Are they basically just an evolved version of service-oriented architectures (SOA)? Since a microservice must be exposed via an application programming interface (API) for an organization to scale it to new developers, does that mean managing microservices is basically the same as managing APIs? As this article will discuss, the answer to both of these questions is a clear “no” — and companies looking to get the most from their microservices need to understand why.
There is a common misconception about data-informed decision making. It goes that once we implement the right tools, and figure out how to analyze the data correctly, the data will automatically turn into insights and will translate to better business decisions. It sounds great in theory.
All marketers have to deal with essentially the same problem when managing their online campaigns and looking for the right attribution model: the data they need for evaluation is scattered across various systems, and collecting and collating it takes too much time. If they decide to use solutions intended to help them to handle this problem, they often find such solutions are not, in themselves, enough.
When I speak to people who are thinking about implementing BI, they are often overwhelmed by all the things they could measure. Many start by wanting to measure everything, which doesn’t necessarily help them. That’s because there’s an inherent cost in measuring things – everything you report and track creates an ongoing burden that your organization has to maintain. That’s why it’s important to be selective about what you measure from the get-go.
Predict and measure the ROI of your next website redesign by following this 11-step playbook, compiled from dozens of experts.
Makeover Monday is a global initiative started by Tableau to encourage people to build visualizations. It involves releasing a new dataset each week and letting people reimagine the way it is visualized. It’s a really interesting experience and anyone can participate.
If you could have analytics everywhere, there would be an abundance of insights, which could lead to a boom in decision making on nearly every level, regardless of the situation or user. This is the notion behind the importance of embedded analytics.
The third edition of Yellowfin Bytes brings you an exciting inclusion made to the Data Transformation module. Users have been exploring Yellowfin’s very own lite version of an ETL tool in the form of this module, ever since its introduction in Yellowfin 7.4 last year. We have since been enhancing this functionality by adding new steps, calculations, configurations, and more.
Databox polled dozens of marketers and salespeople to find out which one does a better job at generating leads, qualifying prospects, and ultimately closing sales.
Today, we want to share a number of updates that will make data analytics easier and more accessible to all businesses. Our goal is to help you focus on data analysis instead of infrastructure management, give you the freedom to orchestrate workloads across clouds, use machine-learning in a way that's integrated with your data analytics operations, and take advantage of open source data processing innovation.
