Data Governance applied to analytics, business intelligence (BI), or data modeling is nothing new, but Analytics Governance is somewhat different from Data Governance, says Malcolm Chisholm, president of Data Millenium. Chisholm spoke at DATAVERSITY’s Enterprise Analytics Online, stating that Analytics Governance is focused within a more centralized unit rather than the distributed model Data Governance requires. “There […]
Digital Transformation Examples for Business Success
Today, most businesses are embracing digital transformation to meet ever-increasing customer expectations and to remain competitive and relevant in the world economy. While many organizations still struggle to adopt new technologies – often due to the lack of a tailored digital strategy and the reluctance of many to change the way of business operations that […]
A Guide to Time Series Databases
Time series databases (or TSDBs) are databases that have been optimized for processing time series data. Time series data is made up of data records that are indexed using timestamps. The timestamps provide a reference for each of the data records and show how they relate to one another in time. An example of time […]
Data Privacy and Security Concerns for Multi-Cloud Organizations
The world is going multi-cloud. Enterprises are leveraging the benefits of multi-cloud services to improve operational efficiency, reduce costs, and drive faster innovation. What does this mean for data privacy? With data residing in multiple locations, it’s more important than ever for organizations to understand their data privacy risks and ensure that any sensitive data is protected. In […]
Data Science Solutions: Applications and Use Cases
Data Science is a broad field with many potential applications. It’s not just about analyzing data and modeling algorithms, but it also reinvents the way businesses operate and how different departments interact. Data scientists solve complex problems every day, leveraging a variety of Data Science solutions to tackle issues like processing unstructured data, finding patterns […]
Data Governance Frameworks
A Data Governance framework can be described as a collection of processes, rules, and responsibilities used to structure an organization’s Data Governance program. A solid framework will cover data standards, data privacy, business strategies, and the responsibilities of key individuals. Well-designed Data Governance frameworks support effective Data Governance programs and should standardize rules and processes across the […]
Do You Really Need a Data Catalog?
A data catalog can be an excellent resource for a business. An important element of modern metadata management, it can make many workflows and business processes clearer, encourage better cross-departmental collaboration, and boost productivity in several key ways. Gretchen Burnham and Becky Lyons of First San Francisco Partners recently sat down with DATAVERSITY® to talk about some of the big questions in […]
Enterprise Blockchain Implementation: Use Cases and Challenges
Blockchain provides a functional and efficient tool for recording business transactions, contractual agreements, and private records. A blockchain can be described as an encrypted digital recording of transactions that is duplicated and distributed to computers that are part of the blockchain network. This form of communicating and sharing recorded data is extremely difficult to hack […]
7 Key Features of Data Management Systems
Data Management is the practice of gathering, organizing, securing, and storing data for an organization so it can be analyzed for business decisions. Data Management helps minimize potential errors by setting processes and policies around its usage and building confidence in the data used for decision-making throughout the company. Businesses implement Data Management systems to ensure that data […]
How to Become a Data Quality Analyst
Data quality refers to the planning and implementation of quality management measures for the data that companies generate. The idea is that the data should fit the end goal of the data consumer’s needs – and must follow specific quality dimensions to be deemed fit for use. The role of a data quality analyst (DQA) is to ensure […]