Asking the right questions of your data and knowing what you are looking to find is a critical component for gaining insights from your data that drive specific actions. Data is not black and white; there is so much you can do with it. Accordingly, two people with the same data can come up with very different insights. This is because so much depends on the specific problem to be solved and the approach you take to solve it.

Companies move as fast as their slowest insight. In the era of big data, information is often produced faster than it can be consolidated and analyzed throughout the Enterprise. Analysts battle for the limited engineering resources, so they can construct data sets for their analytics endeavors to ultimately provide insights to fast-growing enterprises.

Turning organic visitors to your website into loyal customers is the goal of every company that has an eye on growth. B2B traffic that finds your website through Google search brings the best new customers since those visits are organically searching for what you write about. Converting these organic visitors of your website into customers who will return and order should be the focus of business marketing efforts.

The Top 5 Takeaways: Maybe your primary focus is online retail. Maybe you just have a small digital store selling merchandise relating to your main business concerns. Whatever the size of your e-commerce venture, integrating your systems and data is vital. You’re probably already dealing with a number of different platforms and SaaS providers, integrating with their APIs and their services to help run your business more smoothly.

How to improve ROI on social media ads as regulations and technology change.

By incorporating AI and machine learning into mobile testing tools, teams can become more efficient in test automation. In this article, we'll look at how the adoption of AI and machine learning will improve these tools and what the future of testing might look like.

There is an entire category of problems in distributed computing that require a single client amongst a set of peers in a network to coordinate the behavior of all the other clients. It is common in systems responsible for processing queues of work that connect to downstream systems that don’t support concurrent connections. Or in systems where work is parallelized in a specific way.

It’s not uncommon for organizations to have to deploy solutions across (or among) multiple security domains. Here, we use the term “security domain” to refer to a segregated network environment, like a restricted internal network or a DMZ. This post will explore some design considerations when deploying Kong Mesh (and Kuma, the CNCF-hosted open source project upon which Kong Mesh is built) in environments with multiple security domains.

Data warehouse reporting allows you to transform large amounts of data into valuable business insights from multiple sources. Here’s how.

Tell us if this analytics scenario sounds familiar: your organization employs an analyst team that uses old technology, desktop data visualization tools, or homegrown reporting systems to manually build out static dashboards and multiple weekly or monthly reports. As a result, the data analysts have to manually generate and update business reports regularly, and teams cannot keep up. In turn, the rest of the organization is unable to make timely business decisions.