Data Lakes vs. Data Warehouses vs. Data Marts

Let’s precisely define the different kinds of data repositories to understand which ones meet your business needs. October 29, 2020 A data repository serves as a centralized location to combine data from a variety of sources and provides users with a platform to perform analytical tasks. There are several kinds of data repositories, each with distinct characteristics and intended use cases. Let’s discuss the peculiarities and uses of data warehouses, data marts and data lakes.


What is data quality, why does it matter, and how can you improve it?

We’ve all heard the war stories born out of wrong data: These stories don’t just make you and your company look like fools, they also cause great economic damages. And the more your enterprise relies on data, the greater the potential for harm. Here, we take a look at what data quality is and how the entire data quality management process can be improved.


Benchmarking and sizing your Elasticsearch cluster for logs and metrics

With Elasticsearch, it's easy to hit the ground running. When I built my first Elasticsearch cluster, it was ready for indexing and search within a matter of minutes. And while I was pleasantly surprised at how quickly I was able to deploy it, my mind was already racing towards next steps. But then I remembered I needed to slow down (we all need that reminder sometimes!) and answer a few questions before I got ahead of myself.


CDP Data Visualization: Self-Service Data Visualization For The Full Data Lifecycle

With the massive explosion of data across the enterprise — both structured and unstructured from existing sources and new innovations such as streaming and IoT — businesses have needed to find creative ways of managing their increasingly complex data lifecycle to speed time to insight.


Let's start a revolution: Analytics in Action

At ServiceNow, we define analytics as using data to make better, faster decisions to run the company. We use analytics to not only spotlight every corner of our operations, but we also to spark growth, by giving our employees data-driven decision-making capabilities. That means they can take action every single day by using data and digital workflows. In order to drive data-driven decisions, we created a user-centric analytics program based on five major elements, which are listed below. 1.


Listening to the Customer in the 21st Century: It's All About Data

The customer has never been more right. Across industries, customers have become conditioned to demand not only near-instant responses to their needs but that their needs be anticipated in advance. Financial institutions are not given a pass, despite a competitive landscape flooded with regulation and privacy considerations. The customer still has expectations for a personalized, timely, and relevant experience.