Organizations that are in the market for analytics databases that can serve an enormous quantity of queries on massive sets of fast-changing data may want to check out the latest Gigaom Sonar report on real-time analytics databases. Real-time analytics databases are a relatively new product category that has emerged over the past few years to…
In descriptive analytics, data aggregation, and data mining techniques are used to collect and review the historical data of a business to gauge the past performance. The most common example of descriptive analytics is the reports that a user gets from Google Analytics tools. A web server’s summarized performance reports may help the user analyze…
When it comes to building great data products, all the key ingredients are available in the cloud–big data, massive compute, and sophisticated analytics and AI tools. What’s missing is an easy way to turn all those ingredients into finished products. That’s an area that a startup called DataOps.live hopes to fill in the Snowflake environment….
Navigate the choppy and exciting waters of AI with Forrester’s analysts. Forrester’s multi-episode webinar series on AI delves into the profound impact AI has – and will continue to have – on how data scientists and software engineers approach their work and how other job functions will have to adapt to new AI workflows, processes,…
The term “Data Quality” focuses primarily on the level of accuracy possessed by the data, but also includes other qualities such as accessibility and usefulness. Some data isn’t accurate at all, which, in turn, promotes bad decision-making. Some organizations promote fact-checking and Data Governance, and, as a consequence, make decisions that give them an advantage….
The work of data engineers is extremely technical. They are responsible for designing and maintaining the architecture of data systems, which incorporates concepts ranging from analytic infrastructures to data warehouses. A data engineer needs to have a solid understanding of commonly used scripting languages and is expected to support the steady evolution of improved Data Quality,…
Organizations are increasingly employing innovative technology called “data virtualization” (DV) to tackle high volumes of data from varied sources. Data virtualization is widely used in enterprise resource planning (ERP), customer relationship management (CRM), and sales force automation (SFA) systems to collect and aggregate multi-source data. From multi-sourced data acquisition to advanced analytics, this technology seems…
Organizations are constantly seeking ways to gain a competitive edge by transforming data into actionable insights through analytics. However, achieving analytical success goes beyond just collecting large volumes of data and building dashboards. The key to turning data into meaningful and actionable insights across the organization lies in a frequently overlooked area – establishing a…
As the amount of data continues to grow, so does the potential for advanced analytics to make sense of it all. In 2024, tremendous progress will be made in analytics, according to our merry band of prognosticators. It’s hard to avoid the siren call of generative AI. But as the calendar flips over in two…
Generative AI will not just disrupt — it will shatter markets, industries, and economies in waves over the next 10 to 15 years. We sense this, but why and how will this happen? We think that generative AI drives the cost of knowledge activation to zero and creates a virtual knowledge loop that increases what…
