In the big data era: How valuable is data?

With the deep application of big data in various fields, the value of big data itself is also highlighted. Researchers and commercial users analyze big data to gain insight into the real needs of customers.

Data is valuable and companies cannot do without data. But how valuable is data? What is the cost of analyzing big data and obtaining value from it?

In the past, technical experts provided historical data to senior management so that they could determine market trends. Although statistics are helpful for understanding market trends at a higher level and how organizations make the market, they are not enough to determine what new products or services need to be developed. These statistics do not tell you what the customer really wants.

Analysts, researchers, and business users analyze big data to make faster and better decisions. By using advanced analysis technologies such as text analysis, machine learning, predictive analysis, data mining, and statistics, enterprises can analyze previously undeveloped data.

Companies generate a large amount of data and have the ability to collect information from other sources, including mobile applications, sensors, websites, clickstream data, and social media activities. The data can be converted into products.

It is not easy to collect and analyze large amounts of data, especially unstructured data. Currently, enterprise systems cannot process TB of data per week, so there is no way to mine the gold block that can help the company develop new products and services required by customers. This leads the company to seek high-performance computing resources that can solve the problem, such as weather and climate forecasts, parametric modeling and random modeling, to process large-scale commercial data.

Big Data analysis uses analysis technology to analyze very large and diverse datasets. These data types include structured/unstructured data, stream data, or batch data, the scale is also quite different, ranging from TB to Pb and ZB. It checks different data types to find hidden patterns, Unknown associations, and other useful information.

D1net comments:

It can be seen that the above information can provide competitive advantages for competitors, and the result is commercial interests, such as more effective marketing and increased revenue. High-performance computing data analysis (hpda) is a term used to describe the transformation of the data-intensive HPC market and the high-end commercial data analysis market. These are the embodiment of data value.