7 steps to be followed for large data business success

In the IT field, we all love to hype and create popular language. Like fashion trends, we seem to have a 20-year cycle in which we revert to the previous technology and give it a new name, insisting that it is a technology that everyone must have immediately. The latest hype is: Big data.

From the http://www.aliyun.com/zixun/aggregation/13541.html ">interop convention to various cloud meetings, we were told that if we did not develop a big data strategy (and combined with cloud strategy), we would fall behind.

For large data, there are three important facts. First, it is not a new trend. Amazon, Microsoft and Google have been working on big data since the 90 's. In fact, many companies have been digging up data for decades. It may be that only large companies with large financial resources were able to carry out big data studies, but big data did exist already. Now, based on cheap computing and storage capabilities and new tools and technologies, almost everyone can use advanced data mining techniques and algorithms.

Many people think that big data is just the new name for Business Intelligence (BI), although the two have similarities, but big data goes beyond the scope of BI.

Second fact: "Big" is relative. Now, industry organizations do face record levels of data growth. According to IDC, we create more than TB data per second, and by 2020 we will have more than 35ZB of storage data. However, big data is not necessarily huge, the big data is not about its size, but what you need to do with it. Small companies with terabytes may also have big data problems because they need to extract, analyze, and make decisions.

Third, the data used in large-data processing is defined broadly, and it can contain both structured and unstructured data. For some companies, the most important thing is the metadata of large data, or data about data.

The author of McKinsey's definition of large data as "a dataset that is larger than the capture, storage, management, and analysis capabilities of traditional database software" adds: "These datasets require a large number of parallel software (systems) running on hundreds of or even thousands of of servers (clouds) to process." ”

Here are 7 steps to success with large data:

Step 1th: Acknowledge the problem. This is often the hardest step. 10 years ago, we refused to acknowledge that our network was no longer protected by firewalls and proxy servers, and that we had to access the infrastructure and embrace the Internet for our employees remotely. For large data, it leaders need to evaluate their data:

Does your DataSet make you overwhelmed?

You don't know where all the data is?

Do you (or business leaders) not get the information you need from your data?

Do business leaders not make decisions based on data?

Is it possible to increase the relevance of it in enterprise policy and strategic decision making?

If you're like most companies, the answer to some or all of these questions is yes, it's time to control your data and dig out information to provide leadership decisions.

(Responsible editor: The good of the Legacy)