CIO
SEPTEMBER 19, 2023
According to a report from OpsRamp , enterprises are using AIOps platforms for intelligent alerting (70%), root cause analysis (57%), anomaly and threat detection (52%), incident auto-remediation (50%), and capacity optimization (27%). Artificial Intelligence, Chatbots, IT Strategy, Predictive Analytics
CIO
JUNE 12, 2023
Many handle the word a bit carelessly,” says Charlotte Svensson, CIO at SAS, the Scandinavian airline. It can be about anything from classic data analysis and advanced data analysis, to robotics or machine learning. But it’s important to be vigilant and painstakingly sort through all products that have an AI label.
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Datapine Blog
APRIL 16, 2019
Qualitative analysis focuses on data that isn’t defined by numbers or metrics such as interviews, videos, and anecdotes. Qualitative data analysis is based on observation rather than measurement. Here, it’s crucial to code the data to ensure that items are grouped together methodically as well as intelligently.
mention
MARCH 8, 2024
How Reputation Scores are Calculated Reputation scores are calculated using sophisticated algorithms and data analysis. Automated systems crawl through various online platforms, collecting data related to reviews, social media interactions, and mentions. Swift and effective resolutions can mitigate negative impacts.
Netbasequid
NOVEMBER 23, 2021
Data analytics is growing in importance for brands as they discover how much consumer and market intelligence is waiting to be discovered. Thematic analysis is the catalyst for that growth. What is Thematic Analysis? Thematic analysis is a qualitative data analysis method that looks for patterns or themes within a dataset.
Netbasequid
APRIL 25, 2022
Some of the most common approaches include surveys and focus groups, but it should all feed into a larger data analysis to ensure real-time, accurate results. Crosstab analysis comparing airlines.
Datapine Blog
NOVEMBER 27, 2019
Today, most companies understand the impact of data quality on analysis and further decision-making processes and hence choose to implement a data quality management (DQM) policy, department, or techniques. According to Gartner, poor data quality is estimated to cost organizations an average of $15 million per year in losses.
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