Big data modeling of the top ten business trends

Ron Bodkin, CEO and founder of Think Big, a big data consultancy, once said: "Despite the slow development of big data, it is firmly changing the way businesses do business.Each industry has different use cases and big data releases The ability to work with data has long been put under pressure and now there is finally a great deal of pent-up demand being freed up. "

Here are the top ten trends that are shaping big data and the future of your business.

1, machine data and Internet of things will occupy the center stage

While sentiment analysis and clickstream data analysis will continue to be important in the big data space, their data will become more important. From RFID tags and industrial instruments to jet engines and consumer electronics, the world is producing ever-larger volumes of data.

Businesses are starting to use this data to improve products, increase efficiency, find flaws, and enhance security.

2, various types of portfolio use of big data to create value

The new aggregation of public and private domain data is giving us a new opportunity to aggregate new insight into multiple big data sets far beyond the insights gained from a single large data set. "The big value of big data is that it's a collection of multiple big data sets," Bodkin said. For example, Land O'Lakes'WinField, a provider of seed and crop protection, has taken advantage of several large data sets, including weather data, soil moisture data, soil type data, seed data and other data to help growers harvest the highest yields.

3, built-in open source big data tools in the explosion of innovation

With an open source core. Businesses are developing a series of big data platform technologies, tools and components. "The open source core of big data continues to be a source of action," Bodkin said. "Big data is basically driven by the open-source paradigm, and the resulting organizational innovation is also driving the business forward."

There are a number of vendors that provide tools to ease the implementation of big data solutions, including GE and tools to help manufacturers harness their data; Microsoft, working closely with Hadoon publisher Hortonworks, is helping businesses pass Excel Analysis Big Data Set.

4. Use a forward-looking approach to identify where big data works

Many early big data projects are basically project team projects intended to prove the value of big data, but the situation is changing. "We saw the viral spread of success stories," Bodkin said. "But we think there is a better way to do that, instead of relying solely on the team's innovations, to adopt a more forward-looking approach to identifying where big data actually can work, and we think it is important to have A verified test case, of course, the manager's support allows you to get results faster. "

5, the actual production of large data projects more and more

In the past few years, most of the big data industry projects have been pilot projects, but actual production projects have been growing over time, Bodkin said. Most of these projects, he said, are data scalability and cost control, much like building a data lake, but some of the early innovators are now turning their attention to implementing businesses with new analytics capabilities Transformation. "They spend less time collecting data and more and more time actually analyzing the data and answering questions," said Bodkin.

6, large companies began to accelerate the use of big data

Big companies are starting to adopt big data, which is a major trend in 2012. In a Global Enterprise Study conducted by Tata Consulting Services (TCS) this year, 53% of 1217 large companies began adopting big data innovations. But also have great confidence in their own innovation, about 43% of large enterprises expect big data return on investment will exceed 25%.

7, most companies spend less on big data, a small number of large business spending

Most businesses do not invest much in their big data innovations, but some invest heavily. The Tata Advisory Services survey found that large enterprises using big data innovations have a median input of $ 10 million. In 2012, 25% of large enterprises generally invested less than 2.5 million U.S. dollars.

On the other hand, however, 15% of the big companies surveyed by TCS are spending more than $ 100 million in 2012 on big data, and 7% spend more than $ 500 million. TCS also found that businesses in telecommunications, tourism, high technology and banking spend the most, while businesses in the life sciences, retail and energy sectors spend the least.

8, big data investment for revenue and sustainable income

According to the Tata Consulting Services survey, it is hardly surprising that businesses gain the most from their business functions that generate revenue and sustain revenue as they adopt big data innovations. In fact, 55% of spending goes to four business functions: sales (15.2%), marketing (15%), customer service (13.3%) and research / new product development (11.3%). Business functions that are not directly revenue-generating receive less investment: IT (11.1%), finance (7.7%) and human resources (5%).

9, the maximum return on big data from logistics and finance

Although the largest piece of investment in revenue-generating features such as sales and marketing (which together add up to 30.2% of the big data budget), TCS found that logistics and finance (only 14.4% of big data investments) Expected to get a higher return on investment.

In fact, of the eight business functions that TCS may benefit from big data innovations, TCS requires companies to rank the importance of 75 of these activities. As a result, many of the large global companies surveyed have put together many logistics activities and sales activities Listed in the top 25.

10, the biggest challenge comes from the corporate culture and technology

While many businesses are still responding to the technical challenges of big data, others have told TCS that the biggest obstacle to big data innovation is getting BUs out of the door and sharing information with other departments. Of course, the technical challenges in many aspects of capacity, speed and type are also in the lead when it comes to processing data. Data analytics tops the list. At the same time, companies need to work hard to figure out what data can be used to make better decisions.