The data industry has changed drastically over the last 10 years, with perhaps some of the biggest changes happening in the realm of data storage and processing. If there’s a need for data storage and processing of transactional data that serves an application, then an OLTP database is great. MORE

Data lineage management, once a time-consuming process of manual data tracking used only in times of crisis, has been transformed by automation into an essential tool for making informed business decisions. One of the biggest challenges facing data analytics teams is how to reconcile data that arrives from different sources which contains different metadata lineage information. The last decade has been an important one for enterprise data lineage. MORE

The optimized data warehouse isn’t simply a number of relational databases cobbled together, however—it’s built on modern data storage structures such as the Online Analytical Processing (or OLAP) cubes. Cubes are multi-dimensional datasets that are optimized for analytical processing applications such as AI or BI solutions. As these algorithms grow ‘smarter,’ businesses will be able to automate even broader swaths of processes. Analytics and BI MORE

The world of business analytics is evolving rapidly. Unfortunately, it also introduces a mountain of complexity into the reporting process. Let’s begin with an overview of how data analytics works for most business applications. MORE

CRM software has gone through a similar transformation, starting with sales force automation, and more recently evolving into a new breed of products that support digital marketing campaigns through email, social media, and online advertising. First, accounting moved into the digital age and made it possible for data to be processed and summarized more efficiently. Analytics and BI Webinars MORE

Many of the features frequently attributed to AI in business, such as automation, analytics, and data modeling aren’t actually features of AI at all. The optimized data warehouse isn’t simply a number of relational databases cobbled together, however—it’s built on modern data storage structures such as the Online Analytical Processing (or OLAP) cubes. Analytics and BIArtificial Intelligence is coming for the enterprise. MORE

Data drives everything in the business world, from manufacturing to supply chain logistics to retail sales to customer experience to post-sale marketing and beyond, data holds the secrets to making processes more efficient, production costs cheaper, profit margins higher and marketing campaigns more effective. Business intelligence (BI) software can help by combining online analytical processing (OLAP), location intelligence, enterprise reporting, and more. MORE

This is where Business Analytics (BA) and Business Intelligence (BI) come in: both provide methods and tools for handling and making sense of the data at your disposal. So…what is the difference between business intelligence and business analytics? What Does “Business Analytics” Mean? Business Analytics is One Part of Business Intelligence. ” In the past, the hard graft of BI had to be performed by IT analytics professionals, resulting in static reports. MORE

The optimized data warehouse isn’t simply a number of relational databases cobbled together, however—it’s built on modern data storage structures such as the Online Analytical Processing (or OLAP) cubes. Cubes are multi-dimensional datasets that are optimized for analytical processing applications such as AI or BI solutions. As these algorithms grow ‘smarter,’ businesses will be able to automate even broader swaths of processes. Analytics and BI MORE

Having seven years of experience with managing Redshift , a fleet of 335 clusters, combining for 2000+ nodes, we (your co-authors Neha, Senior Customer Solutions Engineer, and Chris, Analytics Manager, here at Periscope Data by Sisense) have had the benefit of hours of monitoring their performance and building a deep understanding of how best to manage a Redshift cluster. WLM can be thought of as “how many processes can be managed by a cluster at one time.” MORE

D365F&SCM customers are invariably processing enough data that they can run into substantial issues with reliability and performance when running reports using entities. Jet Analytics provides for a one-to-one match with source tables and a set of pre-defined data entities out of the box. MORE

This is how the Online Analytical Processing (OLAP) cube was born, which you might call one of the grooviest BI inventions developed in the 70s. Basically, data lineage is a process of researching the genealogy of the data, where it comes from and how it all fits into context. Saving time and headaches with online analytical processing tool. How much time has your BI team wasted on finding data and creating metadata management reports? MORE