Three great myths about big Data

Much of the discussion about Big data now revolves around http://www.aliyun.com/zixun/aggregation/12240.html "> data collection, but unless internal and external users can easily consume the data, they will be worthless." What are the problems of dealing with big data?

Michel Guillet, a 2.4bn Analytics company that provides data visualization, believes that some companies tend to fall into these three myths when dealing with big data:

Myth 1: Internal data users need flexibility rather than guidance

Did you have too many things to choose from when you went to the grocery store?

Big data is the same. In fact, while your supervisor may have expressed interest in more data (more metrics, more naked data access, more charts, etc.), it is an uncertain expression rather than an interest in stronger analysis. These people are not sure exactly what they want to do with the data, so they think it might be better to have all the data coming.

Users want to be guided to the answer, and they want to show the data to eliminate uncertainty-not to create more problems, even if they don't want to answer a few more minutes on the data.

In the report design to spend more time, to the user point out a clear path of exploration, this is the right way.

Myth 2: Our customers do not require

Although customers may not directly require data products, but the indirect expression will have. In their words to you, your sales or support team, you can feel:

• How is it compared to the industry average?

• How can I access my data more frequently?

• Do other people in my organization have access?

I have to make a monthly report for my boss.

Customers rarely ask for large data reports at the start of a project, but at the end of the year they always ask for it. If you keep this in mind at design time, you can better handle what the system needs to collect and understand why.

Myth 3: Customer Data I can't collect money.

What you sell is not data, but the analysis, metrics, algorithms, and presentations that are incorporated into the analytics, which enhance the value of the data. Instead of positioning data products as "easy access to raw data," You can solve the problem.

Can you easily compare customer data with other customer base? Is there a third-party source for benchmarking comparisons?

Although customers do have their own data, you can provide value-added services through specific industry metrics, customer benchmarks, and recommendations.

Large data can be a competitive advantage for your company, as long as it avoids hype and creates a set of goals and quantifiable data collection and use plans.