Challenges in the big Data Age: corporate socialization and data competition

Over the past 50 years, the New York Times has produced 3 billion of words, and now Twitter generates 8 billion words each day; human beings produce about 15 data per day, which is 8 times times the books of academic libraries in the United States. Everything has changed since the advent of social media in the 2004. Today, 80% of the data is unstructured, and the data is data of personal behavior.

November 4, Kingdee hosted the "2012 China Management • Global forum", Kitsolutions software company Asia-Pacific affairs director, "Big Data" author Shiji in "Corporate Social and management Innovation" special, published "Analytics: Connect ERP, social media and large data" Keynote speech. Shiji pointed out: This is a large data era, "big" not only in capacity, but also through data integration and analysis, discover new knowledge, create great value.

What are the challenges and changes that big data will bring to the business?

First, business data began to diversify. ERP data and information system data, each have specific meaning and value, and social media data, can be called fuzzy data. Now the academic research believes that social ERP is a positive and promoting function of enterprise management. Because the social ERP puts the informal communication channel into the enterprise management's view. The informal communication channel has always been inside the enterprise, and it has played a great role in the enterprise culture and management. The human-centered communication complements the traditional ERP business process-centric management. ERP is the process, the leader may be the last to know the truth, and the emergence of social media within the enterprise will greatly improve this situation. A lot of things leaders just need to know that there is no need to make a decision on this matter. Therefore, the most important role of the social ERP is to introduce the unstructured data into ERP system and adapt to the trend and requirement of the large data age.

Second, the biggest challenge that companies need to face is data competition. Data has become the basic element and asset in the current production process. For example, weather data, if you are a sales company, you can integrate the weather data with your sales data, you will find the rules. Data is a very special kind of capital, first of all it is not exclusive, no consumption, but there is integration, through 1+1 can be greater than 2. So data competition will become an important form of business and national competition. Human beings have entered the data age from the software age, and some companies have built every aspect of their business activities on data collection, analysis, and action capabilities, which will be the cheapest company.

How does the data compete?

Data ROI is the data value divided by the cost of data, first, we need to reduce the cost of data, improve the value of data. There are many ways to reduce data costs, the most important of which is to transfer low activity data to low cost memory. How to increase the value of the data? Collect more and more comprehensive data, such as social ERP. Second, there is a data governance team and process for data quality. Third, to have a good ability of data analysis, "Data visualization" is the current trend, through graphics, images, animation and so on to show the data, so that the relationship between the shallow layer of data to get a better understanding and discovery.

From 2006 onwards, the industry's mainstream manufacturers began to call their BI products analytics. What's the difference between it and business intelligence? Business Intelligence has a stronger it orientation, and analytics can think of it as a 2.0 version of business Intelligence. Now, analytics began to change, in the past, 90% of the data used are ERP data, only 10% of enterprises will use external data, but in the future of enterprises, only 50% of their own data, external data and new media data will be more and more important.

There are two cases to illustrate this point. There are more than 100 attractions in Disneyland, which means more than 100 queues. How to reduce the waiting time for customers? Disney used more than 10 years of historical data, combined with weather data, travel data to predict each team each day, every hour of the waiting time required for the visitors to calculate the best scenic spots in the park order. At the same time, real-time collection of Twitter data, deal with unexpected situations, update the queue waiting time for each team, the use of these data visitors on average save 4 hours per person.

The second example is the Pennsylvania State government, which analyses the state of the statewide cold drug sales and the historical data of the system preservation to determine the likely pandemic. Analyze the child's rate of incidence and compare historical data to determine the likelihood of a large area of influenza. Text analysis of Twitter, real-time monitoring of influenza outbreaks, transmission, distribution in various regions. We can see that accurate data, once combined with social media data, will predict the future. That's why Analytics's global investment is just below the cloud, up to $ more than 34 billion trillion.

Four take Moz share

1, enterprises should actively introduce social media data and enterprise ERP data integration.

2, we enter the data age by the software age, analytics will become link ERP, social media and large data bridge.

3, analytics does not equate to the traditional business intelligence, is called "Business Lntelligence2.0".

4, analytics products still dominate the application market, coupled with the popularity of the cloud, will show great charm.