- This week we begin with an article that describes the change in the data culture in past few decades & three keys to build a successful data culture.
- Next, we have a piece describing that advanced data collection methods like web scraping has enhanced the volume of data and data acquisition has been enhanced with the presence of increased accessibility of data as a service (DaaS) businesses and solutions.
- Following that we have a story on how we could store digital data inside strands of DNA that has exploded over the past decade and offers an incredibly compact, durable and long-lasting form of storage.
- After that, we have a note explaining the significant growth opportunities provided through cloud computing in fintechs that allows the industry to establish seamless client contact while increasing business revenue.
- Next is a guide to understand different sets of data modeling that helps in understanding systems, structures, formats and handling functions of the data and how it aids in comprehension of the complexity of a data system.
- Finally, we have a write-up explaining that a business should carefully consider its need for cloud databases and analytics solutions to balance complexity, cost, flexibility, and performance to address its existing demands.
Three Keys To A Successful Data Culture
There was a time when the ability to use a computer was limited to a select few, and when most organizations had just a handful of computers in each office. Fast forward a couple of decades, and digital literacy is now a prerequisite for just about every job. As we move slowly but inexorably towards a future where data literacy is required know-how, how can organizations build a strong data culture that will stand the test of time?
Entering A New Age Of Data Volume
Data collection has been the Achilles heel of all research endeavors. Proper data can be hard to find, acquiring it is often prohibitively expensive and cleaning everything up is time-consuming. Combine that with the fact that novel research usually requires data that’s not readily available in existing databases, and the issues start compounding exponentially.
How To Store Data For 1,000 Years
“You know you’re a nerd when you store DNA in your fridge.” At her home in Paris, Dina Zielinski, a senior scientist in human genomics at the French National Institute of Health and Medical Research, holds up a tiny vial to her laptop camera for me to see on our video call. It’s hard to make out, but she tells me that I should be able to see a mostly clear, light film on the bottom of the vial – this is the DNA.
How Fintech Startups Are Leveraging Cloud Computing To Scale Securely
Cloud computing in fintech has become an emerging trend that has had a significant impact on the needs of the financial sector and has given it a tremendous opportunity to grow. The global market size of the fintech sector is expected to reach $124.3 Bn by the end of 2025, with a compound annual growth rate of 23.84%.
The Different Data Model Types And Their Uses
At its heart, data modeling is about understanding how data flows through a system. Just as a map can help us understand a city’s layout, data modeling can help us understand the complexities of a data system, its structures, formats and handling functions. By diagramming the flow of data, we can identify bottlenecks and inefficiencies. We can also spot opportunities for improvement.
Four Factors Businesses Should Consider Before Choosing A Cloud Database
Businesses should carefully assess whether cloud databases and analytics solutions effectively address their current needs while balancing complexity, cost, flexibility, and performance. The database a company chooses has a substantial impact on productivity, costs, and business value if they intend to migrate its analytical workload to a single or multi-cloud vendor, on-premises, or a hybrid architecture.