Transforming Information Into Knowledge
Transforming Information Into Knowledge
While AI/ML gets a lot of attention, it is not the most common use case for data streaming. Real-time analytics is used by 71% of data streamers in the Redpanda survey, followed by 64% supporting e-commerce transactions with streaming data. Internet of Things (IoT), fraud detection and personalization are also commonly supported. A still impressive 47% of survey participants have a situation where AI/ML uses streaming data.
More than three in four participants in the new Data on Kubernetes survey now acknowledge the use of databases on Kubernetes, up from 50% in 2021.
Data and analytics leaders may be overestimating their companies’ ability to benefit from the next round of investments in data infrastructure technology.
Once a use case is actually defined, it takes 66% of organizations more than a month to develop an ML model. For 64% of organizations, it takes at least another month to deploy that model.
Salesforce is buying Tableau and Google Cloud is adding Looker to its family. After reviewing a panoply of studies about business intelligence (BI), analytics, and data warehousing, we found it evident that both acquisitions will provide best-in-class functionality addressing the needs of both business analysts and the IT department, as well as leadership in high-growth markets that are only tangentially related to artificial intelligence and machine learning.
42% of respondents exclusively rely on third-party cloud providers’ hardware built to build AI/ML models, but only 12% will do so three years from now. Instead, a majority will use a combination of both on-premises and public cloud. Many companies may have gone first to cloud providers because they wanted to quickly launch AI/ML activities. These same companies may migrate to on-premises environments for specialized workloads to reduce costs as they scale-up into production or use proprietary data.
A majority of developers involved with machine learning do not use models to generate predictions.
Most companies are using data in real-time for at least some application or department.
This is the second of several blog posts that look at the use of analytics consultants and when consulting firms should use analytics consultants. The first article can be found here. Management consultancies like McKinsey have been rushing to get in on the analytics boom […]
This is the first of several blog posts that look at the use of analytics consultants and when consulting firms should use analytics consultants. Throughout the corporate world analytics is a necessary capability for a data-driven enterprise. But what types of analytics should be done […]
Bill Franks knew what he was talking about at last December’s Data Skeptic’s Meetup. I recommend him as a speaker. In his job at Teradata, I can easily see him successfully introducing robust new analytic projects to Teradata’s legacy data warehouse clients. While Franks had to cover […]
I was fascinated with the chart that shows how often self-identified data scientists with different skill sets work with different scales of data. It is notable that most data scientists weren’t using big data (see the chart below). I assume that in 2015 a much larger percentage of data scientists are working on a terabyte or petabyte scale.