The eight big data myths that CIOs should eliminate most

Gartner analyst Mark Beyer believes that if corporate CIOs want to achieve big data normalization by 2020, they need to start by eliminating eight myths about big data.
　　
Get 10 CIOs to define big data, and you'll get 10 different answers. Gartner analyst Mark Beyer says this is because big data is still not standard for enterprise IT professionals.
　　
Beyer at Gartner's Symposium/itxpo conference this year. "When things became commonplace, it began to normalize, and our job as an IT professional was to normalize big data 2020 years ago." ”
　　
CIOs can help them by distinguishing facts from big data lies.BIEnterprises step by step towards normal. "Myths help relieve anxiety, but not the actual situation," he said.
　　
Here are eight big data myths presented by Beyer:
　　
　　 1. Big Data starts at + TB. Don't look for big data standard sizes, because they don't have a standard size. "Big data is the processing of data, not the size of the data," Beyer said.
　　
　 2. If you want big data, you must replace the infrastructure. "If I decide to change the entire business intelligence system infrastructure because of new needs, I'm betting on everything I've had before," Beyer said. What are his lessons? "You have to figure out whether the risk of maturity (infrastructure) is worth it." ”
　　
　　 3.80% of the data is unstructured. This is probably the most frequently quoted big data statistic, but according to Beyer, it is inaccurate. "The world's largest information asset is machine data. Because they are not interrelated, it is absolutely a lie that they are unstructured. Machine data is structured data. "By the way, these large amounts of machine data are often repetitive information that confirms everything's normal. "This is what the machine data is usually expressed for," he said.
　　
　　 4. Tools will replace data scientists. Rest assured, all the flowers are attracting, wooing, and getting data on the scientist's money will not be white, Beyer said. "Tools are a kind of engineering, and engineering is a reuse of the facts that have been discovered. And science is to discover new facts. "Tools do not replace data scientists-at least not until the tool can replicate and evolve itself.
　　
　 5. More data can solve the problem of data quality. "The lower the quality of the data, the lower the quality of the answer," Beyer said. CIOs should focus on data quality. For example, some people equate mobile phones with real individuals by using the temperament geo-location data collected by mobile phones, he said. However, the phone can be accidentally left in the office, or the GPS function can be turned off at any point in time. "The phone is not human," Beyer said.
　　
　　 6. Real-time is just faster. Real-time operation does not mean speeding up current data ingestion and analysis processes, Beyer said. Instead, "Make sure that the shorter the interval between data collection and decision-making, the better," he said. In addition, most enterprise data is not required to operate in real time.
　　
　　 7. Data volume is better than professional knowledge. Those who think that they can simply no longer take care of business processes, think again. This is because "a good data scientist must be called off at some point," Beyer said. Without business processes, data scientists will continue to continue without providing business value. Someone needs help to draw the line.
　　
　　 8. The data model is not used. This assertion is absolute. However, Beyer clarified that everything in any digital asset has its own digital model. "We don't abandon models because of big data," he said.

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The eight big data myths that CIOs should eliminate most