The credibility of large data

There's been a lot of discussion about big data, what is it, what can it do, what it can't do? Many people write articles praising how big data will change the way we do business, from predictive analysis to discovering information hidden in massive unstructured data. Others argue that large data should be treated with caution, and that data is difficult to manipulate, and that using large amounts of data does not lead to a change in media hype.

When business leaders consider the cost and application of large data, the question they need to ask themselves is, can they really trust big data? After all, many executives have seen the failure of "small data" before, and the first inclination is to question the authenticity of the data whenever an analyst suggests not to trust an intuition. Some companies are able to cope with big data, while others are extremely insecure. This is a matter of concern, as companies gain valuable insights from large data depending on whether they have the confidence to act on these insights.

One of the most notable problems is the distrust of data, which many executives have about the type of information that large data brings. Many companies worry that large data systems collect vast amounts of unclassified, untested data that does not have the same level of protection as traditional databases for data deployments, such as encryption.

We've seen it in the news that companies are taking action on the wrong data and then "deeply hurt". In April 2013, a news agency's Twitter feed was hacked, and the hacker sent false tweets claiming the White House was under attack. The news led several investors to start selling stocks, which eventually caused them to suffer huge property losses. Such stories give us a wake-up call that data, even large data, is not necessarily accurate or feasible.

However, this does not mean that we should ignore the value of large data. Large data provides tremendous potential to solve a variety of problems, and we cannot completely discard large data simply because of a few erroneous data. According to a Forrester consultancy report, 61% of IT executives think they need to use business intelligence more and more, which is a high-priority job. Most executives also want to see information about transactions, structured, and unstructured data as a whole. However, without technology such as Hadoop, they are less likely to exploit such information. Companies are also under pressure to analyze more data faster to make better decisions.

Should companies give up big data, or do they have a way to believe big data? The answer is yes, we have a way to believe in big data, but not all big data solutions are the same. Hadoop has made great strides in extreme large-scale data analysis, but many people think it is not suitable for enterprise applications. Large data distributors have started to create platforms to overcome the drawbacks of Hadoop, primarily by fixing security issues and making them easier for business executives to use. Some companies have even moved large data to the cloud database. These platforms also provide the necessary additional components for real-time analysis, transforming Hadoop from a cost-effective mass storage system into a real analytics platform.

More importantly, these platforms provide varying degrees of information management to increase the confidence of the enterprise in these data. These platforms can test the authenticity of data through data analysis, record-level access control, and logging capabilities, and in the long run, business executives should choose this type of platform to help them develop.

Figure out whether big data can help your business, make a plan for how to consolidate large data, and then look for a platform with the tools you need to build confidence in the value of large data.