IT Brief New Zealand logo
Story image

AI for enterprise: Data lakes for decision-making

For many years now, experts have been calling artificial intelligence (AI) the next frontier in technology, and recently, we have seen those predictions come to light for consumers.

From Siri to Alexa, AI has manifested itself in a range of consumer-facing products that aim to assist and supplement the menial tasks of daily life. Just think: when was the last time you asked Siri to give you the temperature outside, to set a reminder, or to order a product from your favourite retailer?

Halfway through 2018, the conversation around AI is shifting to the enterprise, as companies worldwide are beginning to adapt AI as a means to enhance employee experience and remain agile in today’s technological environment.

However, the stakes for the tasks being carried out by AI in the enterprise are often much higher – and come with significantly greater risks. As such, enterprise AI must be adopted quite differently than consumer AI. Alexa can easily tell you the depth of the deepest point in the Pacific Ocean with little consequence, but would you trust Alexa to carry out a multi-million dollar deal involving operations or a product rollout seamlessly – and without human intervention? While the technology may be capable, the risk is often too large to take.

Yet, integrating AI into enterprises is imminent, and those businesses that fail to embrace this type of technology risk becoming extinct. The question is no longer whether to use AI in business, but rather, which specific tasks are appropriate for automating and what are the best practices for making it most valuable for your business?

Perhaps the most obvious starting point for enterprise AI usage is data storage and retrieval. 

Creating and maintaining seamlessness in software solutions is no easy task, but it can be made fundamentally easier with AI. This is where the importance of data comes in – and becomes part of the technological solution. 

For example, many of our day-to-day technologies face constant upgrades that require support from IT teams. With AI and Big Data, this process – which takes up a significant amount of an IT team members’ time – can now be fully automated. Additionally, updates to the data itself can now be handled by technology, rather than by an employee.

The ability to capture data from across your enterprise—whether generated by disparate applications, people, or IoT infrastructure–offers tremendous potential. The value of AI is completely driven by the breadth and quality of the data. A data centre or data warehouse stores data that has been generated for a specific purpose, in a specific format, in files or folders. Layering services on top of a separate repository are no longer enough. The best solutions will allow for data ingestion and training off a data lake.

Data lakes store data in its organic format, with no hierarchy. This enables the complex integration points to prepare, cleanse, or even update data points. 

Data lakes are built on strong foundations centred around metadata. A metadata-driven approach to storing and consuming information forms a key part of decision-making as the business is able to search, catalogue, and marshal data to help deliver on heterogeneous integration requirements, ad-hoc reporting and referencing needs, and networking collections of data.

Insights and investments are infinitely scalable by ingesting more content to make better-informed business decisions. Data lakes can improve your analytics profiles and provide increasingly rich data sets to build more powerful machine learning processes.

By using data lakes to store their data, an enterprise can put their toe in the water, figuratively speaking, and make use of the benefits AI has to offer. 

By Infor SVP and GM Helen Masters.

Story image
ThreatQuotient launches automation capability for detection and response
"The focus of ThreatQ TDR Orchestrator is data, not process. In detection and response, what is learned when performing an action is far more important than the action itself."More
Story image
Everbridge acquires xMatters, bolstering incident response capability
End-users can now combine Everbridge’s CEM platform with xMatters’ solutions to support their digital transformation and mitigate cyber-incidents. More
Story image
Oracle Cloud selected to provide infrastructure for Premier League football
The league is deploying Oracle’s cloud, analytics and machine learning technologies to power in-match statistics that complement live action on the pitch.More
Story image
CrowdStrike and Google Cloud announce product integrations to boost security
CrowdStrike and Google Cloud have announced a series of product integrations to deliver customers better security, visibility and workload protection.More
Story image
Excelero announces public cloud storage support, starting with Azure
First for Azure, NVMesh expands public cloud capabilities and in doing so aims to address performance challenges organisations face while transitioning IO-intensive workloads to public clouds.More
Story image
Why the rise of containers has created a vulnerability crisis
A rise in the use of Kubernetes and Docker services — and increased adoption of DevOps methodologies — have all contributed to the rise in popularity of containers. But as with all emerging technologies, there are risks.More