4 Use Cases That Will Supercharge Your AI and Big Data Projects

By: Guest

6, June, 2019

Categories:

Artificial Intelligence - Business Intelligence - Data - Deep Learning - Machine Learning -

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When it comes to AI and big data, the possibilities seem limitless. But how can you ensure your AI and big data initiatives will bear fruit and prove business value? Check out these four use cases that may benefit your organization.

1. Automate Big Data Workflows

A recent Wall Street Journal article pointed to bad data as a reason that many big data and AI initiatives fail. Companies are having a difficult time prepping and processing the data to get it ready for consumption so the machines can act on it.

Malwarebytes struggled with processing the billions of records it collects to find the threats that really matter. The solution for them, according to Darren Chinen, head of the Data and Artificial Intelligence team at Malwarebytes, was to automate the way data is pushed into the company’s AWS-based data lake. “It was also critical that we had a world-class orchestration platform that could empower our engineers to focus on solving our big data challenges. We didn’t want them to spend time figuring out job failures or struggle with job scheduling and orchestration logic,” Chinen explains.

Malwarebytes uses Control-M to orchestrate the transformation for data cleaning, enrichment, optimization, and aggregation; to refresh Tableau dashboards; and to trigger the retraining of the models used for machine learning and prediction. “Keeping models trained is a key activity in machine learning and Control-M removes the worry of models going stale as it is intimately aware of when underlying data features are ready,” Chinen says.

2. Use AI for Event Noise Reduction in IT Operations

False events and alerts from the many monitoring tools installed across IT environments can lead to major headaches. These alerts could indicate a critical problem to a customer-facing app or service, but more often than not, they just clutter inboxes and cause unnecessary churn. AIOps reduces the noise of myriad events across an environment. To start, AIOps intelligently learns how the environment behaves in busy and slow times. It can then apply the knowledge of the behavior to the system-generated alerts to determine if, in fact, the alert indicates a bigger incident with potential service impact.

With AIOps, IT will only be alerted when the environment’s behavior indicates anomalous behavior indicative of app or service degradation or system downtime. This also helps prioritize which issues need immediate attention and which can be addressed in a less timely manner or suppressed to drive efficiencies for ITOps.

For instance, hybrid IT solutions provider Ensono was able to reduce event noise by 90% with the help of AlOps. Ensono also cut costs by reducing 10,000 tickets per month down to just a few hundred.

3. Power Capacity Analytics with AI

If IT is to support today’s digital business, it must understand resource consumption on premises and in the cloud. Capacity management is challenging and can be considered a skill that few can master. Yet with AIOps, that changes. Using behavioral learning, advanced analytics, and more, AIOps interprets the gathered data to understand what resources are being used and when—and perhaps more importantly, what resources will be needed to support the apps and services most in demand by customers.

By using the intelligence embedded in AIOps, IT can more easily plan for future needs using correlation analysis between business drivers and resource utilization metrics. The insights can also enable IT to allocate and schedule the resources needed to support new apps. IT can gain the intelligence to right-size resources, keeping costs down and applications performing as expected.

“With capacity management, you can take the business drivers and put them into the tool and understand those impacts across the different resources,” says Justin Martin, leader of the capacity management team at healthcare and technology leader Cerner.

4. Improve IT Service Management Data and Decision Making

Your service desk fields lots of requests and questions everyday through phone calls, emails, and chatbots. So how do you use the data contained in those interactions to better make decisions?

Doug Hynes, head of sales engineering for North America for Fusion Global Business Solutions, suggests using AI to help the service desk, which  can assist with properly categorizing information from phone calls or emails. This will help in the future when people are doing reporting or making capacity and hiring decisions. The AI-processed data will allow them to look at questions such as, “What are the areas where we’re lacking in knowledge?” Or, “What are our hot areas?”

Doing so gives more value to a service request. Now you’re looking at your business as a whole and making sure that you understand things in a more repeatable and a more efficient fashion.

Article by BMC who is a Platinum sponsor and will be exhibiting at the AI & Big Data Expo 2019 in Europe. Booth number 541.