It would be any data manager’s nightmare to run meetings that create pedantic and irrelevant business glossaries or data dictionaries, which eventually gather cyber dust. However, skipping over building and maintaining a good business glossary or data dictionary risks convoluted meanings, confusing communications, and business failures. What should a company do? Robert S. Seiner (Bob), […]
A Brief History of Data Lakes
Data Lakes are consolidated, centralized storage areas for raw, unstructured, semi-structured, and structured data, taken from multiple sources and lacking a predefined schema. Data Lakes have been created to save data that “may have value.” The value of data and the insights that can be gained from it are unknowns and can vary with the […]
Working with Complex Data Models
Complex data models have now become the norm. A single stream of the data can travel through many hubs, and many different technologies. It may travel through the front end, the APIs, the Kafka pub/sub systems, Lambda functions, ETLs, data lakes, data warehouses, and more. Riding within this stream of data is the schema, and […]
Building a Productive Organization with Artificial Intelligence
There’s plenty of research on employee productivity, which is key to building a profitable and successful business. Engaged employees are likely to put their heart into their jobs – not just go through the motions. Here are some findings that put this into perspective: Happy workers are 13 percent more productive, according to research by […]
Data-Centric Architecture: Find Value with a Data Platform Approach
Applications provide a way to capture raw data in forms and store it in databases, and automated processes make it possible to extract meaning from that data using application programming interfaces (APIs). The current process-centric mindset assumes that the value resides in the automated processing, yet the limitations and costs inherent in its reliance on […]
Graph Databases vs. Key-Value Databases
Graph databases and key-value databases have very different features and are used for accomplishing different tasks. Key-value databases are streamlined and fast, but are limited and not as flexible. Graph databases, on the other hand, are very flexible and great for research, but not terribly fast. Both typically use a non-relational foundation. The two key […]
Scaling the Analytics Team: Developing Key Roles
In an enterprise analytics team, different roles exist to fill different needs, and those needs must be met in order to be successful. Launching an analytics program doesn’t necessarily require a massive influx of personnel before producing usable insights from data, yet it’s important that critical roles are filled, whatever the size of the team. […]
Case Study: Tracking and Tracing Drugs in the Pharmaceutical Supply Chain
Failures or lack of visibility in the many-tiered pharmaceutical supply chain have multiple repercussions. Drug shortages have adverse economic and clinical effects on patients — they are more likely to have increased out-of-pocket costs, rates of drug errors, and, yes, mortality. Hospitals and health systems allocate over 8.6 million hours of additional labor hours to […]
Business Intelligence Meets Metadata Challenges
Many BI managers, CEOs, and CIOs cannot afford to add more staff, so they are seeking technologies that can help their existing teams operate more accurately and efficiently. They “need to change the physics, as we call it. They can’t just add more people to the team,” said Amnon Drori in a recent DATAVERSITY® interview. […]
Optimizing the Data Warehouse
The data warehouse, a relational database technology, makes all enterprise information actionable, and will continue to be prominent as a Data Architecture component. In the 2000s, a typical business would consolidate data from multiple relational databases, centralizing all this information through a data warehouse, and consequently streamlining business tasks. However, the business context has shifted […]