and artificial intelligence (AI) will have a far-reaching impact on our everyday lives, from healthcare to education.
AI as a Service
With the rise of AI-powered services such as Google My Business, LinkedIn, and Facebook’s Machine Learning, we are seeing a lot of changes to how companies store, structure, and understand data. This new landscape demands an approach centered around knowledge graphs (a flexibly-structured dataset containing semantically structured data for entities such as people, places, or things), a foundation upon which organizations can efficiently collect, organize and store business-critical information.
New Wave of AI
AI is rapidly evolving, with new applications spanning a seemingly unlimited range of uses. While AI upends old-world practices of enterprise IT operations and software deployment, it is adapting to recommend cloud services, monitor network performance, and analyze user experience.
The economics of AI adoption will look different than past technology waves because AI has the potential to be deployed as a service, meaning organizations won’t need to make significant capital expenditures to take advantage of it. There are no existing data centers to buy and upgrade, few supply chain barriers, and little to no new infrastructure required.
Because the talent pool of engineers who can work with AI is growing but not yet strained by demand, experts can typically receive higher salaries than in other fields. As a result, the market for AI is set to grow by a compounded value of 32% per year through 2022 — which is faster than almost any other segment in tech.
Transformation of Enterprise Practices
AI is transforming many industries and bringing new data management challenges with it. In response, new technologies that can streamline the storage of multiple types of big data in a centralized repository are emerging. However, with huge amounts of data, these technologies must also be able to simultaneously give enterprises the flexibility they need to test various AI models in a demanding environment.
As AI becomes ubiquitous across industries and applications, enterprises need to re-think their data management and AI strategies. The data they store will likely change dramatically as new machine learning techniques emerge and business needs shift. Consequently, organizations are looking for advanced AI tools to get ahead of the curve and ensure their enterprise is leveraging maximum value from the data at their disposal.
A graph database can adapt to changing data and changing data structures much better than traditional database management tools. Katana Graph’s intelligence platform can store huge, evolving data sets and run a range of AI algorithms on that data to look for new patterns and connections in that data to help make your organization “AI-first.”
