In a new report, it is predicted that by 2017, 60% of big data projects will not pass the trial period and will be smashed into the cold. why?
In a new report, it is predicted that by 2017, 60% of big data projects will not pass the trial period and will be thrown into the cold. Why? The reason is not the lack of interest, effort or investment. Rather, it makes it difficult to create value from existing customer, operational, and service data, not to mention the large amount of unstructured internal and external data generated by social media, mobile devices, and online activities.
Companies are under increasing pressure to leverage big data and analytics tools because customers want more information from the organizations they deal with. Competition is intensifying, especially in mature industries such as financial services, retail, communications and media. The data-driven industry continues to shuffle. Spoilers in both the old and new industries have created data-driven business models and applied them to the production of customized products and services.
The continuous speculation on big data depends on three misunderstandings: First, big data technology will identify business opportunities by itself; second, the more data is mastered, the more value is automatically created; third, data scientists can help any company from big Profitable in the data, regardless of the company's organizational structure. And the dangers contained in these three misunderstandings will immediately reveal your secrets.
First, big data technology will identify business opportunities by itself.
Danger: Despite the large amount of money and time invested, the return on this investment is very limited.
A failed technical layout often begins with the assumption that this new tool will generate value on its own. Companies that successfully use big data energy often apply analysis to a small number of high-value business problems before investing heavily in big data technology. In the process, they learned how to implement solutions in an organized way, gained new insights into operational challenges, and gradually understood the limitations of their data and technology. Based on their understanding of their actual needs, they can determine the specific requirements of big data technology solutions.
Second, the more data you have, the more value you generate automatically.
Danger: Over-investing in unconfirmed data sources ignores valuable data sources that are close to the truth.
With the explosive growth of social media and mobile devices, the temptation to acquire and utilize new data is steadily increasing. Many large organizations have been drowned in the ocean of data, most of which is stored in silos and cannot be easily accessed and connected. We have found that the path to successful big data often begins with the full development of existing data for the institution. From an analytical point of view, it is often easier to process historical data than to process new data.
Third, a good data scientist will find value for you.
Danger: Existing organizations are not ready to realize the value of the data.
In order to continue to profit from big data, you need to create an operating model that continues to leverage big data and analytics. Based on the thinking of the data and analytics team, a successful data-driven business can coordinate its organization, processes, systems, and capabilities to make better business decisions.
in conclusion
The big data revolution has disrupted many industries, and companies that are committed to customer data analysis typically follow these three rules:
1. Before investing in big data technology solutions, prove that your organization can apply analytics to solve some high-value business problems.
2. Use existing data to create value before expanding to new data sources. Then use test-learning methods to inject forward-looking data into your historical data.
3. Encourage the business model, especially the front line of the business, to make it move quickly, and have confidence in the insights of the enterprise analysis team.
In the era of big data, companies that adhere to these rules will be more likely to succeed.
In a new report, it is predicted that by 2017, 60% of big data projects will not pass the trial period and will be thrown into the cold. Why? The reason is not the lack of interest, effort or investment. Rather, it makes it difficult to create value from existing customer, operational, and service data, not to mention the large amount of unstructured internal and external data generated by social media, mobile devices, and online activities.
Companies are under increasing pressure to leverage big data and analytics tools because customers want more information from the organizations they deal with. Competition is intensifying, especially in mature industries such as financial services, retail, communications and media. The data-driven industry continues to shuffle. Spoilers in both the old and new industries have created data-driven business models and applied them to the production of customized products and services.
The continuous speculation on big data depends on three misunderstandings: First, big data technology will identify business opportunities by itself; second, the more data is mastered, the more value is automatically created; third, data scientists can help any company from big Profitable in the data, regardless of the company's organizational structure. And the dangers contained in these three misunderstandings will immediately reveal your secrets.
First, big data technology will identify business opportunities by itself.
Danger: Despite the large amount of money and time invested, the return on this investment is very limited.
A failed technical layout often begins with the assumption that this new tool will generate value on its own. Companies that successfully use big data energy often apply analysis to a small number of high-value business problems before investing heavily in big data technology. In the process, they learned how to implement solutions in an organized way, gained new insights into operational challenges, and gradually understood the limitations of their data and technology. Based on their understanding of their actual needs, they can determine the specific requirements of big data technology solutions.
Second, the more data you have, the more value you generate automatically.
Danger: Over-investing in unconfirmed data sources ignores valuable data sources that are close to the truth.
With the explosive growth of social media and mobile devices, the temptation to acquire and utilize new data is steadily increasing. Many large organizations have been drowned in the ocean of data, most of which is stored in silos and cannot be easily accessed and connected. We have found that the path to successful big data often begins with the full development of existing data for the institution. From an analytical point of view, it is often easier to process historical data than to process new data.
Third, a good data scientist will find value for you.
Danger: Existing organizations are not ready to realize the value of the data.
In order to continue to profit from big data, you need to create an operating model that continues to leverage big data and analytics. Based on the thinking of the data and analytics team, a successful data-driven business can coordinate its organization, processes, systems, and capabilities to make better business decisions.
in conclusion
The big data revolution has disrupted many industries, and companies that are committed to customer data analysis typically follow these three rules:
1. Before investing in big data technology solutions, prove that your organization can apply analytics to solve some high-value business problems.
2. Use existing data to create value before expanding to new data sources. Then use test-learning methods to inject forward-looking data into your historical data.
3. Encourage the business model, especially the front line of the business, to make it move quickly, and have confidence in the insights of the enterprise analysis team.
In the era of big data, companies that adhere to these rules will be more likely to succeed.
Bread Baking Pan,Bread Pan,Loaf Pan,Bread And Loaf Pans
Xiongyang Household Co., Ltd , https://www.yjkitchen-manage.com