How to translate large data into actual benefits?

Authorities in the IT industry like to advocate that data should be the strategic asset of an enterprise, but the value of the data is often seen as an inability to quantify, something like corporate goodwill, intellectual property, patents and business methods. So even if corporate executives understand the value of the company's data, they are not always the first to think of making money.

The data market may become a new market category. Here, businesses can buy, sell data or make mutually beneficial data trade.

There is an old maxim: anything you don't use is spending your money. This is true for your organization's data, especially if the enterprise collects and stores data information at a very expensive cost. That being the case, it's understandable that companies are trying to get more value from big data by using the relevant data from their systems.

In quantifying the value of data, as in the case of separating gold from pyrite, the extraction of critical information is critical. One approach is to determine the value of the relevant data information for the business. For example, the enterprise can quickly adapt to the changing market conditions by analyzing the data information of the enterprise's revenue in real time. At this point, the data information becomes a tangible, valuable asset.

As is known to all, social media companies are seeking to profit from the personal and professional interaction data they collect and capture. They are almost entirely dependent on advertising revenue, so their data information is valuable to advertisers, who can use the data for targeted branding or more effective customer segmentation and marketing. These social media advertising users can better through the network and mobile device platform to accurately locate their target customers to advertise, at the same time through a variety of tools for cross-selling, upward sales, advance notice of recent preferential information, further attract customers and reduce management losses.

Online businesses analyze customer transactions through sales data, online product search, or other similar shopping behavior information from customers, and then adjust their products and quotes according to the customer's purchasing model. The more data they collect from consumers, the more accurate they are at targeting customers.

At the same time, there are more innovative ways of monetization of data information. Considering the development of data information into a market, a model can potentially create a new market classification. Here, businesses can buy, sell data or make mutually beneficial data trade.

For example, a cloud service provider installs a large technical infrastructure to analyze the performance and reliability of installed hardware. At this point, the enterprise can provide the hardware vendor with the relevant information through the data market, which can relate to the warranty issue of the product and the direction of improvement of the future version of the hardware product. In return, hardware vendors can provide complementary product or component performance data to cloud service providers. Hardware vendors can also offer product improvement suggestions or custom service levels to cloud service providers for feedback.

Similarly, a snack maker may decide to share the nutritional information of its proprietary products through the data market in exchange for feedback from consumers in certain regions on their products. In the data market, there may be a smartphone app developer willing to sell or swap the snack maker's nutritional profile information, because the app developer's software PRODUCT is to compare the calories and fat content of various desserts and offer product advice to customers.

As you can see, increasing synergies is likely to generate considerable revenue. The cloud service provider above provides a more reliable service; and hardware and software vendors have access to relevant product usage information to improve their products and services; Snack makers also get field data from a variety of previously unavailable data sources, and application developers get the exact data they need to build their application software.

All this data sharing, related issues are easily solved. But privacy is a key issue, especially when companies are looking for market potential customers ' data. This is the time when the relevant service agreements and privacy policies play a key role. If companies inform their customers timely, let them know the relevant matters, or let the customer own the right choice, then the enterprise can greatly reduce the distress of privacy issues.

Companies should make these notices and agreements easy to understand, and when preparing to sell or trade the data, ensure that the relevant confidentiality rules are strictly adhered to. Otherwise, this error can lead to fatal trouble. Of course, companies also need to ensure that these valuable data information does not fall into the hands of competitors.

An enterprise's IT department can help remove shared data information to minimize competitive risk. Executives at the corporate CXO level must also understand the potential risks. In short, this is a new world, those who can boldly discover the value of data information can make money.

(Responsible editor: The good of the Legacy)