Enterprises are reaching strides in Marketing, but are these companies reaping the rewards of unified data analysis?

Over the recent years, software development organizations have seen a major shift in where they build and run their applications. Teams have transitioned from building applications that run exclusively on-prem to microservices applications that are built to run natively in the cloud. This shift gives businesses more flexibility as well as quick and easy access to enterprise services without the need to host costly applications and infrastructure.

With the blistering pace of digital transformation, disruption won’t wait for organizations to adapt. In fact, the most important application you write from year to year might be software you have only weeks to write. The best option for success is a software delivery methodology that delivers value fast. Which is where our newly published Appian Delivery Methodology comes in.

Evaluating a new, unknown technology is a complicated task. Although you can articulate the goals you’re trying to achieve, you’re probably faced with multiple solutions that approach the problem in different ways and highlight varying features. To cut through the clutter, you need to figure out what questions to ask in order to evaluate which technology has the optimal capabilities to get the job done in your unique setting.

In the first part of the blog series, we discussed how correlation analysis can be leveraged to reduce time to detection (TTD) and time to remediation (TTR) by guiding mitigation efforts early. Further, correlation analysis helps to reduce alert fatigue by filtering out irrelevant anomalies and grouping multiple anomalies stemming from a single incident into one alert. In this part, we throw light on the applicability of correlation analysis in the realm of eCommerce, specifically, promotions.

A North American telecom company struggled for years trying to react quickly enough to new categories and new levels of spam texts and calls. They also did not have a good way to know when and why they would need additional capacity on their own, or any other telecom company’s networks.

Random forest is one of the most widely used machine learning algorithms in real production settings.

Automate the process of building and maintaining data pipelines to free up data engineers for more interesting, mission-critical projects.

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