Analysis Factory

"Companies in the financial services and insurance industries use big data to detect fraudulent transactions and insurance fraud by finding anomalies. Banks and credit card processors can also use big data to spot fraudulent payments, sometimes even before the cardholder knows that their card has been compromised. Big data analysis can also reduce the incidence of false positives in fraud detection, whereas previously, the financial institution would have frozen the merchant’s account and it might have turned out to be a false alarm."

10 Ways Big Data Is Changing Business What is big data, and how can you use it to increase your business's success?

"Citizen analysts need to be trained to both interpret data and infer from its characteristics and lineage how useful it will be. Essentially, a citizen analyst needs to develop risk assessment skills on top of understanding the data itself. Citizen analysts who understand the risk profile of their data and the sensitivity of the business to risk and data variations are well poised to leverage that data appropriately."

Data Science Reskilling Challenges: Four Key Considerations This is part of Solutions Review’s Premium Content Series, a collection of contributed columns written by industry experts in maturing software categories. In this submission, Talend Senior Directo…

"If you want to make your self-service business intelligence work, you have to enable:

-Technology environment that automates the whole data analytics cycle as much as possible and is suitable for non-technical users.

-Data maturity, which implies that all BI users are well aware of the importance of timely access to corporate information, high quality of information, security of all available data assets, collaboration with colleagues, etc.

-Established processes and practices to monitor and supervise self-service BI workflows."

Self-Service BI: Overcoming Adoption Barriers - RTInsights Self-service BI adoption rates remain low due to reluctance of users to embrace it, hidden overhead, neglected data governance, and more.